esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Cooperative SiM-RPO
### Relay Selection and Data Holding  
When the MS requests for the data, it sends the request packet of a few bits. The nodes that hear the request packet deliver their data to the MS. However, all these nodes' data transmission creates a data burden and excessive data forwarding. To avoid these redundant transmissions, only one relay transmits its data to the MS. The node that receives the request first broadcasts its data without any latency and embeds its own ID with the data. As the relay broadcasts data, it removes these data from its memory. The other nodes also hear the relaying data. When the nodes receive data, then they check their data saved in the memory against the received data. If both data packets have the same packet number and sender ID, then other relays discard the data from memory to avoid redundant data transmission. Every data packet has its own number and sender ID in the header of the data.  
The nodes hold the data until they receive an acknowledgment or request from the MS. In the case of an acknowledgment reception, the nodes discard their data. In the case of request reception, the node that receives the request first broadcasts the data at that time and discards them, while the other nodes discard the data after reception of the relaying data. The node that does not hear the acknowledgment, request, or relaying data discards the data after some time.
###### Abstract  
We present a simple yet effective method, without the need for any additional recording of intensity maps or tremendous iterative computations, to recover reference-wave intensity resulting from the complex hologram acquired by quadrature phase-shifting holography. This is achieved by utilizing a certain area of interest in the complex hologram. We select a particular area in the complex hologram where there is negligible diffraction from the test sample to estimate the reference-wave intensity. The calculated intensity value allows us to extract exact phase distribution of the object in the context of two-step-only quadrature phase-shifting holography (TSO-PSH) without the zeroth-order beam and the twin image noise on the reconstruction plane. Computer simulation and experimental results have been performed to verify the effectiveness and feasibility of our proposed method.  
Keywords:phase-shifting holography; complex hologram; zeroth order noise; phase retrieval +
Footnote †: journal: Applied
# Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning
## 4 Loss Function  
The proposed detection algorithms involved in the loss function mainly includes three aspects: boundary box loss, loss of confidence, and classification loss. The confidence level and classification loss calculation formula is:  
\[L_{\text{conf}(o,c)}=-\frac{\sum_{i}{(o_{i}In(\hat{c_{i}})+(1-o_{i})In(1-\hat {c_{i}}))}}{N} \tag{6}\]  
\[L_{\text{cls}}(O,C)=-\frac{\sum_{i\in pos}\sum_{j\in cla}{(O_{ij}In(\hat{C_{ ij}})+(1-O_{ij})In(1-\hat{C_{ij}}))}}{N_{pos}} \tag{7}\]  
For the calculation of positioning loss, CIoU (Complete IoU) Loss is used in this paper instead of MSE [14]. The advantage of this kind of loss function is that it not only takes into account the influence of the overlapping area between the predicted boundary frames on the loss calculation but also takes into account the influence of the center point distance and aspect ratio. The expression of the function is as follows:  
\[CloULoss=IoU-(\frac{\rho^{2}(b,b^{st})}{c^{2}}+\alpha v) \tag{8}\]  
\[v=\frac{4}{\pi^{2}}(\arctan\frac{w^{st}}{h^{st}}-\arctan\frac{w}{h})^{2} \tag{9}\]  
\[\alpha=\frac{v}{(1-IoU)+v} \tag{10}\]  
In the formula, \(\rho(b,b^{st})\) is the center distance between the predicted boundary box and the real boundary box; \(c\) is the minimum diagonal distance between the smallest external frame between the two boundary boxes; \(\alpha\) and \(\
u\) are the balance coefficient and the aspect ratio coefficient, respectively; \(w^{st}\) is the width of the marked boundary box; \(w\) is the width of the predicted boundary box; \(h^{st}\) is the height of the marked boundary box; and \(h\) is the height of the predicted boundary box.
## 3 Methodology
### Double Pyramid Network (DPN)  
DPN module is designed to obtain more informative ROI features, solve the problem of diverse defect scales and ineffective detection by single-layer features. It is formed by the combination of RASPP and top-down and bottom-up feature pyramid networks (PA-Net). The module can expand the receptive field of the model and fuse feature information of different scales. It is essential for ROI feature capture.  
The novel DPN module designed in this paper is shown in Figure 4. The feature maps of C2, C3, C4, and C5 levels extracted by the backbone is the input of DPN module. Firstly, a \(1\times 1\) convolutional layer is employed to reduce the channel dimension of the feature maps extracted by the backbone to 256. This can reduce the amount of computation and facilitates the fusion of features at different levels. Secondly, the top-down path is used to transfer the high-level semantic information to the low-level features, and a \(3\times 3\) convolutional layer also is utilized to reduce confusion caused by feature fusion. Thirdly, the RASPP module is used to expand the receptive field and enrich the multi-scale information of the feature maps. Finally, the bottom-up path is used to transfer rich location information of the low-level feature to the high-level feature. The DPN algorithm is shown in Algorithm 1. It is worth noting that the RASPP module is applied in all layers of DPN for more efficient extraction of multi-scale feature information. However, some extra features are useless and  
Figure 3: The structure of the proposed residual atrous spatial pyramid pooling (RASPP) module. \(1\times 1\) Conv: Convolution with a kernel size of 1, \(3\times 3\) Conv rate = 3: Dilated convolution with a convolution kernel size of 3 and an expansion rate of 3, other similar.  
counterproductive. Therefore, the weights of the RASPP module are shared to reduce the parameters and complexity of the model. DPN enriches the multi-scale information of the features and expands the receptive field and improves the utilization of the rich location information in the low-level features by combining RASPP with top-down and bottom-up feature pyramids. In addition, the features of each layer have similar semantic features in DPN, which is beneficial for the model robustness to multi-scale defect detection.
## 3 Identification of the Optimal Operating Maps  
To carry out the analysis addressed in Section 2, it is sufficient to know only the constructive and functional layout of the PS-CVT. This allows the calculation of the PSU speed ratios as well as the dimensionless mechanical power that electric machines should provide or absorb, by considering the PSU friction losses. More specifically, in power-split operation, the dependent variables, which are normalized to ICE speed or power, can be assessed by freely assigning the speed ratio and the power ratio between the output and the input port (Figure 2). On the other hand, in FEV operation, the speed ratios are univocally defined, thus the dependent variables, which are normalized to the output power, can be determined by freely supposing the power ratio between one PSU port connected to an electric machine and the output port (Figure 3). In other words, for a given vehicle speed (directly related to \(\omega_{out}\)) and for a given demanded torque, it is necessary to assume a specific functioning point of the ICE (in PS operation) or of one electric machine (in FEV operation) to univocally determine speeds and powers (and thus torques) on each PSU port.  
Obviously, the output torque and the wheels' speed can be considered independently one from the other, but if the output torque is higher or lower than the driving resistance, the powertrain work in dynamic operations, and the inertia of the vehicle and the propulsors should be considered in the analysis. In this article, for computational simplicity, the steady-state operation is analyzed, whereby the output torque is a function of the vehicle speed (\(V_{veh}\)), thus the power delivered by the powertrain is:  
\[P_{out}=-\Big{(}mg\sin\alpha+f_{r}mg\cos\alpha+0.5\text{-}C_{d}A_{f}\rho_{a}V_{ veh}^{2}\Big{)}V_{veh} \tag{7}\]  
where \(m\), \(f_{r}\), \(C_{d}\), \(A_{f}\) are the Chevrolet Volt parameters reported in Table 4, \(\alpha\) is the road slope in radians, \(g=9.81\)\(m/s^{2}\) is the gravitational acceleration, and \(\rho_{a}=1.225\)\(kg/m^{3}\) is the air density. It is worth noting that \(m\) is the sum of the unladen vehicle mass \(m_{0}\) and the mass of passengers, fuel, or any other additional load. Therefore, \(m\) and \(\alpha\) depend on the driving conditions. Nonetheless, it is sufficient to modify these values upstream of the procedure described in this section so as to obtain data referred to any steady-state driving condition.  
Once fixed a vehicle speed, the functioning point of the ICE in power-split operation or of one electric machine in FEV operation has to be selected so as to determine \(\tau=V_{veh}/(R_{w}\cdot\omega_{in})\) and \(\eta=-P_{out}/P_{in}\), or \(p_{o}^{\prime}=-P_{o}/P_{out}\), respectively. To investigate all the viable powertrain functioning points for given vehicle speed, the whole range of operation of the ICE or MG O has to be explored. Therefore, the efficiency maps of the propulsors are needed (Figures 4 and 5). These are necessary also for evaluating the propulsors efficiency in each achievable working point.
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 7 Conclusions  
A growing body of work is being conducted using Sentinel-2 for both direct phenological assessment, such as changes in timing of phenological stages, and indirect applications, such as land cover mapping based on images from multiple dates through the growing season representative of the different phenological stages of the vegetation cover. The higher spatial, spectral and temporal resolution of Sentinel-2 is preferable to many of the other freely available optical datasets, since it has shown improved capabilities for vegetation mapping and estimating phenology. Sentinel-2 imagery used on its own in phenological studies has been proven to offer benefits over other sensors; however, its greatest strength lies in the ability to integrate the imagery with compatible sensors. This increases the opportunity to acquire cloud-free coverage and thus capture rapid, and short-lived, phenological events; however, as an optical sensor Sentinel-2 will always have limitations in areas with extensive and frequent cloud coverage. Given the success of the research done to date with data from within one year, or a small number of years, it would be envisaged that as the length of the Sentinel-2 record increases, this work can be expanded to explore climatological and human influences on phenology, as has been very successfully seen for the coarser resolution sensors, such as MODIS and AVHRR.  
[PERSON] wrote the first draft of the paper. [PERSON] and [PERSON] provided inputs and helped in extensive editing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This project is funded under the EPA Research Programme 2014-2020 (PhenoClimate, 2018-CCRP-MS.54). The EPA Research Programme is a Government of Ireland initiative funded by the Department of Communications, Climate Action and Environment. It is administered by the Environmental Protection Agency, which has the statutory function of co-ordinating and promoting environmental research.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 5 Open Issues and Challenges
### Achieving Interoperability  
Interoperability is a capability to interconnect two or more than two different devices and networks for data exchange. The smart health-care network consists of various IoT devices with different domains (i.e., remote health monitoring, remote surgery, and ECG). Interoperability performs a significant role by providing a platform of connectivity to various devices with different communication technologies. However, due to a lack of universal standards communications technologies, the interoperability between multiple domains is a significant hurdle for IoT success [79]. Therefore, a critical intelligent approach is required to check interoperability at different levels and allow for millions of devices in the network to communicate with each other. Different organisations, like oneM2M and FIWARE, are working in collaboration with various standardisations, like ETSI, OMA, and 3 GPP, to sort out the interoperability issue.
## 3 Results and Discussion
### Soil Aluminium Fractions
#### 3.2.3 Organo-Aluminium Complexes of Different Stabilities
Considering all variables together and based on eigenvectors and correlation coefficients (Table 3), it is possible to separate three distinct groups comprised of PM, biochars (BPC450 and BPC600), and hydrocarbons (HPC200 and HPC240). Overall, according to the PCA results, it was possible to conclude that the thermal processes promoted chemical changes in PM and, consequently, in its interaction with the soil Al. Furthermore, products obtained by distinct thermal processes (pyrolysis and hydrothermal carbonization) act on specific fractions of Al in the soil. The PCA results showed that PM was statistically associated with the soil total (Al\({}_{0}\)) and inorganic non-crystalline Al (Al\({}_{0}\)-Al\({}_{\text{p}}\)). EC in PM was strongly related to the presence of non-crystalline Al in the soil. In general, EC in PM, Al\({}_{0}\) and Al\({}_{0}\)-Alp had the most positive correlation coefficients with PC1 (Table 3), leading to stark contrasts between PM and its biochars and both hydrochars. Hydrochars showed a relationship with the soil exchangeable Al (Al-NH\({}_{4}\)Cl and Al-CaCl\({}_{2}\)), probably associated with high CEC in hydrochars, mainly in the one produced at the highest temperature (HPC240). Furthermore, the high OC values in the hydrocarbons [15] were also related to the presence of organically bound Al (Al\({}_{\text{p}}\)) in the soil. As previously shown (Table 2), the soil amended with hydrochars presented the highest concentrations of OAC of high stability. From the PCA results it is also possible to surmise that pH and carbonate content were the properties of biochar that contributed the most to the OAC of low and medium stability accumulation in the soil. In general, biochar pH measured in water increases with increasing total alkalinity. Although it varies widely according to the feedstock, pyrolysis temperature, and other pyrolysis conditions, generally the total alkalinity of biochars is composed mostly of carbonates [6]. Overall, the results of the PCA seem to suggest that there are different mechanisms underpinning the observed changes in Al fractions upon the addition of biochar, hydrochar or manure. These mechanisms are likely to include pH, EC, the amount and form of organic matter and the presence of different anions and cations introduced with the amendments.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 3 Experimental Results
### Results
#### 3.4.1 Results on OASIS Dataset  
The OASIS dataset contains the images which are already skull stripped. Bias field correction was performed using the ROBEX tool [34]. Figure 1 shows the qualitative segmentation results obtained using the proposed method. We compared the results of the proposed method with the three state-of-the-art methods, i.e., HMRF-EM [8], FAST-PVE [41], and MSSEG [42]. All three methods' codes are available on the authors' websites. The comparison of their results is presented in Table 1. We notice that the proposed model has better performance with regard to CSF, GM, and WM when compared to the other methods.
## 4 Simulation Experiment
### Experimental Results
#### 4.2.5 Comparison with Other Algorithms  
To make an objective evaluation of our algorithm, we selected several representative algorithms for comparison, the corresponding algorithms and evaluation indicators are shown in Table 4; among them, our algorithm takes the algorithm under the adjacent orbit as an example. We compare and analyze the corresponding algorithms and indicators, and Reference [20] is also compared based on the switching algorithm under an adjacent orbit.  
From Table 4, we can find that our proposed algorithm has greater advantages than the algorithm [15] in terms of longitude, latitude, and altitude indicators, especially in altitude indicators. Compared with the dual-system positioning algorithm proposed by [PERSON] [17], our algorithm also has certain advantages. Compared with the algorithm proposed by [PERSON] [20], although our algorithm is slightly inferior in the mean longitude, the standard of the longitude of our algorithm is \(0.4628\times 10^{-4}\), which is better than the standard deviation of \(0.56238\times 10^{-4}\) of the algorithm [20]; this shows that our algorithm has smaller fluctuations in longitude errors and more concentrated errors, and our algorithm has certain advantages in terms of latitude and altitude indicators. Compared with the algorithm proposed by [PERSON] [21], in addition to the longitude and latitude indicators, our algorithm has better accuracy in terms of altitude indicators. From these comparisons, we can see that our proposed algorithm is better than the traditional dual-satellite positioning algorithm and dual-mode system positioning algorithm and even some advanced location service solutions, therefore, our proposed algorithm can be used as a good reference solution in difficult environments.
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
## 2 Method
### Proposed Method: Total Variation Strain Field Regularization (TV-Reg)  
The proposed methodology is based on DVC technique with the incorporation of a regularization step. The innovation lies in the introduction of a regularization term defined by the Total Variation of the strain field. The objective is to minimize the TV of the strain field, which enforces a piecewise constant strain profile while permitting discontinuities  
\begin{table}
\begin{tabular}{c|c|c} \hline
**Technique** & **Parameters** & **Disadvantages** \\ \hline Gaussian filters [(20)] & Window size, standard deviation & Not suitable for interface-related issue \\ \hline Median filters [(10)] & Window size & Challenges related to identifying narrow gaps between cracks \\ \hline Average filter [(17)] & Window size, weight & Loss of information at the edges and borders \\ \hline PLS [(23; 24; 25)] & Window size & Trade-off between noise reduction and window size \\ \hline \multirow{2}{*}{Geers’ method [(22; 26)]} & \multirow{2}{*}{Number of neighboring points} & Trade-off between noise reduction and locality of the solution \\ \cline{1-1}  & & \\ \end{tabular}
\end{table}
Table 1: An overview of methods employed for reducing noise in strain field.  
(jumps) in the strain field. The cost function to be optimized comprises an image-based similarity metric and a strain field-based TV metric. The optimization of this function requires balancing the trade-off between data fit, represented by the similarity metric, and deformation discontinuity.  
DVC has been performed by using a B-spline function as model to describe the deformation. In this way, a parametrization of the displacement field was possible and that allows the dimensionality of the registration optimization problem to be reduced.
# Removing Land Subsidence Impact from GPS Horizontal Motion in Tianjin, China
## 3 Results
### Comparison  
We used three parameters, the Mean Absolute Deviation (MAD), Standard Deviation (SD), and Coefficient of Variation (CV) of horizontal rates and azimuths, to measure the degree of dispersion of the horizontal motions of the regional GPS stations. These parameters offer a multifaceted assessment of data dispersion, taking into account not only the magnitude of absolute variations (MAD) but also the variations relative to the mean (SD), and the relative variations among different variables (CV). The integrated application of these three metrics enables a more precise characterization of the extent of data dispersion.  
The dispersion parameters of the original and predicted values of the horizontal motions of the 10 stations in the subsidence area and all 18 stations in the study area are shown in Tables 2 and 3.  
The predicted values, which exclude the impact of land subsidence, are markedly smaller than the original values for each parameter. This is indicative of the extent of dispersion in the horizontal motion of the stations. To illustrate, the coefficients of variation of the model-predicted values of horizontal rates and azimuths for stations in the subsidence area are 57% and 51% of the original values, respectively, in comparison to 66% and 57% for the entire study area.  
After removing the impact of land subsidence, the dispersion of the horizontal motion of the stations in the subsidence area in southern Tianjin is greatly reduced, and the overall trend of horizontal motion is more obvious, which is more consistent with that of the stations in the neighboring non-subsidence zones. The significant reduction in the inconsistency of the horizontal motion of the stations affected by land subsidence proves the effectiveness of the model in removing the impact of land subsidence from the horizontal motion of the stations.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 5 Experimental Setup
### Data Generation and Augmentation  
To create a speech mixture, the following method is used: Initially, a pair of speakers is chosen at random. Subsequently, short segments lasting 4 s are randomly selected from two audio source files. The source segments are finally combined and noise is applied at a  
Figure 10: Flowchart of a revised U-Convolutional block, which adds a conformer layer to the bottom of an ordinary U-Convolutional block.  
random signal-to-noise ratio (SNR). The training set consists of 20,000 mixtures, while the validation and test sets each contain 3000. The choices of utterances for training, validation, and test sets exactly follow the arrangement of the works SuDoRM-RF [12] and SuDoRM-RF++ [13]. All audio clips are downsampled to 8 kHz and normalized by subtracting their mean and dividing by their standard deviation.