esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
In this section, a novel cycle slips detection method is introduced to solve the anomaly problem of double frequency BDS-3 observations in a severe multipath environment. Firstly, the conventional cycle slips detection model of a geometry-free phase combination and Melbourne-Wubbena combination is described in detail. Secondly, an AR model was constructed with a sliding-window method by selecting the pure pseudorange observations as the training quantity. By this means, the MW combination observation equation is reconstructed by using the predicted dual-frequency pseudorange observation values, and the AMG combined cycle slips detection model and estimation is rigorously derived. Finally, the completeness monitoring index is introduced to analyze the performance of the proposed method.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 4 Discussion
### Potential of Sentinel-2 for Supporting Agricultural Management
Nonetheless, where our study focuses on the spectral characteristics of Sentinel-2, we would recommend future research related to Sentinel-2 spatial resolution that includes tracking the propagation of uncertainty when estimating variables using Sentinel-2 data at 10 and 20 m resolution for specific crop types.
## 5 Simulation Results
Consequently, link persistence emerges as superior to other routing metrics in terms of path reliability. The demonstrated performance indicates that the proposed link persistence estimation can effectively assist routing protocols in selecting more reliable paths. Finally, we present a comparison between the mobility model proposed in this paper, the mobility model outlined in [7], and a mobility model derived from actual vessel trajectories. Because of disparities in simulation experiment configurations, we were \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Packet Loss Ratio** & **End-to-End Delay** & **Throughput** \\ \hline Link persistence & 0.6456 & 12.38 & 18.98 \\ Shortest path & 0.7105 & 25.62 & 15.78 \\ First found path & 0.6562 & 42.03 & 16.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different metrics. Figure 10: Objective function versus interrupt probability. unable to directly correlate our findings with those of [7]. Therefore, we replicated their methodologies and assessed them within the same simulation environment. Employing the DSR protocol and adopting the routing metric proposed in this paper--link persistence--we were able to synthesize the results which are shown in Table 3.  
The results in Table 3 clearly demonstrate that the proposed mobility model more closely aligns with the performance of the mobility model derived from actual vessel trajectories than with the mobility model presented in [7]. This highlights the accuracy and reliability of the proposed mobility model in characterizing node mobility. An accurate mobility model is fundamental to the effectiveness of routing metrics. The proposed mobility model shows superior performance to the model in [7] in terms of packet loss ratio, end-to-end delay, and throughput. This indicates that the proposed model has a higher degree of accuracy and reliability in describing node mobility and predicting link persistence.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 4 Fast Time Simulation Results
### LECMDGU Sector  
#### 4.3.1 Capacity  
The days of the busy week (1-7 July 2019) were simulated in RAMS for the computation of the capacity in the LECMDGU sector. The capacity value obtained in the Baseline Scenario after performing the FTS in RAMS was 46 aircraft/hour (46.74 aircraft/hour). The declared capacity of the LECMDGU sector is slightly lower, 44 aircraft/hour. Hence, there is a difference of two aircraft/hour, whereas for the LECMZMU and LECMDGU, the difference was only one aircraft/hour. Nevertheless, this capacity value should be considered as a benchmark to compare the capacity values associated with the other two scenarios. In Scenario 1, the capacity outcome is 47 aircraft/hour (47.17 aircraft/hour) while in Scenario 2, this stands at 47 aircraft/hour (47.63 aircraft/hour).  
Table 4 presents a comparison of the capacity results obtained for the LECMDGU sector. It is observed that the increase in capacity is similar to that in LECMTLU, though slightly lower. In relative terms, there is a shift from 46 aircraft/ hour to 47 aircraft/hour. However, in absolute terms, the increase in the best-case scenario (Scenario 2) is 0.9 aircraft/hour, signifying an almost 2% capacity increase in the sector.  
Figure 9: Environment and cost-efficiency results for the LECMTLU sector.
## 7 Security Analysis
### Formal Security Analysis Using BAN Logic
#### 7.3.3 Idealized Forms  
The idealized forms are formulated as follows:  
**\(\begin{array}{ll}\textbf{{Msg}1:}&\;\;\;\;\;\;\;\;\;\;\;\;  
**Step 3:**: Using the freshness rule with \(A_{1}\), we can obtain  
\[S_{3}:UC_{j}\mid\equiv SD_{i}\mid\equiv\#(RID_{i},R_{SD})_{X_{i}}\]
**Step 4:**: From the nonce verification rule with \(S_{2}\) and \(S_{3}\), we can obtain  
\[S_{4}:UC_{j}\mid\equiv SD_{i}\mid\equiv(RID_{i},R_{SD})_{X_{i}}\]
**Step 5:**: Using the belief rule with \(S_{4}\), we can obtain  
\[S_{5}:UC_{j}\mid\equiv SD_{i}\mid\equiv(R_{SD})\]
**Step 6:**: Because of \(SK=h(R_{SD}||R_{UC})\), from the \(S_{5}\) and \(A_{2}\) we can obtain  
\[S_{6}:UC_{j}\mid\equiv SD_{i}\mid\equiv(UC_{j}\stackrel{{ SK}}{{ \longleftrightarrow}}SD_{i})\quad\textbf{(Goal 2)}\]
**Step 7:**: From the jurisdiction rule with \(S_{6}\) and \(A_{7}\) we can obtain  
\[S_{7}:UC_{j}\mid\equiv(UC_{j}\stackrel{{ SK}}{{\longleftrightarrow}}SD_{i})\quad \textbf{(Goal 1)}\]
**Step 8:**: According to \(Msg_{2}\), we can obtain  
\[S_{8}:SD_{i}\lhd\left(RID_{i},RID_{j},R_{UC}\right)_{X_{i}}\]
**Step 9:**: Using the message meaning rule with \(S_{8}\) and \(A_{4}\), we can obtain  
\[S_{8}:SD_{i}\mid\equiv UC_{j}\mid\sim(RID_{i},RID_{j},R_{UC})_{X_{i}}\]
**Step 10:**: Using the freshness rule with \(A_{2}\), we can obtain  
\[S_{10}:SD_{i}\mid\equiv UC_{j}\mid\equiv\#(RID_{i},RID_{j},R_{UC})_{X_{i}}\]
**Step 11:**: Using the nonce verification rule with \(S_{9}\) and \(S_{10}\), we can obtain  
\[S_{11}:SD_{i}\mid\equiv UC_{j}\mid\equiv(RID_{i},RID_{j},R_{UC})_{X_{i}}\]
**Step 12:**: Using the belief rule with \(S_{11}\), we can obtain  
\[S_{12}:SD_{i}\mid\equiv UC_{j}\mid\equiv(R_{UC})\]
**Step 13:**: Because of \(SK=h(R_{SD}||R_{UC})\), from the \(S_{12}\) and \(A_{1}\) we can obtain  
\[S_{13}:SD_{i}\mid\equiv UC_{j}\mid\equiv(UC_{j}\stackrel{{ SK}}{{\longleftrightarrow}}SD_{i})\quad\textbf{(Goal 4)}\]
**Step 14:**: Using the jurisdiction rule with \(S_{13}\) and \(A_{8}\) we can obtain  
\[S_{7}:SD_{i}\mid\equiv(UC_{j}\stackrel{{ SK}}{{\longleftrightarrow}}SD_{i})\quad\textbf{(Goal 3)}\]  
Based on Goals 1-4, we proved that proposed protocol ensures secure mutual authentication between \(SD_{i}\) and \(UC_{j}\).
## 3 The Deep Learning-Based Approaches  
Although optimization algorithms can achieve relatively high performance in solving the associated resource management problems, these algorithms entail a huge number of iterations, which increase the computation time. So, the implementation of these algorithms in real time is still a challenging issue. In this section, we suggest a deep learning-based approach to wireless resources allocation, where wireless resources can be approximated by deep learning architectures such as the DNN and RNNs. The proposed deep learning-based approach is shown in Figure 2. The basic idea is to use a set of wireless resources that is solved by an optimization algorithm, and to attempt to learn its input and output through the training stage. If a network efficiently approximates to a wireless resource allocation optimization algorithm, then the input of the optimization algorithm is passed into the trained network to get the output with higher computational efficiency.
###### Abstract  
Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.  
Keywords:automated processing; workflow; real time; very high spatial resolution; surface reflectance; satellite image; smallholder farming; agriculture +
Footnote †: journal: Remote Sens. 2017
## 3 Healthcare
### Human Activity Monitoring
#### 3.1.3 Challenges and Future Directions  
This case study on activity monitoring using dense ambient RF waves brings up a potential use case for future 6G mobile networks. Intelligent reflecting surface technology [41] being proposed for 6G are also great contenders for indoor activity monitoring [42]. By having these activity monitoring modules inherently integrated into the 6G radio antennas, this use case falls under two of the three key drivers for 6G, especially (i) high-fidelity holographic society, and (ii) time-sensitive and time-engineered applications. The ubiquity of 6G means that this approach can address a large number of applications. The practical implementation of such use cases adds a certain complexity to the radio planning stage, where in addition to the coverage footprint, the monitoring area and its environmental complexities also need to be taken into account. At this stage, this application is mainly proposed for healthcare settings. However, with large scale and ubiquitous penetration of human activity detection, this use case can play its role in energy usage monitoring, search and rescue missions, physical security of premises, Industry 4.0 applications and many other sectors.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Preparation of Data for the System
#### 1.3.6 Interpretation of Response Vectors  
In the diagnostic response vectors, the following number value designations were adopted:  
* **0**--Indicates that there is no question for this specific disease entity (class) at this position in the vector. In disease-related questions, the absence of a question means that this feature is not relevant for diagnosing this disease entity.
* **1**--Indicates that the question was asked, and the patient answered negatively.
* **2**--Indicates that the question was asked, and the patient answered positively.
* **3 and above**--These values are used for multiple-choice questions, where each number represents a specific answer identifier within the question system. For example, the question \"Where do you feel pain?\" might have the following answers: neck (3), leg (4), abdomen (5), and so on.  
In this way, the system uses the patient's responses to match the response vector to the model disease vectors, enabling effective diagnosis based on a minimum number of questions.
## 2 Synopsis of the Methodology  
The methodology [4; 5] comprises two stages; assessment and retrofitting at both local and global structural level. The assessment starts upon knowledge of the building's geometry, materials properties, and reinforcing detailing of the primary elements (columns, beams). The axial loads of the ground floor columns are calculated based on the seismic combination and the elements' slenderness is checked. Those columns subjected to high axial loads are considered susceptible to brittle failure, thus rendering them critical for the stability of the structure. The deformation capacity (in terms of drift at yielding and at ultimate) and flexural strength are assessed. The methodology suggests alternative approaches to estimate the deformation capacity aiming to demonstrate the easiness in application of each approach and the differences in the yielding values. At this stage the secant to yield translational stiffness is estimated and the effective period of the structure is calculated and directly related to demand in terms of elastic spectral displacement. The deformation capacity of the existing members is dictated by the type of deficiency identified and needs to be modified accordingly (i.e., in case of insufficient shear strength, short lap splices).  
Strengthening at global level aims to modify the lateral response shape of the building by controlling the distribution of interstorey drift height-wise. This is achieved by the addition of stiffness along the height of the building as to comply with the target period value (lower than the effective period and closer to the code requirement) and the target response shape (i.e., target interstorey drift distribution). Design charts are used to facilitate the procedure of defining the required increase in stiffness per storey. In general, an exceedance of the code's reference period value no higher than 25% and a shear-type shape leads to reduced intervention cost. The seismic displacement demand is recalculated for the strengthened building and demand at structural element level is defined. Based on the assessment outcome and if the ductility capacity is below demand, then the required FRP number of plies need to be calculated. In case of columns, the addition of FRP reinforcement also aims to alleviate brittle modes of failure (i.e., shear, lap-splice, compression bar buckling). Special attention is attributed to the external, beam-column joints which are not laterally supported by transverse beams and do not have sufficient detailing (i.e., horizontal stirrups).
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 5 Discussion  
### Model Acceleration  
#### 5.1.1 Latency Quantisation  
To accelerate the model effectively, we first quantified the latency of this method on the ResNet 101 model, which has the most significant number of computational layers, as shown in Table 6.  
As can be seen from the table, the main delay of the model is as follows:  
1. The model validates the intermediate results of the calculation several times to verify the validity of the intermediate results calculated by the server.
2. The model is a single-threaded mechanism in the process of network transmission and server computation, in which the computation power is not effectively used, i.e., the server computation power waits for data transmission.
3. Due to the nonlinear operator of the tested algorithm, a large amount of communication between the client and the server is created.  
Based on the above experimental results and analysis, three optimisation methods were designed: pre-calculation of some validation values; batch transmission of data; and modification of the calculation model.
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 3 Results and Discussion
### Qualitative Analysis of Soy Product Types  
The possibility to differentiate soy protein types in ground beef was also evaluated by a classification approach using support vector machine (SVM). In SVM, the classification decision function is fixed based on the structural risk minimum mistake instead of the minimum of the misclassification on the training set to avoid an over-fitting problem [13]. In our work, SVM was selected because it is more suitable to grasp the nonlinear relationship between the data and features when the sample size is small. Samples of each type in various contents were all used in the calibration set. As for validation, eight samples for each soy type were randomly selected in the duplicate test to create a set of total 32 samples. The classified results from comparing the two models developed by dispersive and FT-NIR spectra are shown in Tables 3 and 4. For dispersive spectral data, the evaluated model offered a very high discriminating ability, showing a total accuracy of 95% and 84.38% for calibration and validation. In the calibration set, only toasted soy grits was wrongly discriminated at low contents, which might due to it having the lowest correlation with the dispersive NIR spectra among the four types. The classification model developed by FT-NIR spectra data gave a result of a total of 83.33% and 78.13% recognition accuracy for the calibration and validation set, respectively. However, the low soy content (\(\leq\)5%) classification performance was quite unsatisfactory, as there were incorrectly classified samples for Arconet, toasted soy grits, and Profam. Although better results were obtained by dispersive NIR data than by FT-NIR, low mixed soy protein contents (\(\leq\)5%) cannot be detected using either method.
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 6 Conclusions  
To solve the problem that the finite element method takes a long time to calculate the multi-physical field coupling model, a cable temperature prediction model construction method based on machine learning is proposed. The random forest algorithm is used to calculate the importance score of the characteristic variables, and six-dimensional input characteristic variables with an importance that is greater than 4% in model training are selected. The cable temperature prediction model based on RF-GPR is constructed by the series mixing model. The model accuracy (\(R^{2}\)), root-mean-square error (\(RMSE\)) and average relative error were 0.9879 and 0.8564, respectively. The average relative error of the cable temperature prediction in the cable trench is 0.17%, which can replace the finite element method to predict the temperature distribution in the cable trench. Under the same conditions, the prediction time of the cable trench temperature based on the Gaussian process regression algorithm is nearly 1500 times shorter than that of the finite element calculation, which effectively improves the real-time performance of cable temperature prediction. A digital twinning platform for cable temperature calculation based on RF-GPR is designed to provide technical support for the application of digital twinning for cable running state evaluation.  
Conceptualization, W.H.; methodology, G.Y.; validation, C.H.; writing--original draft preparation, G.Y. and D.K.; writing--review and editing, D.K.; project administration, C.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of State Grid Hebei Electric Power Company (kj2021-060).  
The authors declare no conflict of interest.  
Figure 10: Cable temperature calculation digital twin platform interface.
## Results
### Subcellular Localization of OpWRKY3  
To experimentally confirm the subcellular localization of OpWRKY3, _OpWRKY3_ was cloned into the pMON530 vector (the binary vector) and fused with the green fluorescent protein (GFP) reporter gene to generate vector pMON530-OpWRKY3-GFP. Then, the constructed vector and the pMON530 (used as the control) was transformed into the ASE strain and expressed in tobacco leaves, respectively. In the leaves of the transformed control vector plant, the fluorescence of the GFP was detected in the cytoplasm and nucleus, whereas the fluorescence of the OpWRKY3/GFP fusion protein was exclusively found in the nucleus (Figure 3). The expression pattern was consistent with the character of OpWRKY3 as a TF.  
Figure 1: Multiple sequence alignment and phylogenetic analysis of OpWRKY3. **(A)** Phylogenetic analysis of WRKY transcription factors. OpWRKY3 is highlighted with a red triangle. The phylogenetic tree was constructed with the neighbor-joining test using MEGA 7.0. **(B)** Multiple alignment of OpWRKY3 with other WRKY members. The following proteins were used for analysis: VqWRKY52 (AQ895512.1), VvWRKY30 (ALM9663.1), AcWRKY41 (PSS28797.1), PtWRKY30 (AZQ19316.1), AtWRKY41 (AAL35289.1), and AtWRKY30 (NP\({}_{-568439.1}\)). Identical and similar amino acids are shaded in black and red, respectively. The WRKY domains and the zinc-finger structures are boxed and marked by squares, respectively.
## 2 Related Research
### Residual Learning, Spatial Pyramidal Pooling, and Attention Mechanism  
Segmentation accuracy can be improved with increasing network depth. However, this has been shown to hinder the training process and ultimately contribute to accuracy degradation [32]. [PERSON] et al. [33] proposed deep residual learning to facilitate the training process and solve the degradation problem. ResUNet [5] uses full residual units prior to activation. The residual unit simplifies the training of the deep network, and the skip connection within the network ensures that the information is passed without degradation. The improved version of the ResUNet is the ResUNet++ architecture [10]. It takes advantage of the residual blocks, the squeeze and excitation block, ASPP, and the attention block. The attention mechanism, placed at the bottom of UNet architecture, determines which parts of the network require more attention [12]. In this way, it reduces the computational cost of information encoding and enhances the quality of features that boost the results, ultimately enhancing the results.  
Convolution is an essential step in both CNN and FCN models, as it allows models to learn increasingly abstract feature representations. However, the pooling operations and the convolution steps between layers in the convolution process reduce the spatial resolution of the feature map, resulting in a loss of spatial detail. To learn the contextual information at multiple scales, dilated or atroid convolutions [34; 35; 36] are introduced. They are able to increase the receptive field and maintain the resolution of the feature map by injecting holes into the standard convolution. Compared to the original standard convolution, the dilation convolution has a hyperparameter called dilation rate, which refers to the number of intervals of the convolution kernel. The idea of capturing contextual information at different scales subsequently led to the ASPP module [9; 37]. Here, a large number of parallel atrous convolutions with different rates are fused together in the input feature map to control the field of view and accurately capture information at different scales [38]. The most prominent network that uses dilated convolution is DeepLabV3 [39]. This network combines the contextual information at multiple scales by fusing the lower and upper layer features. In addition, PSPNet18 [40] uses a pyramid pooling module that aggregates contextual information of different grid scales to improve the ability to obtain global information. In DeconvNet [41], stacked deconvolution layers are used for full resolution recovery. However, this results in a large number of parameters and can lead to difficulties in training.