esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-Scale Fusion Method for Ancient Bronze Ware X-ray Images in NSST Domain
## 3 Results
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **EN** & **AG** & **MI** & **Q\({}^{\text{AB/F}}\)** & **PSNR** & **VIF** \\ \hline LDR & 4.5385 & 8.5701 & 3.5222 & 0.7052 & 64.4021 & 0.9007 \\ \hline NMP & 4.8727 & 8.6665 & 3.3458 & 0.7241 & 65.9974 & 0.8981 \\ \hline F-PCNN & 4.3310 & 8.3193 & 2.7501 & 0.6869 & 66.0704 & 0.8739 \\ \hline LatLRR & 5.1003 & 7.2020 & 3.8165 & 0.6782 & 66.0627 & 0.8890 \\ \hline IVFusion & 4.4806 & 9.3643 & 2.2952 & 0.5079 & 57.9535 & 0.8172 \\ \hline PL-NST & 4.4114 & 8.3688 & 2.8678 & 0.6682 & 65.4488 & 0.9123 \\ \hline MMIF & 4.8872 & 8.2915 & 3.0396 & 0.6212 & 65.4325 & 0.8730 \\ \hline IFE & 4.7079 & 9.4627 & 4.2427 & 0.7322 & 62.6034 & 0.9251 \\ \hline Proposed method & 4.9274 & 9.9533 & 4.5322 & 0.7628 & 65.9145 & 0.9849 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean values of six evaluation indexes for 36 fused images. This table shows that the proposed method is competitive with the recent image fusion methods in terms of computational efficiency. Specifically, the running time of the proposed method is comparable to that of the PL-NSCT method and significantly better than those of the LRD, NMP, F-PCNN, LatLRR, IVFusion, and MMIF methods. The IFE method results in the shortest execution time but does not yield competitive results. In summary, the method employed in this paper effectively preserves both the decorative and disease information present in the source images of bronze mirrors. It achieves a visual quality that closely resembles human perception and surpasses the performance of the other eight methods in terms of overall fusion results. This significant outcome holds great potential for advancing research and conservation efforts concerning bronze mirrors.
## 3 Results
### Assessment of Physicochemical Properties and Morphology of the Printing Substrate
#### 3.1.5 Fourier Transform Infrared Spectrometer  
The FTIR spectra for theophylline, gum, their physical mixture (PM), and the drug-loaded film are presented in Figure 5. The FTIR spectrum of theophylline showed characteristic peaks at 3119 cm\({}^{-1}\) (N-H stretching), 3063, 2989, and 2828 cm\({}^{-1}\) (C-H stretching), 1718 and 1666 cm\({}^{-1}\) (C=O stretching bands), 1567 and 1443 cm\({}^{-1}\) (ring stretching), 1241 cm\({}^{-1}\) (C-N vibration), and 1187 cm\({}^{-1}\) (C-O vibration) [34; 35]. The spectrum for the gum expressed major absorption bands associated with a -OH stretching vibration at 3384 cm\({}^{-1}\), and -CH stretching and bending vibrations at 2924 cm\({}^{-1}\), and at approximately 1410 cm\({}^{-1}\), respectively. The absorption band appearing at 1657 cm\({}^{-1}\) corresponds to the OH bonds of water molecules [34]. The FTIR spectrum of the physical mixture of the seed gum and theophylline contained important peaks corresponding to the main components. However, the theophylline peaks were rarely observed in the form of drug-printed substrate. This might be because the amount of printed drug was very small compared to that of the gum.  
Figure 4: Powder XRD patterns of gum, theophylline, their physical mixture (PM), and the drug-loaded/non-drug-loaded films.
## 1 Introduction
The complete process was divided into three main stages. Firstly, the EGSM beam travels from the source plane to the target plane in atmospheric turbulence. Then, after the interaction of the laser beam with the object surface, the beam propagates from the target plane to the receiver plane. Finally, we calculated the degree of polarization of the returned beam and the detailed parameters of the object were derived.
## 11 Responses for the Incremental-N
### Computational Burden and Real-Time Feasibility  
The proposed control architecture achieves an effective compromise between control performance, adaptability, and computational feasibility by integrating incremental-N modeling, Takagi-Sugeno fuzzy systems, discrete LQR design, and Extremum Seeking Control (ESC). While PID controllers are widely adopted due to their simplicity and low computational demand, they exhibit significant limitations when applied to multivariable, nonlinear, or strongly coupled systems, such as the thermal mixing tank addressed in this study. Additionally, the tuning process for PID gains in such contexts is often heuristic and lacks robustness.  
Model Predictive Control (MPC) offers a more sophisticated alternative by explicitly handling system constraints and nonlinearities through online optimization. However, this advantage comes at the cost of high computational complexity, as MPC requires solving an optimization problem at every sampling instant. Consequently, its real-time implementation becomes impractical for fast dynamic systems or platforms with limited processing capabilities.  
In contrast, the methodology proposed in this work exploits the structure of the incremental-N model to manage high-order reference signals and disturbances with improved steady-state accuracy. The fuzzy state-space framework allows for an efficient representation of the system's nonlinear dynamics, while the LQR formulation ensures stable and optimal multivariable control. Furthermore, ESC is employed exclusively as an offline optimization tool to tune the LQR weighting matrices, thereby avoiding any real-time computational overhead. Thus, the proposed approach combines the simplicity and efficiency of LQR with the adaptive tuning capabilities of ESC and the modeling flexibility of fuzzy systems, resulting in a control scheme that is both computationally efficient  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method for Obtaining \(Q\) and \(R\) Matrices** & \(J(\theta)\) & **RMSE (\(Q_{s}\))** & **RMSE (\(T_{s}\))** \\ \hline Global ESC with noise & 1.0830 & 0.8485 & 6.0797 \\ Global ESC without noise & 1.0691 & 0.8373 & 6.0641 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Value of \(J(\theta)\) and RMSE of \(Q_{s}\) and \(T_{s}\) for Incremental-1.  
Figure 32: Comparison of the response of \(T_{s}\) to excessive step-type reference signals using the Global ESC with and without noise for Incremental-1.  
and well-suited for real-time applications. It offers a viable alternative to PID and MPC, particularly in scenarios where nonlinearities, multivariable interactions, and high-order dynamics must be addressed without incurring excessive computational cost.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 3 Results
### Cardiovascular and SnO2 Parameters  
A total of eight studies analysed the accuracy of SRs in measuring cardiovascular outcomes (Table 1). The recording duration ranges between a minimum of 9 min to a maximum of 37.1 h of sleep. The accuracy of Oura Ring(r) devices was analysed in four studies: two studies analysed the second generation, and one the first generation, and one study the third one. Other types of SR analysed in the studies included CART Ring(r) in two studies, Senbiosys(r) in one study, and a prototype in one study. The control devices were ECG in five studies, an invasive Blood Pressure device in one study, a PSG in one study and the measurement of BP by auscultation in one (Figure 4). The outcomes explored were heart rate (HR) in five studies, atrial fibrillation (AF) in one study, and Systolic, Diastolic and Mean Blood Pressure (SBP, DBP, MBP) in two studies. Financial statements and possible conflict of interest were explored: in two studies funding was not specified, two were funded by public resources, four received private funding. A meta-analysis was conducted including three studies that analysed the accuracy of Oura Ring(r) devices in measuring nocturnal HR compared to ECG (Table 3). The meta-analysis found a pooled mean bias for nocturnal HR of \(-0.41\) (SD = 1.09; LOA_L = \(-2.65\), LOA_U = 1.81; CI_Lm = \(-5.95\), CI_Um = 5.11).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mean Bias (min)** & **SD** & **LoAs** & **CI\_Lm** & **CI\_Um** \\ \hline TST & \(-21.3\) & 5.4 & \(-69.9\), 27.4 & \(-45\),916.4 & 45,873.8 \\ REM & \(-18.2\) & 5.8 & \(-33.3\), \(-3.1\) & \(-281.5\) & 245.1 \\ \hline \hline \end{tabular} Abbreviations: SD = Standard Deviation, LoAs = limits of agreement, CI_Lm = 95\% confidence interval lower bound of the lower LoA, CI_Um = 95\% confidence interval upper bound of the upper LoA.  
\end{table}
Table 2: Results of the meta-analyses of the accuracy of Oura Ring® in monitoring sleep quality compared to PSG.  
Figure 3: (**A**) Sleep studies: control device characteristics. PSG: polysomnography. EEG: electroencephalography. (**B**) Sleep studies: smart ring characteristics. Gen: generation.  
Only one study assessed the precision of SRs in measuring oxygen saturation (SaO2) with respect to a pulse oximeter (Table 1). The study was carried out in the USA, using a prototype as SR, and Masimo Radical-7 pulse oximeter as a control device, with eleven healthy adults, and it received private funding. The overall Root Mean Square Error for the SR was approximately 2.1% for both the finger and the fingertip placement.
## 3 Off-Line Compression by Longest-First Substitution
### How to Update \(\mathit{SLSTree}(w_{k}^{i-1})\) to \(\mathit{SLSTree}(w_{k}^{i})\)
Due to the property of the longest first approach, we have \(|A_{h}|\geq\ell\). Thus \(v_{j}=\mathit{leaf}_{j}\). Moreover, for any \(j\leq k\leq p-1\), \(v(k)=\mathit{leaf}_{k}\). (See also the right illustration of Figure 7). It is thus clear that each \(v(k)\) can be found in constant time. Since \(|A_{h}|\geq\ell=\mathit{LRF}(w_{k})\), the leaves corresponding to \(w_{k}^{i-1}[p+x-1\ :]\) with \(1\leq x\leq\ell\) do not exist in \(\mathit{SLSTree}(w_{k}^{i-1})\). From the above discussions, we conclude that:  
**Theorem 1**: _For any string \(w\in\Sigma^{*}\), the proposed algorithm for text compression by longest first substitution runs in \(O(|w|)\) time using \(O(|w|)\) space._  
Pseudo-codes of our algorithms are shown in Algorithms 1, 2, and 3.
## 1 Introduction
### Methodology and Contributions  
In this article, a literature review investigation on the current research on VR conferencing and panoramic live broadcast is presented. By analyzing the related works, we determine the features and obstacles of VR conferencing. The procedure and key technologies of panoramic live broadcasting are also presented as well. With the use of available tools, the simple implementation of two immersive systems is given, aiming to provide a research and application reference for VR audio and video transmission, which can guide subsequent relevant research studies.
## 4 Experimental Results and Discussion
### Bead Geometry Prediction  
To test the prediction algorithm proposed in Section 3.1, a hollow wall of dimension 150 mm \(\times\) 50 mm with 15 layers was deposited using the following process parameters: laser power = 1.4 kW, wire-feed rate = 1.4 m/min, and travel speed of robot = 0.45 m/min. These initial process parameters were chosen to achieve a stable, uniform, and homogeneous final part, as shown in Figure 7.  
It is worth noting that the plot of the training set in Figure 8 shows a power decay trend in height across layers. This observed trend is the so-called self-regulation mechanism of the layer height, and it has been discussed by [PERSON] et al. in [25].  
In Figure 10, the bead-width-over-bead-height ratio across layers is plotted. From this experimental result a logarithmic increase can be observed for each layer. In the observation, the width-to-height ratio increases significantly during the first layers before reaching a threshold around layer 10.  
Interestingly, the prediction ratio, shown in Figure 10, indicates that the bead width's increasing ratio is more significant compared with bead height change over layers. The physical phenomenon observed, here, is due to the conduction of heat resulting from  
Figure 10: Bead width over height prediction.  
Figure 9: Result of the bead width prediction.  
the laser-matter interaction. When the wire is irradiated by the laser beam, the energy absorbed by the wire leads to creating molten pools and its deposition on the substrate. As the beads are added layer by layer, the heat source \"moves away\" from the substrate. The consequence is a decrease in the volume of material allowing the transfer of heat. This leads to an increase in the flux density, which inevitably leads to local heating, a modification of the local properties of the metal, and the wetting of the deposit, among other things. The spread of layers becomes important with the increase of bead widths and the decrease of the bead height. It is observed that this phenomenon stabilizes after a number of deposited layers, as shown Figure 11.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### ML Models
#### 2.4.4 Support Vector Machine  
The SVM is a very common ML algorithm used for classification. SVM finds the optimum boundary separating the data of different classes to map the space of the original variables into an unknown high-dimensional feature space, where the data are linearly separable. This is performed by constructing a hyper-plane or a set of hyper-planes. The hyper-plane, \(H\), is also known as the decision boundary and the data points closest to \(H\) are denoted as the _support vector_ (SV) points. The distance between \(H\) and the SV points are known as the margin \(M\). Equation (16) is the formulation of \(H\) where \(a\) is perpendicular to \(H\) and \(b\) is the offset of \(H\) from the origin along the normal vector \(a\).  
\[a^{T}x-b=0. \tag{16}\]  
SVM is a binary classification algorithm which can be extended to handle multiclass problems. In this study, SVM applied the error correcting output code (ECOC) or the \"one-vs-one\" approach for training. For the four classes specified in this study, six binary SVM models were used as learners. Minimizing the loss function was used as the decoding scheme where the loss function is represented as follows,  
\[\min_{a,b}\quad\frac{1}{2}||a||^{2}\quad\text{ s.t. }\quad y_{i}(a^{T}x_{i}-b)\geq 1, \tag{17}\]  
where \(||a||\) is the length of the vector \(a\), i.e., its Euclidean norm.
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 3 Results
### SOS and EOS Estimates from GPP  
The absolute errors in the modelled SOS and EOS are relatively well spread around the zero-line for all tree species (Figure 6). Errors located above the zero-line represent overestimated SOS or EOS, and errors located below the zero-line represent underestimated SOS or EOS. Overall, the absolute errors are larger for EOS compared to SOS, as denoted by the generally longer boxes. The larger absolute errors of EOS are particularly noticeable at some stations and for specific years, with the highest error at Starse in 2012 for _Tilia_ sp. (error of \(-\)48 days).  
Scatter plots of modelled vs. observed SOS (Figure 7) or EOS (Figure 8) based on the holdout years (left out during training) show relatively low RMSEs in general, considering the original temporal resolution of the remote sensing data (8 days). For SOS, the lowest RMSE of 7.71 days was recorded for _Tilia_ sp. A comparable RMSE of 7.89 days was obtained for _Fagus sylvatica_. The highest average RMSE recorded is 14 days for _Populus tremula_. The RMSEs of other species _Bettula pendula_, _Quercus_ sp. and _Fraxitus excelsior_ were 8.98, 9.07 and 11.1 days, respectively.  
The RMSE recorded for EOS is generally greater than 10 days, with the exception of _Fagus sylvatica_ and _Populus tremula_ (Figure 8). While the errors for EOS are generally higher than for SOS, this was not the case for _Populus tremula_ (9.07 days for EOS vs. 14 days for SOS).  
Figure 5: Mann–Whitney test for comparing the share of different land uses for two groups of best thresholds (\(\sim\)0.5, i.e., 0.45 to 0.55, and \(\
eq\)0.5, i.e., \(<\)0.45 or \(>\)0.55) at the substitute forests for SOS (**A**) and for EOS (**B**). “ns” and “**” mean non-significant (\(p\)-value \(\geq\) 0.05) and significant (\(p\)-value \(<\) 0.05) difference, respectively.  
Figure 6: Error (modelled—observed) of SOS and EOS per station, all species and each species separately.
## 2 HFRC Preparation and Characterization
### Concrete Production  
The mixing procedure of concrete needs to be rigorously controlled to ensure good workability, particle distribution and compaction, noting that the small particles tend to agglomerate which may break the chunks when the particles are dry. It is suggested to blend all fine dry particles before adding water and superplasticizer. In the climatic chamber with 90% humidity, the concrete samples were prepared with the following mixing procedures. First, the dry cementitious materials (cement, fly ash) and quartz sand were put together simultaneously and mixed for 1 minute at a low speed to achieve the binder-sand mixture. Afterwards, the water and superplasticizer were mixed and gradually poured into the mixture to improve its fluidity. Finally, the fibers were slowly added and mixed for another 5 to 8 min to ensure that all the fibers were evenly distributed in the mortar. 24 h later, the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Cement** & **Fly Ash** & **Water** & **Quartz Sand** \\ \hline kg/m\({}^{3}\) & 1165.9 & 145.7 & 327.9 & 590.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mixture for concrete.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Steel Fiber** & **PP-Fiber** & **PVA-Fiber** \\ \hline S1 & 0 & 0 & 0 \\ S2 & 0 & 2\% & 0 \\ S3 & 0 & 0 & 2\% \\ S4 & 2\% & 0 & 0 \\ \hline S5 & 0.5\% & 1.5\% & 0 \\ S6 & 1.0\% & 1.0\% & 0 \\ S7 & 1.33\% & 0.67\% & 0 \\ S8 & 1.5\% & 0.5\% & 0 \\ \hline S9 & 0.5\% & 0 & 1.5\% \\ S10 & 1.0\% & 0 & 1.0\% \\ S11 & 1.33\% & 0 & 0.67\% \\ S12 & 1.5\% & 0 & 0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fiber volumetric content for HFRC.  
Figure 1: Fibers used for HFRC material cast.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Cement** & **Fly Ash** & **Water** & **Quartz Sand** \\ \hline kg/m\({}^{3}\) & 1165.9 & 145.7 & 327.9 & 590.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of fibers.  
specimens were removed from molds and cured for another 6 and 27 days at room temperature (about 15-25 \({}^{\circ}\)C) with humidity \(>\)95%.  
It is worth noting that PP and PVA microfibers at 2% volume content negatively affect the fluidity of mixture due to their high specific surface area. Due to the lack of slump-flow test or J-ring test to assess the fluidity of the fresh mixtures, the qualitative evaluation of workability was achieved since the HFRC mixtures exhibit good deformability and proper stability to flow under their own weights. To ensure fluidity, superplasticizer-to-binder ratios were controlled between 0.06% (S1) to 0.5 % (S3). It was observed that HFRC with higher content PP or PVA-fiber exhibited poorer fluidity since more porous microstructure might be yielded due to relatively poor consolidation condition.
## 3 The Digital Simulation
### The Flow of the Fault Diagnosis Experiment  
The application experiments were conducted according to Figure 1.  
First, each fault sample was decomposed through EMD, and the 20 feature parameters presented in Table 2 of that sample's first five intrinsic mode functions were extracted to constitute a feature set of 100 features. All fault samples were described by the feature vector composed by this feature set.  
After that, the DFV of six REB faults presented in Table 1 was established through THFS proposed in this paper, and the FST was obtained at the same time. In accordance with the FST, the RS-DFV extraction tree (RS-DET) was set up for RS-DFV extraction, and the RS-DFVs of all the REB fault samples in Table 1 were calculated by means of the RS-DET.  
Finally, the PNN was used to construct a fault classifier, and the values of the related parameters in this fault diagnosis experiment are presented in Table 3. For each test, 70 samples of each fault type described by RS-DFV were randomly selected to train the PNN, and the rest of the fault samples were used to verify the diagnostic accuracy and effectiveness. And the diagnostic accuracy, training time and test time of each experiment were recorded. This fault diagnosis test was repeated 100 times with the same data under the same condition, and the average recognition accuracy, training time and test time are calculated.
# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 4 Experiments
### Datasets and Experimental Setup  
To evaluate the overall denoising performance of the proposed denoising model comprehensively, we compared our proposed model to ten other models. Some of these competitive methods are used as benchmark methods in the field of image denoising and show good denoising results, such as BM3D [12], weighted nuclear norm minimization (WNNM) [27], NCSR [14], FFDNet [20], DnCNN [17] and VDNet [22]. Specifically, BM3D is a non-locally collaborative filtering method, which performs well in repeated textures. On the other hand, WNNM is a low-rank-based method, and NCSR is a sparse coding scheme. They both give superior results in the homogeneous regions. FFDNet and DnCNN are both representatives of learning-based denoising methods. The learning-based denoising methods aim to learn a mapping between noisy images and denoised images with the assistance of external information, and they usually perform well in noisy images with similar features to the training data. We also selected VDNet because it demonstrates higher generalization capability and flexibility than the first two learning-based models for its explicit form of posterior expression. There is also the combined denoising method CSNet and some state-of-the-art image combination methods that can be used for image denoising, such as SPD [32], unified unsupervised image fusion network (U2 Fusion) [37] and a fast multi-exposure image fusion method, namely MEF-Net [38]. We conducted denoising experiments on two benchmark datasets. The two benchmark datasets are as follows: (1) the common image set shown in Figure 5, which consists of 10 test images widely used in various studies, including images with a size of 256 \(\times\) 256 (Monarch, House,Cameraman and Peppers) and six images with a size of 512 \(\times\) 512 (Lena, Barbara, Boat, Hill, Couple and Man); (2) 50 randomly selected images from the Berkeley segmentation dataset (BSD) [39], for which 10 representative images are shown in Figure 6. The images in the BSD contain rich content and texture details, which are very suitable for testing the robustness against images of our denoising model. In addition, we visually evaluated the results to subjectively assess the effects of denoising. All models were run on the same hardware platform (i.e., Intel(R) Xeon(R) CPU E5-1603 v4 @ 2.80 GHz RAM 16 GB, NVIDIA GPU Quadro M4000) and software environment (i.e., Window10 operating system, Pytorch 1.1.0).