esa-sceva/satcom-chunk-collection huggingface dataset:
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 2 Materials and Methods  
### Elliptical Paraboloid Array  
The elliptical paraboloid is a common type of quadric surface, which has wide application prospects in the field of precision measurement. As shown in Figure 1a, the three-dimensional model of the elliptical paraboloid with a superior optical reflection performance was machined by an ultra-precision single-point diamond lathe. The processing quality of the elliptical paraboloid was detected by an optical 3D surface profiler, and the results are shown in Figure 1b. In Figure 1b, different colors in the image represent different depths. According to Figure 1b, it can be seen that the machining height (80.679 \(\upmu\)m) and contour of the elliptical paraboloid are consistent with the theoretical design.  
[PERSON] et al. have described in detail the principle of 2D micro-displacement based on a single elliptical paraboloid [24]. However, the range of this measurement system is limited by the surface area of the elliptical paraboloid. A larger elliptical paraboloid will cause inconvenience in manufacturing and application. In this paper, a linear elliptical paraboloid array is proposed, in which the distance of the centers between adjacent elliptical paraboloids is only 50 mm in the X direction. As shown in Figure 2, due to the existence of machining errors and installation errors, the vertex distance of the actual elliptical paraboloid array may be biased in both the X and Y directions. Therefore, it is our responsibility to accurately calibrate the vertex distance between elliptical paraboloids in the array to reduce systematic errors in actual applications.
# Chinese Character Component Deformation Based on AHP
## 2 Related Works
### Chinese Character Component Deformation  
Existing methods for the deformation of Chinese character components can be divided into three categories. One is to start from the strokes and formulate transformation rules. This method usually makes mistakes in the structure of Chinese characters and has a small scope of application. For example, [PERSON] et al. [16] proposed a deformation technique based on the skeleton diagram of characters, which established a graphical model for strokes and generated deformation by isomorphic triangles and computational interpolation. [PERSON] et al. [17] designed a transformation sequence for each stroke and realized the distortion-free scaling transformation of Chinese characters, but only for the Song type.  
Another one is to convert other font styles into the font we need. For example, [PERSON] et al. [18] proposed a method to change Chinese characters from \"Regular Script\" to \"Semi-Cursive Script\" by using the Trail Point Set description. [PERSON] et al. [19] generated shape templates for characters, used the Coherent Pointment Drift (CPD) algorithm to implement point set registration, and finally achieved the deformation of Chinese characters from SimHei to KaiTi with shape interpolation. However, the Chinese characters obtained by this method usually have blurred and deformed outlines.  
Another deformation method is to deform the component as a whole by affine transformation. For example, [PERSON] et al. [20] achieved the deformation of the Chinese character component by establishing a control relationship between the skeleton point and the contour point. However, some manual intervention is still required in the process. The method in [21; 22] transformed the image function to establish a linear equation system about the parameters of the affine transformation, but it is powerless for binary images. [PERSON] et al. [23] selected three groups of generalized centroid points to calculate the affine transformation parameters, but the three points may be on the same straight line, resulting in no solution. There are also some algorithms based on local optimization, such as Moving Least Squares [24], but when used in Chinese characters, the phenomenon of stroke distortion often occurs.  
Based on the above problems, we proposed an AHP-based affine transformation control point selection method. This paper is organized as follows: In Section 3, we detail the feature point matching algorithm. In Section 4, we introduce the detailed application of AHP. In Section 5, we present the results of our method. In Section 6, we discuss our proposal and future work directions.
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
## 3 Preliminaries
### Definitions  
The amount of data transmitted from the source node to the destination node is \(\Lambda(t)\) in time interval \([0,t]\). Then, throughput capacity is defined as  
\[\mathcal{T}=\lim\limits_{t\rightarrow\infty}\left(\frac{\Lambda(t)}{t}\right).\]  
From network perspective, data can be bits, bytes, or packets. If there is a spatial and temporal scheme for scheduling transmissions, the throughput of each node \(\mu\) is feasible, called achieved throughput of each node.  
Network delay is the total time spent by data transmission from the source node to the destination node. Since we analyze delay from the perspective of network, packet processing time of the node is neglected.
## 4 Discussion
[27] measured BOP one year after consuming _L. reuteri_ pills for three weeks without additional SRP and obtained a reduction of 69.6% and 50.95% in the control group and 77.85% and 77.3% in the study group, respectively. [PERSON] et al. [17] observed a reduced BOP in the study group but did not obtain statistically significant differences in the three-month assessment. Various anti-inflammatory effects have been reported for _L. reuteri_, including the reduction in MMP-8 expression [28] and an increase in TIMP-1, which is a modulator of MMP activity [27]. Moreover, _L. reuteri_ proved to be associated with a decrease in proinflammatory cytokines, such as TNF-\(\alpha\), IL-1\(\beta\), or IL-17 [29]. It also forms reuterin, which induces oxidative stress [27]. This aspect is particularly important if we think of the important influence of periodontal inflammation on the systemic status and the relationship between periodontitis and cardiovascular diseases, osteoporosis, and other conditions and outcomes [30; 31; 32; 33]. In terms of microbiological results, one study reported that probiotic pills, either alone or in combination with SRP, decreased the number of _A. actinomycetemcomitants_, _P. gingivalis_, and _Prerotella intermedia_. [PERSON] et al. [24] reported greater reductions in periodontal pathogens on days 21, 90, and 180, but did not show any differences between groups on day 360, suggesting that the beneficial effects of probiotics are temporary. Our study is a pilot one, with certain limitations. First of all, the number of participants is not large enough to extrapolate our results in the general population. Furthermore, our research focused on clinical aspects only, with the main purpose to expand our interests in microbiological and immunological investigations related to the use of _L. reuteri_ as adjunctive periodontal therapy. Larger study periods of time are also required to clarify the long-term potential benefit of probiotics. We strongly believe that probiotics could play an important role in the management of particularly severe or resilient cases of periodontitis. However, of course, further studies are required in order to establish a clear work protocol, in terms of particular strains, dosage, number of sessions, and product use. We consider that research should be also conducted in order to obtain a locally delivered product with a controlled release system.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods
### Unsupervised Process Monitoring Method  
An unsupervised process occurs where the prior details of the process are unknown or are insufficient. Existing data-based monitoring methods for unsupervised processes include the Gaussian mixture model (GMM), the pattern recognition clustering method and the monitoring statistics method.
## 3 Experimental Results and Discussion
### Comparison with State-of-the-Art
[47] introduced a brain tumor classification approach having three main steps: first, brain images' transformation; second, salient features extraction; and finally, brain tumors' classification using Regularized Extreme Learning Machine (RELM) and achieved an accuracy of 94.23%. [PERSON] et al. [6] proposed a CNN-based model using data augmentation and claimed an accuracy of 96.13%. Recently, [PERSON] et al. [48] proposed a transfer learning-based customized Mask Region-Convolution Neural Network (Mask R-CNN) with a DenseNet-41 backbone architecture for classification and segmentation of brain tumors and achieved a classification accuracy of 98.34%. However, their approach was based on transfer learning, computationally intensive, and used a much larger network. [PERSON] et al. [32] introduced a DCNN that included a multiscale approach for brain tumor segmentation and classification using different processing pathways with data augmentation by elastic transformation and achieved an accuracy of 97.30% using 80 epochs. The classification function counts the tumor type prediction for every pixel and considers the highest value to be the predicted tumor type. Some researchers used the same dataset using transfer learning, where the pre-trained networks were used to classify the system by changing the number of neurons in the last fully connected layer [13; 49; 50]. Similarly, some cohorts modified this dataset while others processed only the tumor region in an image [9; 24; 45; 51]. In recent work, [PERSON] et al. [52] achieved an accuracy of 95.56% using \(n\)LBP and \(\alpha\)LBP features. A specifically designed solution using DCNN is simpler and faster than the pre-trained networks and do not require high-performance computing machines. VGG-16, a very deep architecture having 44 layers requires dedicated hardware for real-time performance and it is pre-trained on a huge dataset, namely ImageNet (more than one million instances) [53], using powerful computing machines for the categorization of 1000 object classes. [PERSON] et al. [50] augmented the brain tumor dataset and used transfer learning. He achieved the best result of 98.69% with VGG-16 pre-trained architecture using a stochastic gradient descent approach. Similarly, [PERSON] and [PERSON] [51] achieved 98.60% accuracy by using 100 tumor images in the transverse plane for each class, with a training to test ratio of (70:30)%, using CE-MRI variant and pre-trained AlexNet. The details need to be explored for the results in case the entire dataset is used along with its generalization capability. Similarly, the methodologies requiring regions of interest, although computationally less expensive, require a dedicated panel of experts for marking the regions to work on a regular basis.
# Optimized Combination of Local Beams for Wireless Sensor Networks
## 3 Proposed Algorithm
**\(\mathfrak{S}\) starts the BP process from broadcasting the _beam perturb message_ to \(\mathcal{D}\). When each \(d_{i}\) receives this message, it steers the beam toward \(\theta_{i,\mathbf{t}}\) randomly chosen from \(\left\{-\pi+\frac{\pi}{N},\cdots,-\pi+\frac{\pi\left(2m_{i}-1\right)}{N}, \cdots,-\pi+\frac{\pi\left(2N-1\right)}{N}\right\}\). After switching the beam, the node responds by the _offer message_ containing \(d_{i}\) at a randomly delayed time [28], and \(\mathfrak{S}\) responds by the _approval message_. This step repeats until \(\mathfrak{S}\) receives the _offer message_ from all elements of \(\mathcal{D}\) or the scheduled time is over. **- Step 2-2: Sound. **\(\mathcal{D}\) simultaneously transmit the _sounding message_\(z\). All of the APs measure and send their \(\xi_{j}\left(\mathbf{\vartheta}_{\mathbf{t}},\mathbf{\Gamma}_{1}\right)\) to a linked radio network controller (RNC). The RNC feeds \(O\left(\mathbf{\vartheta}_{\mathbf{t}}\right)\) back to \(\mathfrak{S}\), and \(\mathfrak{S}\) records \(O\left(\mathbf{\vartheta}_{\mathbf{t}}\right)\). **- Step 2-3: Transition. ** Once \(\mathfrak{S}\) receives the feedback signal, it checks whether the condition of \(O\left(\mathbf{\vartheta}_{\mathbf{t}}\right)\leq\mu\) is met. If so, go to **Step 2-5**. Otherwise, the decision of the acceptance or rejection of the state transition is conducted. If \(O\left(\mathbf{\vartheta}_{\mathbf{t}-1}\right)\) is not ready for this decision, go back to **Step 2-1**. If ready, \(O\left(\mathbf{\vartheta}_{\mathbf{t}-1}\right)\) is replaced with \(O\left(\mathbf{\vartheta}_{\mathbf{t}}\right)\) when the transition is accepted. After then, go back to **Step 2-1**. **- Step 2-4: Cooling Temperature. ** The inner loop from **Step 2-1** to **Step 2-3** is repeated at the temperature \(T_{f}\) for the \(R\) iterations. After the loop is terminated, \(T_{f}\) is reduced to \(\rho T_{f}\). Then, go back to **Step 2-1** and run another loop at \(T_{f+1}\). **- Step 2-5: Optimization termination. ** When the temperature finally reaches \(T_{F}\) or \(O\left(\boldsymbol{\phi_{t}}\right)\leq\mu\) is met, the whole optimization process finishes, and the well-optimized combination of the beams \(\boldsymbol{\vartheta_{opt}}\) will be obtained.
## 7 Discussion  
The introduction of an ITD chiefly has two effects on the harvester's performance: the lowering of the main resonance frequency and the appearance of a new resonance peak that can be exploited in order to harvest energy  
The first effect could be achieved by adding a tip mass, but the tip mass is much larger than the ITD mass and it leads to a large increase in the inertia forces. This fact increases the generated power (Figure 16) but also the stress inside the piezoelectric layer (Figure 17). Hence, an ITD is useful for trimming a harvester when the vibration levels are high and the stress inside a harvester equipped with a tip mass could reach dangerous values.  
The lowering of the resonance frequency could also be attained by adjusting the geometric and inertial parameters of the harvester without inserting an ITD. The basic theory of vibrating beams [23] shows that this effect could be attained by increasing the length and decreasing the width of the rectangular cantilever harvester (slender harvester), but the results of numerical simulations (Table 3) show that the stress near the clamped end increases. The reduction in the equivalent bending stiffness of the composite cross section could reduce the natural frequency, but also in this case the harvester becomes rather weak and it could be plastically deformed and/or damaged during manipulations. Another possibility is the modification of the shape of the harvester, even if the deposition of the various layers on a non-rectangular base could lead to practical problems. To make a comparison an inverse-tapered harvester [42] having the same cross section and the same volume of piezo material as PPA 1001 was investigated. Numerical results, which are reported in Table 3, highlight that also in this case there is a large increase in stress near the clamped end.  
The second effect of the ITDs (the presence of two resonance peaks) presents some limits and some potentialities as well. The main limit is the large frequency interval \((\Delta f)\) between the peaks (64 Hz in ITD1). Therefore, the harvesters with these ITDs cannot be considered broadband harvesters. The analytical model and experimental results on the prototypes (Table 2) actually show that \(\Delta f\) can be decreased by decreasing the mass of the trimming device, nevertheless, it is rather difficult to obtain two close peaks (\(\Delta f<10\) Hz). The potentialities of the harvesters with ITDs are related to the fact that periodic excitation with relatively large frequency intervals between the harmonic components is rather common in machines. Typical examples of periodic excitation can be found in rolling bearings [43], electrical machines [43], piston machines [44] (engines and compressors), fans and blowers [43] (harmonics of the blade passing frequency), and in geared systems [45] (harmonic of the mesh frequency). In these cases, the harvesters with ITD can be trimmed to a couple of sharp and stable peaks that are characteristic of the spectra of these sources of excitation. Numerical simulations show that ITD2 can be trimmed to 90 Hz and 180 Hz with an overall harvested power of 1.37 mW.
# An Improved Target Network Model for Rail Surface Defect Detection
## 4 Experimental Results and Analysis  
### Evaluation Metrics  
To validate the effectiveness of the improved algorithm for detecting small targets, this study conducted ablation experiments under consistent training methods and environments. MobileNetV3, k-means++ clustering, and EIOU loss function modules were sequentially added to the original YOLOv7 algorithm in the ablation experiments. Table 1 shows results from the ablation tests.  
In Table 1, \"\(\surd\)\" indicates that the corresponding module was used in the experimental algorithm, while \"\(\times\)\" indicates that the module was not used in the algorithm. Overall, in detected object categories, our model showed higher classification accuracy than YOLOv7 for three diseases, with the maximum accuracy difference between them being 5%. Our overall false positive rate was lower than that of YOLOv7. Specifically, optimizing the model's learning capability by using MobileNetV3 as the backbone network on the original network resulted in an average detection accuracy improvement of 1.6%, with improvements in detection accuracy for various target classes; using the EIOU as the bounding box regression loss function addressed the unclear definition of aspect ratios and the sample imbalance issue in the regression, leading to a 2.5% mAP increase; and incorporating the k-means++ clustering algorithm better adjusted to targets of different scales and shapes, enhancing network information acquisition capabilities and resulting in a 0.4% and 2.3% accuracy improvement for two smaller target classes, respectively, with a minor impact on larger target classes in image processing operations, hence no improvement in the Joint class accuracy. By conducting a sequence of ablation experiments, we were able to clearly observe the beneficial impact of each enhancement on the overall detection performance, thereby substantiating the effectiveness of these enhancements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MobileNetV3**} & \multirow{2}{*}{**EIOU**} & \multirow{2}{*}{**k-means++**} & \multirow{2}{*}{**Input/(Pixel \(\times\) Pixel)**} & \multicolumn{3}{c}{**AP/(\%)**} \\ \cline{5-8}  & & & & **Joint** & **Squats** & **Ssquats** & **[EMAIL_ADDRESS]/(\%)** \\ \hline \(\times\) & \(\times\) & \(\times\) & 640 \(\times\) 640 & 99.4 & 95.8 & 75.5 & 90.2 \\ \hline \(\surd\) & \(\times\) & \(\times\) & 640 \(\times\) 640 & 99.6 & 96.3 & 79.5 & 91.8 \\ \hline \(\surd\) & \(\surd\) & \(\times\) & 640 \(\times\) 6
## 4 Numerical Applications
### System Description and Basic Data
The output power of the energy storage system of the four cases will be illustrated below, respectively. Figure 7 indicates the daily output power of energy storage system at different installation sites. Figure 7a-d shows the daily output power of the energy storage system when the energy storage is located at LP30, LP40, LP45 and DG, respectively. After the further analysis of the results of the presented method, we find that the energy storage is charging power during the period of 11:00-22:00 and discharging power during the period of 7:00-11:00. The change in energy stored in the energy storage is limited to the maximum energy stored in the energy storage and the depth of discharge. From Figure 7, comparing the output power of the energy storage installed at different sites, we find that the charge and discharge time of the energy storage have a consistent trend. The output power of the energy storage located at LP40 is more than the others, and when it is located at DG, the output power is more than at LP45 and LP30. The energy storage located at LP40 improves the system reliability more effectively. Furthermore, the output  
Figure 7: Daily output power of energy storage located at DG, LP30, LP40 and LP45, respectively. (a) Daily output power of energy storage at DG; (b) daily output power of energy storage at LP30; (c) daily output power of energy storage at LP40; (d) daily output power of energy storage at LP45. power of energy storage at LP30 and LP45 has the same power curve in the chosen 24 h. Therefore, when the energy storage is installed at the end of the distribution network, the energy storage will have a greater contribution to system reliability.
## 3 Methodology
### Error Correction and Wind Speed Generation  
The flow diagram in Figure 3 shows the proposed algorithm for error correction for the simulated wind speed using Equations (10) and (14) for Weibull and Rayleigh distribution functions, respectively. Initially, the sequential variation data of wind speed is calculated, and mean variation is recorded. We assume that the wind distribution follows the persistence model. Hence, the predicted wind speed should also have the same sequential variation. Therefore, the simulated wind speed is checked each time to ensure that it is within the acceptable range (\(-\alpha\) and \(\alpha\)). If it is not within the limits, the algorithm of Gaussian filtering is applied [21]; this algorithm would prevent unnecessary deviations of the predicted value from the range extracted from historical data. Thus, error-corrected wind speed is generated and is used as the predicted value for WEBS and RYMS and input for other machine learning models. The error-corrected model based on WEBS is abbreviated as WEBSEC and the error-corrected model based on RYMS is abbreviated as RYMSEC.