esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial-Temporal Patterns of Standardized Anomalies of GOSIF and VIs during the 2019 Drought  
As shown in Figure 9, the monthly variation trends of GOSIF, NDVI, EVI, and NIRv were similar and gradually decreased from July to December; however, on the temporal scale, the differences between the monthly average of 2019 and that of 2011-2020 averages for SIF and each vegetation index are not significant. This phenomenon may be due to the averaging of high and low values across the region. Considering the complexity of regional drought, the comparison of monthly average changes between 2019 and 2011-2020 from July to December still cannot reflect the intensity of the drought occurrence in 2019. Thus, it is necessary to explore the standardized spatial anomalies of SIF and vegetation indices.  
Figure 8: Spatial distributions of the standardized anomalies of PPT, SM\({}_{\text{surf}}\), SM\({}_{\text{root}}\), SPEI, Tem, LST, and PAR from July to December 2019.  
To further reveal the impact of drought on vegetation in 2019, the spatial distributions of the standardized anomalies of GOSIF, NDVI, EVI, and NIRv from July to December 2019 are displayed in Figure 10. In July, the negative anomaly of GOSIF was mainly distributed in the southern part of the MLRYR; however, the corresponding PPT, SPEI, SM\({}_{\text{surf}}\), and SM\({}_{\text{root}}\) were positive. In addition, the negative anomalies of NDVI, EVI, and NIRv exhibited relatively sparse distribution, especially the NDVI. In August, the negative anomaly of GOSIF was mainly distributed in Hubei province. Unlike GOSIF, the negative anomaly region of NDVI in Jiangxi province was apparent.  
Moreover, the negative anomaly areas of EVI and NIRv were enlarged to most of Hunan province, northern Fujian Province, central and southern Zhejiang, and Anhui province. The spatial negative anomaly distribution of GOSIF, NDVI, EVI, and NIRv is generally consistent. In September, the hostile anomaly areas of GOSIF, NDVI, EVI, and NIRv were mainly distributed in Hubei, Hunan, and Jiangxi provinces. In October, the negative anomaly areas between GOSIF and NDVI and EVI and NIRv were consistent; however, in November, the negative anomaly area of GOSIF had been reduced, which was mainly distributed in the central and northeastern regions of MLRYR. Unlike GOSIF, the negative anomalies of NDVI, EVI, and NIRv were still severe; the distribution characteristics were similar to those of GOSIF. In December, the negative anomaly area of GOSIF was further reduced, while NDVI, EVI, and NIRv decreased slowly and were mainly distributed in central and southern Anhui province, most parts of Jiangxi province, western Zhejiang and Fujian provinces, and the eastern parts of Hubei province.  
Figure 9: Variations of GOSIF (**a**), NDVI (**b**), EVI (**c**), and NIRv (**d**) from July to December 2019. Note: The black curves denote the multi-year monthly averages of these variables during 2011–2020; the red curves denote the monthly average of these variables in 2019; the grey shaded areas denote the \(\pm 1\) standard deviation of the multi-year monthly averages.
## 3 Methodology
### Image Generation
#### 3.3.2 The Architecture of the OCT Discriminator  
As shown in Figure 12, the proposed system uses the Markov Discriminator [26] as the discriminator. The difference from the CNN classifier is that the output of the last layer is a convolutional layer instead of a fully connected layer, as shown in Figure 12. The final output of the model is a 16 \(\times\) 16 matrix. Each output in the matrix can be expressed as a receptive field of view in the original image, and each receptive field can represent an area in the original image, as shown in Figure 13.  
The reason for using the convolution operation in the last layer is to allow the feature matrix produced by the discriminator to be traced back to a specific region of the original image.  
In order to improve the performance of the discriminator, the mean-square error (MSE) is used as the loss function for our Markovian Discriminator. The MSE can measure the average between the predicted value and the real value; the smaller the value, the higher the similarity.  
Suppose the label of the real image is \(z\), and the label of the generated image is \(\overline{z}\). In order to measure the data distribution output by the real image and the generated image through the discriminator, we denote the Euclidean distance between the score obtained by feeding the real image \(x\) to the discriminator and the real image label \(z\) as \(D_{real}\), defined as Equation (6). We also denote the Euclidean distance between the score obtained by feeding the generated image \(y\) to the discriminator and the generated image label \(\overline{z}\) as \(D_{fake}\), defined as Equation (7).  
\[D_{real}=\frac{1}{\mu m}\sum\
olimits_{i=1}^{n}\sum\
olimits_{j=1}^{m}\left(z ^{ij}-x^{ij}\right)^{2}, \tag{6}\]\[D_{fake}=\frac{1}{mn}{\sum_{i=1}^{n}{\sum_{j=1}^{m}{\left({\bar{z}}^{ij}-y^{ij} \right)}^{2}}}, \tag{7}\]  
where \(m\) and \(n\) are the size of the image length and width.  
This method allows the data distribution in the generated images to be closer to real OCT images. The loss of discriminator is defined as:  
\[Discriminator\ Loss=\left({D_{real}+D_{fake}}\right)\times 0.5, \tag{8}\]
## 4 Approximate Optimization Process  
An outline of the optimization process using the approximation method is given as follows:  
1. Establish the initial FE model and perform a detailed mode analysis to get \(\varphi_{local}^{0}\) and \(\omega_{local}^{0}\);
2. Extract the nodal equilibrium forces of local vibration mode from mode analysis results, \(F_{local}=K\big{(}X^{0}\big{)}\varphi_{local}^{0}\);
3. Establish a static load condition according to above-obtained nodal equilibrium forces and perform a static analysis;
4. Extract the maximum nodal displacement from static analysis results;
5. Create a high-quality approximation using the aforementioned information and then perform the optimization with constraint, \(g^{*}(X)=u_{local}(X)-\frac{1}{p^{2}}\varphi_{local}^{0}\);
6. Update the analysis model and perform a complete analysis. Check for convergence to an optimum. If satisfied, terminate. Otherwise, repeat from step 2.  
Repeat steps 2-4 until the convergence requirements are satisfied. Steps 2-4 constitute one iteration. The flow chart shown in Figure 1 illustrates the proposed approximate method.  
We need to mention at this point that in order to obtain a high-quality optimization, artificial modeling is required. Due to part of the needed date, the orders of the local mode, for instance, must be manually selected. How to obtain the nodal equilibrium forces of local mode and establish static load condition in FE model are two difficult and important issues in artificially forming the approximation. Here, we accomplish both operations with the help of Msc. [PERSON]/[PERSON]. In the software, the Grid Point Force Balance is optional in the output request of mode analysis subcase, which is the virtual equivalent load in Equation (9). With the Grid Point Force Balance information, we can establish static load with fields table node by node.
## 3 Results
### Application of 3D-Printed Models in Pre-Operative Planning of Revision Total Knee Arthroplasty  
The goals of revision TKA are the same as for primary TKA, but there are often more challenges to face. In some cases, it may be difficult to obtain good stability, treat bone loss, achieve good fixation and restore knee kinematic at the same time. An accurate pre-operative planning is mandatory in revision TKA to correctly address all these aspects. The first step in pre-operative planning for revision TKA is to make a diagnosis. Firstly, it is mandatory to rule out infection [47]. Recently, the new International Consensus Meeting (ICM) of Philadelphia has been published, with a new algorithm for peripro prosthetic Joint Infection (PJI) diagnosis [48]. A patient's history and careful and complete lower limb clinical evaluation are mandatory to identify the cause of failure [47]. A complete X-ray evaluation should be obtained, including long-leg weight bearing view, AP and LL weight bearing view and patellar view [47]. X-rays can provide a lot of information, including implant position, bone loss, implant alignment, patellar height and morphology, presence of cement and implant size. Particularly, bone loss should be accurately evaluated pre-operatively, and, in some cases, a Computer Tomography (CT) can be useful to better evaluate bone loss amount and location and to determine how to treat it, even if it can be difficult to visualize due to metal artifacts [49; 50].  
However, not only the amount of bone loss, but also the quality of the remaining bone should be evaluated to predict which treatment choice should best achieve a good fixation in at least two out of three zones as described by [PERSON] (epiphysis, metaphysis and diaphysis) [51], as well as bone filling [52].  
Standard pre-operative planning still has and important role in evaluating implant size, need for stems, augments, cones or sleeve and correct component positioning. Digital planning may help in terms of accuracy and to better understand the relationship between the different revision implant components. Table 2 summarizes key points for pre-operative planning in revision TKA.  
Figure 5: Post-operative X-ray. (**A**) Antero-posterior view; (**B**) lateral view.  
However, 3D-printed models, obtained from CT scans, can be useful in the pre-operative planning, particularly in complex revision cases, in which bone loss can be a huge issue. The usefulness of 3D-printed models in pre-operative planning was previously published only in revision THA, while there are no studies regarding revision TKA [53].  
The following clinical case will show how 3D knee models can be used in pre-operative setting of complex revision TKA.
## 4 Reconstruction of Wind Pressure Field on Roof Surface
### Energy Ratio Analysis  
In order to analyze and show the influence of the mean value of wind pressure on the results in the reconstruction analysis of the wind pressure field on the roof surface using the POD method, based on the mean wind pressure (including the mean value of wind pressure on the roof surface) and the fluctuating wind pressure (excluding the mean value of wind pressure on the roof surface), the correlation matrix of wind pressure coefficients in each zone was analyzed through POD method. From the perspective of energy ratio of Equation (11), the cumulative energy ratio of each eigenmode is shown in Figure 6.  
It can be seen from the figure that the modal energy ratio of the reconstruction based on the fluctuating wind pressure in each zone is smaller than that of the reconstruction based on the mean wind pressure in the first order, but the former quickly approaches 1 and is close to the full energy. However, in the wind field reconstruction based on the mean wind pressure, the convergence rate of the cumulative modal energy to the true energy is much slower. In addition, the convergence rate of the windward folded plate (zone C), the leeward folded plate (zone F), and the roof flat plate (zone K) is relatively fast, and the rate of the side folded plate (zone E) is relatively slow. Despite the above phenomena, the energy ratios of the first few orders in each zone in the two reconstruction results are very large. The cumulative energy ratios of the first five orders can basically reach more than 90%, and that of the first 20 orders can reach more than 95%, which can realize the restoration of the real wind pressure field energy with high precision.
## 6 Conclusions and Future Directions
### Frequency Reference and Precision Time-Keeping  
There is a growing demand for constructing clocks with improved precision. Recent advances in optical clocks have yielded frequency stabilities exceeding 1 part in \(10^{15}\), surpassing the precision of Cs atomic clocks [66; 67; 68]. Optical clocks utilize ultra-stable lasers that are locked to very narrow atomic transition lines. With a CW molecular modulator, it may be possible to extend the frequency stability of an optical clock to the full optical spectrum. As previously mentioned, a key advantage is that since the molecules are prepared with two CW lasers, the linewidth of the molecular oscillations can be made very narrow. The frequencies of the two driving laser beams, \(\
u_{pump}\) and \(\
u_{Stokes}\), can in principle be locked to optical reference lines (such as to two teeth of a stabilized, frequency-comb Ti:sapphire laser). As a result, the absolute frequency of the molecular oscillations, \(\
u_{M}=\
u_{pump}-\
u_{Stokes}\), would be known to a very high precision (potentially approaching a precision at the level of 1 part in \(10^{15}\)). The idea then would be to use a frequency stabilized mixing beam so that all mixing orders with frequencies \(\
u_{0}+q\
u_{M}\) have an absolute frequency stability on the 1 part in \(10^{15}\) level. As discussed above, we could also use a Ti:sapphire laser as a mixing beam. For this case, if the Ti:sapphire laser is absolute frequency stabilized (a frequency comb), the generated spectrum of Figure 6 will be an absolute frequency reference covering the full optical region. We believe this would constitute a light source with many exciting applications in a number of research areas. For example, one can perform precision spectroscopy of a large number of atomic and molecular species from a single source. One would also be able to construct optical clocks in regions of the spectrum that are currently inaccessible.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 5 Test and Results
### Test Results  
After the test is finished, we calculated average value and the 95% confidence interval based on the listeners' scores. The average confidence interval of each test codec is [77.2, 87.0], [74.4, 84.2], [70.5, 80.7], [65.8, 76.6], [56.1, 66.9], and [78.6, 86.2]. After removing three outlier data (data beyond confidence interval), the test results of MUSHRA are shown in Figures 18 and 19.  
Compared with traditional parametric stereo, the PCA-based parametric stereo has less bitrate, higher quality, and better spatial sense. Compared with G719 with traditional parametric stereo with the same bitrate, PCA-based codec signal has better quality. Compared with HE-AACv2 signal, the average score of the PCA-based parametric stereo is slightly less than HE-AACv2. HE-AACv2 is a mature codec that uses several techniques to improve the quality, including Quadrature Mirror Filter (QMF), Spectral Band Replication (SBR), noise shaping and so on. The complexity of PCA is less than the part of the 32-band QMF in HE-AACv2. Considering the high complexity and maturity of HE-AACv2, the test results are optimistic. Conclusions can be drawn that the PCA-based codec method possesses good performance, especially for stereo signal in which the audio quality and spatial sense can be recovered well.
###### Abstract  
Vehicular networks are becoming increasingly dense due to expanding wireless services and platooning has been regarded as a promising technology to improve road capacity and on-road safety. Constrained by limited resources, not all communication links in platoons can be allocated to the resources without suffering interference. To guarantee the quality of service, it is required to determine the set of served services at which the scale of demand exceeds the capability of the network. To increase the number of guaranteed services, the resource allocation has to be adjusted to adapt to the dynamic environment of the vehicular network. However, resource re-allocation results in additional costs, including signal overhead and latency. To increase the number of guaranteed services at a low-cost in a resource-limited vehicular network, we propose a time dynamic optimization method that constrains the network re-allocation rate. To decrease the computational complexity, the time dynamic optimization problem is converted into a deterministic optimization problem using the Lyapunov optimization theory. The simulation indicates that the analytical results do approximate the reality, and that the proposed scheme results in a higher number of guaranteed services as compared to the results of a similar algorithm.  
resource-limited; re-allocation; service guaranteed +
Footnote †: journal: Sensors
## 2 Related Background
### Peak Signal-to-Noise Ratio  
The peak signal-to-noise ratio (PSNR) measures the similarity of two signals--typically a reference signal and a processed version of it--which defines the relation between the maximum energy of a signal and the noise affecting it, expressing this ratio in decibels [24; 25]. Given a 16-bit audio clip \(f\) and a copy of the processed audio \(g\), both of size \(N\), the PSNR between \(f\) and \(g\) is computed by  
\[\text{PSNR(f, g)}=10\cdot\log_{10}\left(\frac{65,535^{2}}{MSE(f,g)}\right), \tag{7}\]  
\[MSE(f,g)=\frac{1}{N}\sum_{i=1}^{N}(f_{i}-g_{i})^{2}. \tag{8}\]  
As for the mean square error (MSE), the difference between the samples \(f_{i}\) and \(g_{i}\) is considered to be an error that generates signal quality loss. The lower the MSE, the higher the PSNR; therefore, the higher the PSNR (f, g) values, the higher the signal quality. For audio and image signals, a PSNR greater than 35 dB is typically considered of good quality.
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 3 Datasets and Experimental Conditions  
### Datasets  
The main difficulties in the detection of rebar on a construction site are as follows: the background of the rebar is complex, and there is a possibility that the background color is similar to that of the rebar. Furthermore, pipes, pads, and other tools from utilities, as well as shadows from light, are present with the rebar at construction site. As there is no public rebar dataset, we used smartphones to capture a large number of rebar images at the construction site. To prevent training overfitting and to improve the accuracy of the model, a data enhancement approach was adopted to expand the dataset by processing the images using flipping, exposure and perspective change, and motion blurring to enrich the diversity of the training sample. The final dataset contained 3130 images in total, which was divided into three groups at a ratio of 8:1:1, i.e., 2504 training images, 313 validation images, and 313 test images.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Data Preprocessing and Standardization  
There are integral steps aimed at enhancing the reliability and interpretability of analyses. This involves standardizing units, ensuring consistency in labeling, and addressing  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Vitals**}} & \multicolumn{2}{c}{**Non-AAS (n = 148,578)**} & \multicolumn{2}{c}{**AAS (n = 129)**} \\ \cline{2-5}  & **Count** & **Percentage** & **Count** & **Percentage** \\ \hline \multicolumn{5}{l}{**Systolic-BP (mmHg)**} \\
0–30 & 283 & 0.19\% & 0 & 0\% \\
30–60 & 1471 & 0.99\% & 3 & 2.33\% \\
60–90 & 3884 & 2.61\% & 2 & 1.55\% \\
90–120 & 22,176 & 14.93\% & 32 & 24.81\% \\
120–150 & 22,618 & 15.22\% & 10 & 7.75\% \\
150–180 & 4941 & 3.33\% & 5 & 3.88\% \\
180–240 & 571 & 0.38\% & 4 & 3.10\% \\ \(>\)240 & 4 & 0.003\% & 0 & 0\% \\ Missing data & 92,881 & 63\% & 73 & 56.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Systolic-BP analysis.  
missing data. The standardized datasets enable meaningful comparisons and correlations across various hospitals and patient populations. The implementation of preprocessing before integrating separate datasets entails cleaning, organizing, and standardizing the individual datasets. This stage is crucial to ensure that the data are in a suitable and uniform format for seamless integration. Common preprocessing tasks include handling missing values, addressing outliers, standardizing units or scales, and resolving inconsistencies in data formats. Through these preprocessing steps, the quality, reliability, and compatibility of the individual datasets are enhanced, laying the foundation for a more accurate and coherent analysis upon integration.
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 4 Migration Management of VM over SDVC
### Review of the State-of-the-Art of VM Migration Techniques over SDVC
Autonomous vehicular networks provide significant cooperative services for Connected and Autonomous Vehicles (CAVs) [51]. The regular continuation of computing and storing resources services at the edge of the core network enable NFV and SDN to provide different applications with provisioning cost. Softwarization at the edge of the network improves the intelligent traffic steering, management of multiple computing resources, and reliable access to the network through different wireless access technologies. Service migration strategies based on SDN technology enhance delivery of cloud-based services to mobile users and vehicular users as well. [PERSON] et al. addresses user mobility and its related challenges by designing a migration strategy called Follow-Me Cloud [49]. The Follow-Me Cloud prototype offers an optimized user experience because the migration of current service is triggered by the Follow-Me controller. The latter uses the user current location and movement direction. During the user movements, the ongoing service hosted on the current MEC server is migrated once an optimal decisions predicted by the Follow-Me controller is delivered to the mobile operators. The software-defined networking and locator-identifier separation protocol-based improve mobile cloud-based service. The Follow-Me concept introduces optimized migration models which might be implemented as applications modules to support the SDN controller to select algorithm for service migration decisions. The Follow-Me concept handles the tradeoff between migration cost and user experience. Reconfiguration overhead related to VM migration was proposed by [PERSON] et al. The authors proposed a RSU cloud composed of traditional RSUs and micro-data centers [50]. The RSU cloud is designed to use SDN and can host RSU services to handle drivers requests. During congestion time that leads to inherent dynamic demands from the vehicles, RSU cloud is reconfigured to optimally meet the service demands. The optimization is based on the reinforcement learning algorithm to select a reconfiguration overhead that minimizes the VM migration cost.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 1 Introduction
### Organization  
The organization of this work is presented as follows. Section 2 reviews related works, and Section 3 formalizes our research problem. Section 4 provides background on indoor localization, secret sharing, and function secret sharing. In Section 5, we present \(\mathrm{Sillcom}_{3R}\), which requires only three rounds of online communication, followed by the improved scheme \(\mathrm{Sillcom}_{trd}\) in Section 6, which balances computational and communication overhead. Sections 7 and 8 offer a detailed analysis of our scheme from theoretical and experimental perspectives. Finally, we conclude the paper and discuss future work in Section 9.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 4 Discussion
### Future Research Directions
This is an important literature gap since SAR data is not limited by cloud cover, a crucial advantage for mapping coffee-growing regions. Finally, advances in cloud computing and deep learning algorithms can improve classification in complex landscapes [110]. Deep learning has been successfully used in recent studies to support analyses of image fusion, LCLUC, object detection, and scene recognition, among others [111]. By stressing these research opportunities, we hope researchers can focus on the specific urgent needs for coffee mapping to inform global investments in coffee production communities to improve livelihoods, support biodiversity conservation, reduce deforestation, and improve sustainable production.