esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Collection
### Damage Image Data Collection Using UAVs  
To collect damage image data, a rotary-wing-type UAV was employed due to its ability to perform vertical take-off and landing, maneuver in tight spaces, and hover with stability. In order to capture damage on the underside of railway bridges, the camera was mounted on the top side of the UAV body. The UAV was equipped with two cameras simultaneously: a wide-angle camera for mapping the overall structure and a high-resolution camera for capturing detailed images of localized damage. The spatial resolution of a captured image is determined by the camera's image sensor size, focal length, and the distance from the camera to the subject. This relationship defines the Ground Sampling Distance (GSD), which represents the real-world physical size corresponding to one pixel in the image calculated as the distance between the centers of adjacent pixels projected onto the surface of the target object--in this case, the railway bridge. To ensure reliable detection of fine surface defects such as micro-cracks, it is essential to acquire images under conditions that yield a sufficiently small GSD. In this study, when the UAV-mounted camera was positioned approximately 4 m from the surface of the railway bridge, the resulting GSD was 0.277 mm. This resolution is adequate for detecting cracks as narrow as 0.3 mm, meeting the visual inspection standards typically required in railway infrastructure maintenance. The UAV maintained a consistent flight path at an average distance of approximately 4 m from the bridge surface. High-resolution images were captured at a rate of one frame every 2 s, with a horizontal field of view (FOV) of 4091.4 mm and a vertical FOV of 2731.4 mm at that distance. To ensure sufficient coverage with approximately 10% overlap between images, the UAV flew at a speed of approximately 2.00 m per second. Each flight typically lasted around 20 min, with battery replacements conducted as needed to continue capturing images along the entire bridge length.
## 2 Laryngeal Image Modalities
### Laryngeal High-Speed Videoendoscopy
#### 2.2.2 Limitations  
* Due to the huge amount of data acquired, storing and visualization are great problems. For instance, 10 s recording data at speed of 10,000 fps would require 2 h and 46 min to view the whole recording at a speed of 10 fps [4; 27].  
* It is not possible to provide real-time audiovisual feedback due to the high temporal resolution of HSV. However, it is possible to align HSV with acoustics and EGG signals to provide more precise measures that can improve clinical diagnosis.  
Figure 4 illustrates the procedure used to study the vocal folds motion based on laryngeal image processing techniques. Glottal gap segmentation and the FP (third and fourth row in Figure 4), have been extensively used in HSV and VS to simplify the visualization of the vocal folds dynamics and to provide objective parameters to accurately identify the presence of organic voice disorders [44; 45], classify functional voice disorders [46; 47], vibratory patterns [48; 49], discriminate early stage of malignant and precancerous vocal folds lesions [50], among others [7; 39]. Despite the progress achieved to describe the vocal folds dynamics using laryngeal image-processing, the task is still challenging and poses a difficult computer vision problem.  
Figure 3: Illustrations of the Laryngeal High-Speed Videoendoscopy (HSV) sampling effect for two different frame rates. Adapted from [27].
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 5 Altitude Bias
The optimal values of the estimated values, assuming uniform distribution of heights, are \(\Delta H_{p}\)= 298.72 m and \(\Delta T\) = 17.06 \({}^{\circ}\)C. Although those are not the actual values used for the EUROCONTROL model, the simplified model is able to adjust quite finely to it, and therefore seems a promising model for altitude bias error estimation. If should be noted the purpose of this model is not obtaining a very accurate estimate of \(\Delta H_{p}\) and \(\Delta T\), but to be able to approximate correctly the altitude bias relation with geometric height.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 4 Discussion
Finally, in the ICU patients in this work, the assumption that the breathing motion is primarily in the vertical direction was true; however, in other environments, this might not necessarily be the case, and as such, the methodology should be extended such that the system can automatically detect the direction of the respiration and adapt the assumptions based on it.
###### Abstract  
Distributed video coding (DVC) is a novel coding paradigm that offers low computational encoding relative to conventional video-coding framework at the expense of high-decoding computational complexity. The challenging part of this video-coding framework is achieving better rate-distortion (RD) compared with conventional codec performance. A suitable and accurate correlation noise model (CNM) is crucial in improving the RD performance by achieving high coding efficiency and making decoding less computationally demanding. Since the correlation is nonstationary and time-variant and can vary from frame to frame, offline CNM estimation is not feasible for practical applications and real-time decoding. An online CNM may be the solution to this problem. In DVC, neither Wyner-Ziv frame (WZF) nor estimated side information (SI) of the corresponding WZF is available at the encoder. Therefore, online estimation of the CNM and its parameters can be quite challenging. The contribution of this research work is a novel online CNM which is computed by taking the mean of each transformed coefficient band and deployed for two different codecs. Our proposed codec, DIVCOM, which stands for \"Distributed Video Coding with Online Band Mean Correlation Noise Model\", outperforms the existing baseline codec, DISCOVER (DIS), in both coding efficiency and peak signal-to-noise ratio (PSNR). The DIVCOM codec achieves coding efficiency of up to 8.05 kbps, and PSNR ranges from 0.0245 dB to 0.18 dB. An extended version of DIVCOM incorporating phase-based side information called PDIVCOM achieves coding efficiency up to 10.9 kbps, and PSNR ranges from 0.019 to 0.17 dB compared to DIS.  
distributed video coding; online correlation noise model; online correlation noise modeling for DVC applications; CNM for coding efficient 2022
## 3 Methodology
### System Optimization
#### 3.4.4 Adaptive Penalty Function  
In addition to gradient-based optimization, we introduce an adaptive penalty function to dynamically adjust the system's behavior based on real-time performance metrics. The penalty function \(P\) is designed to penalize high variance \(\sigma^{2}\) and entropy \(H\) in the LLMs' responses, as discussed earlier. The adaptive penalty function can be formulated as follows:  
\[P(W)=\beta_{1}\sigma^{2}(W)+\beta_{2}H(W), \tag{8}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are adaptive weights that are updated based on the system's current performance. For example, if the system detects a high hallucination rate, \(\beta_{1}\) and \(\beta_{2}\) can be increased to penalize variance and entropy more heavily, encouraging the models to produce more consistent and confident responses, ultimately reducing hallucinations.  
The adaptive nature of this penalty function enables the system to adjust to different tasks and scenarios, ensuring that the optimization process remains effective in a wide range of conditions.
## 2 System Model and Channel Model
### Imperfect Channel Model  
In the IRS-assisted FL system, there are two kinds of channels, that are direct UDs-BS channel and cascaded UDs-IRS-BS channel. For cascaded channel, it is a challenge to directly obtain the IRS-related channels due to the passive features of the IRS. Thus, we focus on channel error models in the cascaded UDs-IRS-BS channel.  
The composite channel from the \(k\)-th UD to BS can be rewritten as  
\[h_{k}=h_{d,k}+\mathbf{g}_{k}\Theta\mathbf{h}_{r,k}=h_{d,k}+\mathbf{\phi}^{H}\mathbf{a}_{k}, \tag{6}\]  
where \(\mathbf{\phi}=[\phi_{1},\phi_{1},\dots,\phi_{n}]^{T}\), \(\mathbf{a}_{k}=diag(\mathbf{g}_{k})\mathbf{h}_{rk}\), \(\phi_{n}=e^{i\theta_{k}}\). Assuming that both direct UDs-BS channels and cascaded UDs-IRS-BS channels are imperfect, which can be represented as  
\[h_{d,k}=\hat{h}_{d,k}+\varepsilon_{d,k}, \tag{7}\]  
\[\mathbf{a}_{k}=\hat{\mathbf{a}}_{k}+\mathbf{\varepsilon}_{a,k}, \tag{8}\]  
where \(\hat{h}_{d,k}\) and \(\hat{\mathbf{a}}_{k}\) stand for the direct UDs-BS estimated channel and cascaded UDs-IRS-BS estimated channel, respectively. \(\varepsilon_{d,k}\) and \(\mathbf{\varepsilon}_{a,k}\) are estimated error followed the CSCG distribution, i.e., \(\varepsilon_{d,k}\sim CN(0,\sigma_{d,k}^{2})\) and \(\mathbf{\varepsilon}_{a,k}\sim CN(0,\sigma_{d,k}^{2}\mathbf{I})\), where \(\sigma_{d,k}^{2}\) and \(\sigma_{d,k}^{2}\), respectively, represent variance of the direct channel estimation and cascaded channels estimation. Thus, the composite channel from \(k\)-th UD to BS can be further rewritten as  
\[h_{k} =h_{d,k}+\mathbf{\phi}^{H}\mathbf{a}_{k}\] \[=\hat{h}_{d,k}+\varepsilon_{d,k}+\mathbf{\phi}^{H}(\hat{\mathbf{a}}_{k}+ \mathbf{\varepsilon}_{a,k})\] \[=\hat{h}_{k}+\varepsilon_{h,k}\sim CN(\hat{h}_{k},\sigma_{h,k}^{ 2}), \tag{9}\]  
where \(\hat{h}_{k}=\hat{h}_{d,k}+\mathbf{\phi}^{H}\hat{a}_{k}\) and \(\varepsilon_{h,k}=\varepsilon_{d,k}+\mathbf{\phi}^{H}\varepsilon_{a,k}\). Due to the lack of correlation of estimation errors for the different channel, we have  
\[\sigma_{h,k}^{2} =E((\varepsilon_{d,k}+\mathbf{\phi}^{H}\varepsilon_{a,k})(\varepsilon _{d,k}+\mathbf{\phi}^{H}\varepsilon_{a,k})^{H})\] \[=E(\varepsilon_{d,k}\varepsilon_{d,k}^{H}+\mathbf{\phi}^{H}\varepsilon _{a,k}\varepsilon_{a,k}^{H}\mathbf{\phi})\] \[=\sigma_{d,k}^{2}+N\sigma_{a,k}^{2}, \tag{10}\]
## 2 Materials and Methods
### Instrumentation  
The specific analytes were examined using a UHPLC (Ultimate 3000/Dionex, Germering, Germany) system equipped with a binary pump and a UV/DAD detector. The separation was performed on an Inertsil ODS-3, C18 (150 mm \(\times\) 4.6 mm \(\times\) 5 \(\upmu\)m) column (Merck Germany, Darmstadt, Germany). The isocratic elution program was based on 80% methanol as the mobile phase at a flow rate of 1 mL min\({}^{-1}\), with a running time of 6 min. The detection wavelength was set at 268 nm, and the column temperature was maintained at 32 \({}^{\circ}\)C with an injection volume of 20 \(\upmu\)L. Under the above chromatogram conditions, the retention times of BES, TES, and PRO were approximately 4.08 min, 4.49 min, and 5.42 min, respectively. The solution's pH was determined using a Metrohm pH-meter model 691 (Herisau, Switzerland). Infrared spectra were captured using a V70 FT-IR spectrophotometer (Bruker Optic GmbH, Ettlingen, Germany) utilizing a single reflection ATR cell (DuraDisk, fitted with a diamond crystal). The spectral data were obtained at an ambient temperature in the 400 to 4000 cm\({}^{-1}\) range. Each sample underwent 290 scans at a resolution of 4 cm\({}^{-1}\), and five duplicate spectra were obtained for reproducibility assessment. The \({}^{1}\)H NMR experiments were conducted using an NMR spectrometer model Ascend 400, 400 MHz (Bruker Biospin, Rheinstetten, Germany). The HDE mixture samples were created on 5 mm NMR tubes based on weight, using around 30 mg of eutectic mixture. Subsequently, 0.5 mL of deuterated chloroform (CDCl3) was added. The sample was thoroughly mixed using vortex mixing to ensure uniformity.
# Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas
###### Abstract
We use the student profile scenario shown in Figure 1 to motivate our work. There is a table named _student_ in the database of the academic affairs office and another table named _member_ in the database of the student union. The student _[PERSON]_ has records in both. Language-level disambiguation alone does not resolve this kind of mismatch. A general-sense knowledge graph will never assume this semantic equivalence either. Therefore, it is necessary for an effective criterion on the knowledge level to verify this semantic similarity on the overlaps of the two schemas. This paper proposes property-based semantic similarity criteria to evaluate the overlaps of schemas. For example, _name_ and _taught-by_, in the table _student_, are properties about the entities, such as _Mary_, of that entity type (in a database table). An entity type is denoted by a feature vector formed by such properties. Coverage and flexibility are used to compute the compatibility between the schemas. Firstly, the vectors are disambiguated by the language-level tools and then matched according to the semantic similarity in three levels: label, property and individual. Secondly, the thresholds  
Figure 1: Equivalent entity type student and member in different schemas. for each level are found to be dependent on the domain. Thirdly, the properties are propagated through the _is-a_ hierarchy of the entity types and properties to accumulate the weights of those entity types. The weights are exploited to calculate the schema overlaps. Finally, the coverage and flexibility are computed to help choose the schemas. The contributions of the paper are summarized as the following:  
1. A three-fold semantic similarity is proposed to substitute the classical identity to compute schema overlaps;
2. A property-based weight is defined to extend the influence of the entity types to compute the coverage and flexibility;
3. A set of rules is accumulated to apply the thresholds in the practical medical domain. The rest of the paper is organized as follows: in Section 2, we will introduce the related work. In Section 3, we will introduce the related concepts. In Section 4, we will give the measurement method of semantic equivalence and the formula of quantitative calculation. In Section 5, we will define weight and the calculation method of two evaluation criteria based on weight and we will describe experiments carried out to verify the correctness and effectiveness of the proposed method in Section 6. The last section is Conclusions and Future Work.
# Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting
## 2 Materials and Methods  
### Adaptive Boosting Regression (Adaboost)  
The boosting technique known as the AdaBoost algorithm, sometimes known as adaptive boosting, is used as an ensemble method in machine learning. The weights are redistributed to each instance, with higher weights being given to instances that were mistakenly classified, hence the name \"adaptive boosting.\" For supervised learning, boosting is used to lower bias and variance. It operates under the premise that learners improve incrementally. The meta-estimator adaptive boosting regression [84] starts by fitting a regressor to the initial dataset. It then fits additional copies of the regressor onto the same dataset. The only exception is that the weights of the instances are changed in accordance with the error of the most recent prediction. Consider a dataset \(S=(x_{1},y_{1}),\ldots,\)\((x_{n},y_{n})\) that is derived from a time series. The dataset is composed of \(n\) pairs of observations, and each observation is given a weight \(w_{i}\). The likelihood that an observation will be included in the training set at iteration \(k\) is then determined for each observation \(i\) based on the weight it is given. The weighted sum of the probabilities is then used to get the average loss \((l_{k})\) for the model \(k\) over all the observations \(i\). The average loss \((l_{k})\) and probability \(p^{k}\) mathematical formulae are as shown in Equations (1)-(3):  
\[p_{k}=\frac{w_{i}}{\sum w_{i}} \tag{1}\]  
\[l_{k}=\sum_{i=1}^{n}l_{k}p_{k} \tag{2}\]  
\[w_{i}^{k+1}=w_{i}^{k}\beta_{k}(1-l_{k}) \tag{3}\]  
where \(p_{k}\) is the probability at iteration \(k\), average loss at iteration \(k\), \(w_{i}^{k+1}\) is the updated weight at iteration \(i\), \(w_{i}^{k}\) is the prior weight and \(\beta_{k}\) is the model loss.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 6 Conclusions  
In this study, one ordinary reinforced concrete column and eight SSRC columns were tested under eccentric compression. Furthermore, the SSRC columns were theoretically analyzed based on the finite element model and current codes. The following conclusions were obtained:  
1. Eccentricity had a significant effect on the failure mode of SSRC columns. The failure modes of SSRC columns under eccentric compression were similar to those of ordinary reinforced columns.
2. SSRC columns exhibited good ductility under load. At the ultimate load, the load of the specimens under small eccentricity decreased rapidly, whereas the others under large eccentricity could still maintain a certain displacement growth.
3. The lateral displacement curves of SSRC columns were symmetrically distributed along the middle and were approximately sinusoidal. Under the same eccentricity, the reinforcement ratio had negligible effect on the lateral displacement corresponding to the ultimate load.
4. The mechanical properties of SSRC columns under small eccentric compression were better than expected; however, the ultimate loads under large eccentric compression were lower than the theoretical values. Therefore, the effects of the relative eccentricity and characteristics of stainless-steel reinforcement on the second-order effect were considered. An optimization model of the ultimate load of SSRC columns was proposed. The calculation results of this model were accurate and safe, and the \(N_{\text{u}}\)-\(M\) curve obtained was in agreement with the test and FEA results. Therefore, it is recommended to regard SSRC columns as slender columns and use the proposed optimization model to calculate the ultimate loads of SSRC columns in practice.
5. For the SSRC columns under small eccentric compression, changes in the reinforcement ratio have a negligible effect on the crack development when compared with changes in the eccentricity. The average crack widths of SSRC columns at failure were between 0.13 and 0.42 mm.
6. Based on the existing experimental results, the value of \(\alpha_{\text{cr}}\) in the crack width calculation formula for the SSRC columns under the long-term load was set to 2.7. The calculated results for the long-term crack width at \(\alpha_{\text{cr}}=2.7\) were in good agreement with the experimental results, and the relative error was less than 4%.  
Conceptualization, Q.L.; experiment, Y.Z., Y.K. and W.G.; software, W.G.; validation, Y.K. and W.G.; resources, Q.L.; writing--original draft preparation, Y.K.; writing--review & editing, W.G.; supervision, Q.L.; project administration, Q.L. All authors have read and agreed to the submitted version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51679220) and the Open Project Fund of Research Center on Levee Safety and Disaster Prevention of MMR (No. 2018002).  
The authors declare no conflict of interest.  
Figure 14: Comparison of crack widths calculated using the modified formula with the experimental crack widths.
## 3 Experimental Setup
### Data Receiving and Processing  
The USRP both in destination and eavesdropper node operates in the same way. They monitor at the frequency of 2.45 GHz and save received complex signals to the laptop. Thanks to the extensive GNU Radio platform and the stable USRP hardware, we have little worries about how to generate a baseband bit stream and shifting it to a high frequency is much more easy and there is no carrier frequency offset problem compared to a traditional transmission device. The difficult part in the experiment however is processing the received data. The data are all saved in a complex signal form which can be read from MATLAB. We have to determine which part of the data is the desired signal and leave out the white noise. Besides, complex signals received at the destination and eavesdropper consists of the two parts received from the source and relay, respectively. Synchronization is required to combine these two parts and after synchronization, two different parts of the complex signal are combined using the maximum ratio combining (MRC) method.
## 3 Results and Analysis
### Theoretical Analysis
#### 3.3.5 Examination of the Mechanical Model of the Filling Body  
The CPB roof of the drift-type stope is mainly subject to tensile damage, and since the tensile strength of the filling body is low, it is reasonable to carry out the parameter design of a drift-type stope with tensile strength as the index. In this study, by comparing different mechanical models of the filling body and combining the field test results, the calculation model of the load on the filling body of the drift-type stope is determined, and the calculation model of the tensile stress on the filling body is also obtained based on the thick plate theory (see Equation (32)). Combining the two models, a mechanical model of the filling body subjected to a tension based on the parameters of the stope and the physical mechanical parameters of the filling body can be further obtained, as shown in Equation (33). The parameters in Equation (33) are simple and easy to obtain, which can guide the design of filling mining more easily.  
\[\left\{\begin{array}{l}q_{\max}=\frac{\gamma S}{k\tan\varphi p}\left[1-\exp \left(-\frac{kpH\tan\varphi}{5}\right)\right]\\ \sigma_{\max}=\frac{96\sigma_{\max}}{\pi^{2}\lambda^{2}}\left\{\begin{array}{ l}\frac{a^{2}+\mu b^{2}}{a^{2}b^{2}\left[\left(1/a\right)^{2}+\left(1/b\right)^{2} \right]^{2}}-\frac{9a^{2}+\mu b^{2}}{3a^{2}b^{2}\left[\left(1/a\right)^{2}+ \left(3/b\right)^{2}\right]^{2}}\\ -\frac{a^{2}+9\mu b^{2}}{3a^{2}b^{2}\left[\left(3/a\right)^{2}+\left(1/b\right) ^{2}\right]^{2}}+\frac{a^{2}+\mu b^{2}}{a^{2}b^{2}\left[\left(3/a\right)^{2}+ \left(3/b\right)^{2}\right]^{2}}\end{array}\right\}\end{array}\right\} \tag{32}\]  
\[\sigma_{\rm{f}}\geq\sigma_{\max}=\frac{96\gamma\left[1-\exp\left(-\frac{kpH \tan\varphi}{5}\right)\right]}{k\pi^{2}\mu^{2}\tan\varphi}\times \tag{33}\]  
The calculated values of the loads and tensile stresses on the filling body are obtained by substituting the relevant parameters of the 48# stope and 40# stope into the calculation model, and the calculation results and the field measurement results are shown in Figure 8. From Figure 8a, it can be seen that the errors of the calculated values of the overlying loads on the CPB roofs of the 48# stope and 40# stope are 5.0% and 5.2%, respectively. From Figure 8b, it can be seen that the errors of the calculated values of the maximum tensile stresses applied are 4.1% and 2.5%, respectively. All errors are less than 6%, which indicates the high reliability of the mechanical model of the filling body.  
Figure 7: Effect of factors on stress: (**a**) size of the stope, (**b**) parameters of the filling body.