esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Another method for selecting the effective singular values is based on the asymptotic relationship between the singular values and vectors of the signal matrix and observed matrix [26; 27], and the de-noised signal matrix is reconstructed through minimizing the asymptotic loss, whose performance is superior to the traditional shrink of singular values accomplished by hard and soft thresholding. Nevertheless, the asymptotic framework needs to satisfy some assumptions, such as the orthogonally invariant of the noise [28]. It is widely admitted that many kinds of noise and interference are contained in the actual environment, which makes it hard to meet the condition accurately. As a result, the method proposed by these two papers is restricted to dealing with the fault data concerning the strong noise in the industrial environment. Aiming at the problem narrated above, this paper presents a method that integrates the difference of curvature peaks with incremental singular entropy. Singular entropy is the measurement of the corresponding information contained in the singular value. For a pure signal, the information from the signal is mainly concentrated in the former singular value, and the value of incremental singular entropy is large. For the noise signal, the amount of information is distributed in various average singular values, and the value of the incremental singular entropy coincides with the improvement of the number of singular values. A turning point will appear in the spectrum of the incremental singular entropy. The change of difference at the two adjacent curvature peaks is most obvious at the turning point. On this basis, the position of the difference of the two adjacent curvature peaks declines at first by a large degree, the location where the state of the spectrum changes is ensured, and a singular value that contributes to the reconstruction of the de-noised hidden faulty signal is, to a large degree, retained. Meanwhile, the problem of excessive and incomplete noise reduction is potentially solved. In the course of de-noising and feature extraction based on SVD, the de-noised signal recovered by this method has less distortion compared with the original signal. In the experiment on the de-noising of real bearing fault data, the frequency signatures of the envelope spectrum were used to identify the characteristic frequency of faults. The structure of the article is as follows. First, the basic principles of noise reduction based on SVD and the proposed method are described in Section 2. Section 3 describes how two simulated signals were used to obtain statistics for the de-noised signal-to-noise ratio (SNR) of the four methods. An analysis of the superiority of the method for the treatment of singular values is presented. Bearing failure data from real environments were inputted into the experiment to compare the de-noising effects of the four methods; the results of this experiment are presented in Section 4. Conclusions are presented in Section 5.
## 2 Motivations  
As mentioned before, in the coming years, it will be necessary to eliminate the barriers of geography, making healthcare accessible to individuals in rural or underserved areas. For this purpose, telemedicine allows patients to connect with healthcare providers remotely, receive a diagnosis and treatment, and access specialist care without the need to travel long distances.  
The HealthTracker system can significantly improve the usage of telemedicine in the everyday life of both patients and clinicians to improve people's health for better health treatments. For example, the patient's health data can be automatically collected through the patient's smartphone using the HealthTracker app, saved on its cloud storage system, and subsequently processed to implement the following functionalities:  
1. provide the medical team with an aggregate view (consisting of graphs and tables) for a better and immediate consultation;
2. allow the medical team to customize monitored data according to patients' specific medical conditions. For instance, physicians can selectively focus on tracking the movement patterns of a patient facing challenges in walking or exclusively monitor the heartbeat rate of an individual with heart-related issues. This functionality is provided by HealthTracker's app, which enables physicians to select only those data that require monitoring;
3. process these data with machine-learning algorithms (already available in Healthcare) to perform statistical analysis, both to provide information on the current situation and to predict the trend of health parameters in the short term;
4. capture real-time biomedical parameters to identify abnormal signs, such as elevated body temperature and inadequate oxygenation, to preemptively alert the user and mitigate the risk of developing certain conditions by advising the patient to sit or lie down or relocate to a cooler environment. Furthermore, a notification system to alert family members and selected medical personnel could be implemented, ensuring swift responses and personalized care in critical situations;
5. establish and sustain the foundational core of a knowledge base, with the potential for expansion to incorporate additional parameters characterizing the patient, such as the presence and severity of predisposing or aggravating conditions. This serves as the backbone for conducting statistical analyses leveraging advanced machine-learning techniques.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
###### Abstract  
Improving plant photosynthesis provides the best possibility for increasing crop yield potential, which is considered a crucial effort for global food security. Chlorophyll fluorescence is an important indicator for the study of plant photosynthesis. Previous studies have intensively examined the use of spectrometer, airborne, and spaceborne spectral data to retrieve solar induced fluorescence (SIF) for estimating gross primary productivity and carbon fixation. None of the methods, however, had a spatial resolution and a scanning throughput suitable for applications at the canopy and sub-canopy levels, thereby limiting photosynthesis analysis for breeding programs and genetics/genomics studies. The goal of this study was to develop a hyperspectral imaging approach to characterize plant photosynthesis at the canopy level. An experimental field was planted with two cotton cultivars that received two different treatments (control and herbicide treated), with each cultivar-treatment combination having eight replicate 10 m plots. A ground mobile sensing system (GPhenoVision) was configured with a hyperspectral module consisting of a spectrometer and a hyperspectral camera that covered the spectral range from 400 to 1000 nm with a spectral sampling resolution of 2 nm. The system acquired downwelling irradiance spectra from the spectrometer and reflected radiance spectral images from the hyperspectral camera. On the day after 24 h of the DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) application, the system was used to conduct six data collection trials in the experiment field from 08:00 to 18:00 with an interval of two hours. A data processing pipeline was developed to measure SIF using the irradiance and radiance spectral data. Diurnal SIF measurements were used to estimate the effective quantum yield and electron transport rate, deriving rapid light curves (RLCs) to characterize photosynthetic efficiency at the group and plot levels. Experimental results showed that the effective quantum yields estimated by the developed method highly correlated with those measured by a pulse amplitude modulation (PAM) fluorometer. In addition, RLC characteristics calculated using the developed method showed similar statistical trends with those derived using the PAM data. Both the RLC and PAM data agreed with destructive growth analyses. This suggests that the developed method can be used as an effective tool for future breeding programs and genetics/genomics studies to characterize plant photosynthesis at the canopy level.  
**Keywords:** solar induced fluorescence; cotton; maximal fluorescence estimation; growth analysis
## 4 Results and Discussion
### Comparative Summary and Suitability
Regarding hybrid technologies and storage, for rooftop PV, the main commercial storage options are Li-ion batteries for day-night shifting and small PEM electrolyzer that turn surplus power into green H\({}_{2}\) (65% electrolyzer efficiency, 50% fuel-cell round-trip [51]. For solar-thermal, the lowest cost is an 8 h two-tank molten salt loop [52]. Upgraded biogas fired in a 20 kWe micro turbine can cover PV shortfalls; trials in India and the UAE keep grid quality with \(<\)10% rise in LCOE [53]. Ongoing PEM advances, higher temperature operation, and dynamic start-up are pushing efficiencies upward [54], underscoring the promise of PV-hydrogen and PV-biogas hybrids for reliable low-carbon power.
## 5 Results and Discussion
### Structural Optimization Design
#### 5.2.2 Worktable Optimization Results  
The topology optimization analysis in ANSYS Workbench is another option for increasing the rigidity of the worktable. The worktable is examined for modal frequencies and stress-strain contours. Topology optimization reduces the machine's weight without affecting the design characteristics. As a preliminary precaution, the analysis is set to restrict 85% of the actual mass in order to avoid changing too much of the design. The analysis shows the area that is unaffected by stress caused by the weight of the parts lying on it. An engineering design can be used to clear the marked area; the modified design is reanalyzed, and the results are compared. For topology-shape optimization, the ANSYS FEA package was used; it determines the reduced mass and deformation under a specific shape. Now, the new shape, as approved by the FEA package, has some material removed from the places which would not only partake in major stress bearing but also load bearing. It was effective to remove this excess mass, as we can still get a shape optimized enough to have less stress, as well as less deformation. Table 12 shows the optimized result of the worktable. For the static characteristics of the worktable after optimization, the mass before and after optimization was 341 and 326 kg (4.3% reduction), respectively. For a load of 1000 N, the maximum deformation before and after optimization was 6.77 \(\upmu\
# Sediment Dynamics of the Neretva Channel (Croatia Coast) Inferred by Chemical and Physical Proxies
## 5 Discussions
### Regional Water Circulation and Water-Column Physical Properties  
Surficial currents along the eastern Adriatic in the proximity of the NC are always oriented from SE to NW following the overall cyclonic circulation of the basin [50]. Local data on annual mean values of water circulation within the NC are not available in the literature, except for a little punctual information. Given that the NC represents a semi-closed marine environment, it is reasonable to assume that channel hydrodynamics are the result of a balance between tidal currents and fluvial influence, and seasonal variations in precipitation may act as a driving factor for the regional water exchange with the Adriatic open sea.  
Generally, river currents do not exceed the velocity of 0.1 m/s and are higher during the winter season [51; 52]. In particular, in 2004, daily outflows (m\({}^{3}\)/s) during winter and spring periods ranged from 300 to 400 m\({}^{3}\)/s, with short time intervals of peak flow (\(>\)500 m\({}^{3}\)/s) or reduced flow (\(<\) 200 m\({}^{3}\)/s). However, during the summer season, the river outflow is drastically reduced (up to 50 m\({}^{3}\)/s), as reported by UNEP/MAP [53]. In addition, horizontal distribution of freshwater into the channel may depend on the direction of local winds and may vary on a daily basis [54].  
Based on CTD data, sediment grain size and SAR surficial distributions, we suggest the presence of an eddy in the NC waters located at the center of the channel that may strongly influence sediment dispersion and determine areas of localized preferential sediment accumulation not necessarily in correspondence with the river mouth (e.g., 10 BC, Figure 4). Moreover, a considerable amount of freshwater is discharged into the channel by several submarine karstic springs [55], suggesting high inflow of cold water that lacks sediment load during periods of intense rainfall.  
The observed water stratification along the northern transect may represent a seasonal snapshot of the hydrologic conditions of the study area. Mixed-water conditions may dominate through the entire NC area during flooding events in autumn. However, for the objectives of this study, spring conditions may be considered the optimal hydrological situation. Close to the river mouth, less salty and colder water was detected. This could be due to a reduction of the salt-wedge during low tide conditions and/or intense river outflow, or the karstic characteristics of the region. Karstic fresh waters  
Figure 6: Apparent sediment accumulation rates (SAR) over the study area.  
have generally poor or null suspended material contents, explaining why these inflows do not affect organic matter or metal distributions.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 3 Design of EKF State Observer
\right] \tag{29}\]  
The nonlinear state space Equation (24) is discretized by the Euler approximation method, and the following expression can be obtained. \[\left\{ \begin{array}{l}x_{k}=f(x_{k-1},u_{k-1})+\omega_{k-1}\\ y_{k}=h(x_{k})+v_{k}\end{array} \right. \tag{30}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \(B_{f}\) & \(C_{f}\) & \(D_{f}\) & \(E_{f}\) & \(B_{r}\) & \(C_{r}\) & \(D_{r}\) & \(E_{r}\) \\ \hline
0.12 & 1.6 & \(2.35\times 10^{4}\) & \(-0.3028\) & 0.012 & 1.6 & 12,710 & \(-0.3028\) \\ \hline \end{tabular}
\end{table} 
Table 2: The parameters used in the magic formula (MF) model. The recursive algorithm of EKF is described as follows:  
1. Time update The state estimate time update \[\hat{x}_{k}^{-}=f(\hat{x}_{k-1},u_{k-1})\] (31) The error covariance time update \[P_{k}^{-}=A_{k}P_{k-1}{A_{k}}^{T}+\text{Q}\] (32) where \(A_{k}\) is the process Jacobin matrix, which is the partial derivative of \(f(\hat{x}_{k-1},u_{k-1})\) to the state vector \(x\), and it can be expressed as \[A_{k}=\frac{\partial f(x_{k-1},u_{k-1})}{\partial x}\] (33)
2. Measurement update \[K_{k}=\frac{{P_{k}}^{-}{H_{k}}^{T}}{{H_{k}}{P_{k}}^{-}{H_{k}}^{T}+R}\] (34) \[\hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}\big{[}y_{k}-h\big{(}\hat{x}_{k}^{-}\big{)} \big{]}\] (35) \[P_{k}={P_{k}}^{-}-K_{k}{H_{k}}{P_{k}}^{-}\] (36)  
where \(H_{k}\) is the measurement Jacobin matrix, which is the partial derivative of \(h(x_{k})\) to the state vector \(x\), and it can be expressed as  
\[H_{k}=\frac{\partial h(x_{k})}{\partial x} \tag{37}\]
## 4 Application Scenarios of the Multipath Error Mitigation Algorithm
### GNSS Deformation Monitoring
The sidereal filtering method based on the single-difference procedure has the best effect on weakening the multipath error of high-speed railway foundation deformation monitoring. [PERSON] et al. [108] proposed an empirical wavelet transform independent component analysis reference (EWT-ICA-R) method, which can accurately extract deformation information from GNSS coordinate sequences. The experimental results showed that the extraction of deformation information was expected to achieve submillimeter precision. The multipath error exhibits a strong correlation with the receiver environment of the station. When monitoring the deformation of the structure, the buildings, water environment, and tree vegetation around the station cause multipath effects. The resultant multipath error, not amenable to elimination through the double-difference method, emerges as the primary source of error. This limitation constrains the broader adoption and application of GNSS positioning technology in the deformation monitoring of structures and geographical environments [109]. The exploration of the multipath error mitigation algorithm has great application value and research significance for high-accuracy deformation monitoring.
## 4 Simulation and Analysis of Transmission Lines Foreign Object Detection
### Simulation Results and Analysis
#### 4.2.4 Comparison and Analysis of Different Feature Fusion Schemes  
In order to obtain the optimal feature fusion scheme and verify the effectiveness of multi-network feature fusion in actual simulation, the networks were sorted according to the detection performance obtained by experiments in Section 4.2.3 and named them as N1 to N5, namely GoogLeNet, EfficientNet-B0, DenseNet-201, ResNet-101 and AlexNet. Then, the features extracted from different networks were concatenated separately and then sent to train and test the random forest classifier. Finally, the optimal feature fusion scheme was determined for the transmission line foreign object detection based on the results under different fusion schemes, as shown in Table 6.  
From the results of the above fusion schemes, it can be concluded that the accuracy can obtain the optimal 95.88% by fusing the features of GoogLeNet, EfficientNet-B0, DenseNet-201. The reason why the other schemes are not superior to the feature fusion performance of the above three networks is that the features extracted from the other two networks do not contain enough information or redundant features resulting in lower accuracy after fusion. Therefore, the concatenation fusion of features extracted from GoogLeNet, Efficientnet-B0 and DenSenet-201 can further improve the detection accuracy of foreign objects on transmission lines.
# An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation
## 1 Introduction
This optimization helps improve the accuracy of the human pose-estimation model in predicting the keypoints of the human body, especially the keypoints of the occluded parts. Based on the human posture estimation model named SEPAM_HRNet, which was formed based on the application of the SEPAM module to the HRNet network, we created a human yoga movement posture feature dataset named YOGA2022. This dataset was constructed with labels for ten common human yoga movement postures. It provides the location coordinates of, as well as visibility information about, 17 human keypoints that are consistent with the label form of the COCO dataset. With this new dataset, a deeper understanding of the postural characteristics of human yoga poses can be gained, and the accuracy and stability of the SEPAM_HRNet model in human keypoint prediction can be further validated and optimized. Meanwhile, the establishment of this dataset provides a valuable resource and foundation for future research, which is expected to lead be more in-depth and to have applications in the field of human-motion analysis and posture estimation. Overall, the contributions of the work in this paper are as follows:  
1. Considering the specificity of yoga movement postures that are prone to self-obscuring phenomenon, this paper creates a new dataset of human yoga movement postures, with truth labels, called YOGA2022. 2. In order to solve the problem of self-obscuring keypoint prediction, which requires finer multi-scale spatial information, this paper proposes the SEPAM module and fuses it into the HRNet model to establish the new SEPAM_HRNet model. The SEPAM_HRNet model improves prediction accuracy without introducing a large number of parameters or complexity. Through this innovative fusion, the self-obscuration problem can be better dealt with. 3. After extensive comparative experiments, the experimental results fully validated the effectiveness of the lightweight channel spatial pyramid attention (SEPAM) module.
## 1 Introduction
For this type of SESAMs, the InAs absorber layer was sandwiched between an Au-coated mirror and a GaAs wafer. In general, metal-reflector-based SESAMs have a relatively high non-saturable loss of \(>\)5% induced by the included buffer layer, substrate and Au bottom mirror, and hence may limit the performance of 3-\(\upmu\)m solid-state lasers [27; 28]. Q-switched operation of Er:Lu\({}_{2}\)O\({}_{3}\) ceramic laser using metal-reflector-based absorber has very recently been demonstrated, generating 60 mW of average output power with 660 ns of pulse duration [29]. In this paper, we report on the demonstration of passively Q-switched operation of 2.7-\(\upmu\)m Er:Lu\({}_{2}\)O\({}_{3}\) ceramic laser with improved performance using a Bragg-reflector-based SESAM of modified band-gap and reduced non-saturated loss. With a thermal-stable resonator design, stable pulse trains of 70 ns pulse width and \(\sim\)71 kHz repetition rate were generated. An average output power of 692 mW was measured, corresponding to a pulse energy of 9.8 \(\upmu\)J and a peak power of 0.14 kW. Using a modified resonator design of reduced round-trip time, pulses of 32 ns duration were generated. Results presented in this paper suggest that Er:Lu\({}_{2}\)O\({}_{3}\) ceramic could be a promising gain medium for high power and efficient nanosecond pulse generation in a 2.7-\(\upmu\)m wavelength regime.
# Theory of Edge Effects and Conductance for Applications in Graphene-Based Nanoantennas
## 2 Optical Conductance of a Terminated Graphene Sheet
### Zigzag Edges
Since the expression for the ac-conductivity with a zigzag edge is isotropic, we have \(K_{ab}(\omega;\mathbf{x},\mathbf{x}^{\prime})=\delta_{ab}K(\omega;\mathbf{x},\mathbf{x}^{\prime})\), where the general susceptibility implies that \(K(\omega;\mathbf{x},\mathbf{x}^{\prime})=-i\omega^{-1}(\Pi(\omega)-\Pi(0))\), with  
\[\Pi(\omega)=\sum_{s}\sum_{s^{\prime}}\frac{\left(\hat{\mathbf{j}}(\mathbf{x}) \right)_{ss^{\prime}}\left(\hat{\mathbf{j}}(\mathbf{x}^{\prime})\right)_{s^{ \prime}s}}{\left(\hbar\omega-(\varepsilon_{s}-\varepsilon_{s^{\prime}}) \right)}(f(\varepsilon_{s})-f(\varepsilon_{s^{\prime}})). \tag{15}\]  
where \(\left(\mathbf{j}(\mathbf{x})\right)_{ss^{\prime}}=2 ev_{F}\big{(}\Psi_{s^{ \prime}}^{*}(\mathbf{x},0)\sigma\Psi_{s}(\mathbf{x},0)\big{)}\) represents the matrix element of the current operator, \(\sigma\) denotes the \(xy\) vector of the Pauli matrices, \(\varepsilon_{s}=\pm\hbar v_{F}\sqrt{k_{xp}^{2}+k_{y}^{2}}\) is the energy of the \(s\)-th state, and \(\varepsilon,v_{F}\) correspond to the electron charge and Fermi velocity, respectively. The general susceptibility may be presented here as the sum of two components of different origin, namely \(K(\omega;\mathbf{x},\mathbf{x}^{\prime})=K^{Inter}(\omega;\mathbf{x},\mathbf{x }^{\prime})+K^{Intra}(\omega;\mathbf{x},\mathbf{x}^{\prime})\), where \(K^{Inter}(\omega;\mathbf{x},\mathbf{x}^{\prime})=-i\omega^{-1}\Pi(\omega)\) corresponds to the interband motion which may be omitted at rather low (\(\mathrm{THz}\)) frequencies. The second term \(K^{Intra}(\omega;\mathbf{x},\mathbf{x}^{\prime})=i\omega^{-1}\Pi(0)\) corresponds to the intraband motion and leads to the common conductivity law \(\mathbf{j}(\mathbf{x})=-i\sigma^{Intra}(x)\mathbf{E}(\mathbf{x})\), where the surface conductance is found by substituting the pseudo-spinor modes (14) into (15), results in  
\[\sigma^{Intra}(x)\approx-\frac{ie^{2}v_{F}^{2}}{2\pi(\hbar\omega+i0)}\int \limits_{-\infty}^{\infty}\sum_{n}\frac{\partial f(\varepsilon)}{\partial \varepsilon}\bigg{|}_{\varepsilon=\varepsilon_{s}=\varepsilon_{n}(k_{y})} \cdot\Big{(}\left|u_{s}(x)\right|^{2}+\left|v_{s}(x)\right|^{2}\Big{)}dk_{y} \tag{16}\]  
For details see Appendix A. The final explicit expression for the conductivity given in (16), is spatially inhomogeneous (\(x\)-dependent) and is a manifestation of edge effect and incorporating a self-consistent description.