esa-sceva/satcom-chunk-collection huggingface dataset:
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 3 Related Work
### General-Purpose Platform
's method performs stalling before the read access \(R3\) of _thread_2 is executed to delay the read access \(R3\) until the write access \(W2\) of _thread_1 comes to repair the atomicity violation. Similarly, if this program is correctly executed in the testing phase as shown in Figure 2a, according to the \(BSet\) collection conditions, \(BSet(R1)\) and \(BSet(W2)\) are stored as \(null\) and \(BSet(R3)\) and \(BSet(W4)\) are stored as \(W2\). This \(BSet\) is used to diagnose atomicity violations in comparison with the actual interleaving in the operation phase. If this program is incorrectly executed in the operation phase, as shown in Figure 2b, \(BSet\) is collected as shown in the atomicity violation of Figure 2d. The actual execution is shown in Figure 2b. AI repairs the atomicity violation using the following steps: First, when read access \(R1\) of _thread_1 is issued, it is compared with the \(BSet(R1)\) from the correct interleaving. We find that it is not diagnosed as an error, because the \(BSet(R1)\) from the correct interleaving and the \(BSet(R1)\) from the actual interleaving are equal to \(null\). Next, when the read access \(R3\) of _thread_2 is issued, it is compared with the \(BSet(R3)\) from the correct interleaving. Since  
Figure 2: The result of PSet and BSet of interleaving. (**a**) Correct interleaving. (**b**) Atomicity violation. (**c**) \(PSet\) [36]. (**d**) \(BSet\) (AI) [37]. the \(BSet(R3)\) from the correct interleaving and the \(BSet(R3)\) from the actual interleaving are different from \(null\) and \(R1\), it is diagnosed as an error. To treat the diagnosed error, AI stalls the execution read access \(R3\) of \(thread2\) via sleep() and lets the program continue with \(thread1\) and issue write access \(W2\). Since the execution sequence is reordered to the correct sequence, the atomicity violation is repaired. The on-the-fly repairing of atomicity violations is essential in two aspects, time overhead and coverage. These approaches have the advantage of low overhead because atomicity violations are diagnosed based on the correct interleaving collected in the test phase and treated with stalling. The low overhead of these approaches shows their applicability to real-time systems. The coverage is different because the methods of collecting the correct interleaving of the two approaches are different. [PERSON] et al. [36]'s method collects only one correct interleaving and AI [37] collects as many correct interleaving types as possible. This difference allows AI to diagnose the correct interleaving so that the accuracy of the diagnosis is higher than that of [PERSON] et al. 's method [36].
## 5 Summary and Conclusions  
The sensitivity of the fragility curve of an RC frame to material properties and structural detailing to the fragility curve of an RC frame was evaluated in this paper. The selected uncertain variables considering the possible construction quality defects were concrete strength, yield strength of rebar, longitudinal reinforcement ratio, and volumetric ratio of transverse reinforcement. The vulnerability curve for each limit state at the global and local levels was derived from the extensive nonlinear dynamic analyses with 17 RC frame models. The results of the sensitivity analysis identify the uncertain variables that most significantly affected the seismic response of the selected structures. The important findings are summarized below.  
It was observed that the fluctuation of the vulnerability curves was significant at the damage control and collapse prevention limit states when concrete strength was considered as an uncertain variable. The magnitude of this fluctuation, named swing, was much higher than those of models with uncertainties related to the longitudinal reinforcement. The swing of the fragility curves with the uncertainty in the volumetric ratio of transverse reinforcement also increased notably. These results are clearly confirmed through relative sensitivity analysis. Thus, the seismic vulnerability at the global level was found to be particularly sensitive to the concrete strength and the volumetric ratio of transverse reinforcement.  
The selected structure was identified to be susceptible to failure at the system level as well as the member level. Thus, the impact of the selected uncertain variables on the shear vulnerability of an RC column was assessed at the local level. It was observed that the shear vulnerability was very sensitive to the rebar yield strength, longitudinal rebar ratio, and volumetric ratio of stirrups. Particularly, the most significant effect on shear vulnerability was due to the volumetric ratio of the transverse reinforcement.  
In conclusion, the seismic vulnerability of the selected RC frame was mainly affected by variability in concrete strength and the volumetric ratio of the transverse reinforcement. Therefore, securing the proper construction quality of these parameters is critical for the reliable seismic performance of an RC frame.  
Conceptualization, S.K. and S.J.K.; methodology, S.K.; investigation, S.K. and T.M.; writing--original draft preparation, S.K.; writing--review and editing, S.J.K.; supervision, S.J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (20 DRMS-B146826-03) from the Development of Customized Contents Provision Technology for Realistic Disaster Management Based on Spatial Information Program, funded by the Ministry of the Interior and Safety of the Korean government.  
Figure 11: Shear vulnerability curve and relative sensitivity.
## 3 Methodology
### Feature-Enhanced Point Transformer
#### 3.1.2 Ball Tree Downsampling Module
```
Function ball_tree_search is Global: Q--Cache of k nearest neighbors (initially containing a point at infinity), q--Corresponding to Q, storing distances between points in Q and the test point Input: k--Search for k nearest neighbors, t--Test point, node--Current node Output: None If distance(t, node.pivot) - node.radius \(\geq\) max(q) then Return End if If node is a leaf node, then Add node.p to Q and update q accordingly If there are more k nearest neighbors in Q, remove the point in Q that is farthest from the test point and update q accordingly Otherwise Recursively search the two child nodes of the current node ball_tree_search(k, t, node.s1) ball_tree_search(k, t, node.s2) End if End function
```  
**Algorithm 1** Ball Tree Search Algorithm. Figure 9: The partition process of the ball tree algorithm. The ball tree algorithm is an efficient data structure that can be utilized for point cloud semantic segmentation tasks to enhance both model accuracy and efficiency. In the case of the point transformer network, this study employs a ball tree construction to efficiently prune redundant points, thereby improving the algorithm's efficiency and accuracy.
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 6 Conclusions  
This paper proposes a flexible economic dispatch model under the constraints of power and natural gas systems. To reduce system operation cost and enhance its response capability for uncertainty as much as possible, both the P2G facility and demand response have been considered. The co-optimization model is decomposed into a flexible economic dispatch of upper power system and an economic dispatch of lower natural gas system to realize the distributed co-optimization of power and natural gas system. Moreover, slack variables are introduced for the coordinated optimization of the upper and lower systems. Simulation results demonstrate that the proposed flexible economic dispatch model can improve the power system flexibility better than the economic dispatch model. Considering P2G facilities and demand response at the same time can effectively improve the downward flexibility during the trough period and the upward flexibility during the peak period. Compared with considering the P2G equipment alone, the total operating cost of the electricity-gas system can be reduced by 6.45%. Furthermore, the hourly unit commitment and adjustable capacity of the power system are affected by the natural gas system transmission constraints. The coordinated optimization of the power and natural  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Separate Optimization** & **Coordinated Optimization** \\ \hline Units generating cost/\$ & 227,259 & 190,393 \\ P2G operating costs/\$ & 2400 & 2400 \\ Wind power curtailment cost/\$ & 0 & 0 \\ Load shedding cost/\$ & 7480 & 4300 \\ Units adjustable capacity allocation & 33,272 & 33,295 \\ cost/\$ & 500 & 687 \\ Total cost of power system/\$ & 270,912 & 231,075 \\ Load reduction of natural gas & 368 & 0 \\ system/kcf & 36 & 0 \\ Excess natural gas from P2G/kcf & 36 & 0 \\ Total cost of gas system/\$ & 320,656 & 244,687 \\ Cost of IPGS/\$ & 547,916 & 475,763 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dispatch results of two scheduling modes.  
gas systems can improve the optimal allocation of resources, thus reducing the operation cost of the two systems.  
All authors have made substantial contributions to this paper. Among them, [PERSON] mainly did supervision and review; [PERSON] and [PERSON] mainly wrote and revised this paper; [PERSON] mainly did simulation calculations. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2016 YFB0901900), National Natural Science Foundation of China (51877174), and State Key Laboratory of Electrical Insulation and Power Equipment (Xi'an Jiaotong University, EIPE18201).  
Most of the data supporting the results of this study have been stated in the article or appendix, and the rest can be obtained from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Implementation of Efficient 5G AKA Protocol for Light-Weight Environment
## 1 Introduction
### Background  
Before explaining the methodology of this study, we expand upon two major pieces of background knowledge: the vulnerability of the 5G AKA protocol and the algorithms used in the protocol. In the 5G AKA protocol section, we provide an overview of both the protocol and attacks that exploit the vulnerability. In the algorithm section, we describe the MILENAGE algorithm [19] introduced to apply the LEA.
## 3 Algorithms
### Direction Parallel Algorithms
#### 3.1.1 Fixed Intrinsic Angle (FInA)  
To have a fixed or constant intrinsic angle of the fiber, the lines used for construction of the path are set to be equidistant to neighboring lines in all points. To construct lines with this property, curves of equal value on \(\phi\) are computed with the intersecting plane \(P\) being chosen by the user. These isogeodesics are connected at the ends to obtain a continuous path as displayed in Figure 2a for an example surface. This procedure ensures a high density of fiber while retaining a notion of directionality. Drawbacks are high curvatures when changing directions and during travel motions.  
The inputs for this algorithm are the triangular surface mesh \(\mathcal{T}\), the plane \(P\), the spacing \(d\) and the travel height \(h_{r}\).  
First, the mesh is split into two halves \(\mathcal{T}_{1}\) and \(\mathcal{T}_{2}\) by the plane \(P\) to retain a way of distinguishing the sides of the plane as \(\phi\) is non-negative, which results in a multiplicity of curves for every distance. The seed curve \(\gamma\) for both meshes is calculated by intersecting the seed plane with the triangular surface mesh \(\mathcal{T}\). To obtain the geodesic distance, \(\phi(x_{p})\) is evaluated for every point in the mesh.  
The isogeodesic curves \(\{c_{i}\}_{i=1,\dots,n}\) with \(c_{i}=\{x_{p}|\phi(x_{p})=i\cdot d\}\) are represented as points in space resulting from the interpolated value of the distance function on \(\mathcal{T}_{1}\) and \(\mathcal{T}_{2}\), respectively.  
For every \(c_{i}\), the points in the curve are connected to an ordered path segment \(s_{i}(k)\) with k denoting the point index. To connect the lines, they are sorted by distance from the seed curve and iteratively appended to the resulting path. The order of traversal of a curve is reversed every iteration to ensure the back and forth movement. If \(c_{i}\) has n discontinuities, travel motions are incorporated between the corresponding segments \(s_{i,0}(k)\) to \(s_{i,n}(k)\) by offsetting the respective boundary point by the travel height \(h_{r}\) along the normal vector  
Figure 2: Direction parallel algorithms and resulting paths. Travel motions are not displayed. With \(d=1\) mm (**a**) FInA, (**b**) FExA, (**c**) Ortheo. Filament thickness displayed does not correspond to thickness when printed.  
Upon computing and connecting both sides, the path is resampled to obtain an even point distribution and smoothed by a Savitzky-Golay filter to eliminate sharp turns.
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 6 Current Developments in Using Sentinel-2 for Phenological Research
### Overcoming Cloud Cover  
When compared with other satellite and ground-based remote sensors, Sentinel-2 clearly offers spatial, temporal and/or spectral benefits. The high spatial resolution of 10-20 m, temporal resolution of 5 days or less, and multiple red edge bands makes Sentinel-2 quite promising in observing LSP. However, it does suffer from limitations for phenological studies, especially with respect to the unavoidable data gaps arising from cloud cover (which are a characteristic of optical sensors). For example, [PERSON] et al. (2015) ([PERSON] et al., 2015) discussed the fact that high frequency cloud cover inhibits accurate estimates of SOS and POS, and [PERSON] et al. (2018) ([PERSON] et al., 2018) showed that the impact of clouds, and therefore missing observations, also affects estimates of EOS. This study revealed that identification of the SOS is more sensitive to cloud cover than POS or EOS because it tends to occur over a shorter period of time. Several successful initiatives involving data fusion have been highlighted, such as the HLS dataset to overcome the issue of data gaps arising due to cloud cover. The VENuS sensor with its Sentinel-2-like spatial and spectral characteristics but a two-day revisit also deserves to be mentioned. It has been reported to offer improved capabilities for use in conjunction with Sentinel-2, but its availability is limited to a few sites globally and hence offers little scope for greater use ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018).  
The accuracy of cloud masks is critical to minimise errors in processing of time series data from satellites. Faulty cloud masks have been shown to deviate the estimated start of season by up to 10 days ([PERSON] et al., 2018) and hence require careful consideration in modelling of vegetation index time series curves. This is particularly important for the Sentinel-2 dataset, since [PERSON] et al. (2018) ([PERSON] et al., 2018) showed that the cloud masks available with the HLS, which uses the popular LaSRC and Fmask algorithms, were of poor quality (accuracy of 76-89%) and had to be recalculated from Landsat-8 and Sentinel 2 data using decision trees (yielding an accuracy of 99%). The European Space Agency-based processor,Sen2 cor, is also known to contain several limitations, particularly in misclassifying urban areas and bright pixels as clouds, which further limits the accuracy of its scene classification layer [143; 144]. Hence, more investigation is required in development of improved cloud masks, possibly through local refinement according to prevailing atmospheric conditions.
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 5 Analysis of IoT Authentication Schemes
### Vehicular Networks
In most VANETs, there was a problem of connecting it to IP networks using road-side access routers (ARs) due to obstacles and mobility of the vehicle. Hence, the authors of [102] studied the ability to provide IP connectivity to the vehicles in VANETs using a multi-hop scheme. The location and road traffic information are stored in a location server used for roaming purposes. The approach provides a mutual authentication between the roaming vehicle and ARs.
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 2 Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
#### 2.1.4 Iterative Method for Threshold Selection  
Threshold segmentation refers to using a set threshold to separate the signal and noise, and the threshold determines the edge detection effect. The traditional wavelet modulus maximum edge detection needs to set the threshold manually, the optimal threshold for different images is different, and the automation degree is low. Therefore, we use the iterative method as the threshold segmentation method, and the flowchart of the iterative method is shown in Figure 3.  
First, the gradient image after non-maximum suppression is divided into two parts, and the initial threshold \(T_{0}\) is set as:  
\[T_{0}=(M_{\text{max}}+M_{\text{min}})/2, \tag{12}\]  
where \(M_{\text{max}}\) and \(M_{\text{min}}\) are the maximum and minimum values of the gradient image after non-maximum suppression, respectively. Then, the gradient image is divided into two parts by using this threshold, and the average gray values \(u_{1}\) and \(u_{2}\) of the two parts are calculated. The new threshold \(T_{1}\) is obtained as:  
\[T_{1}=(u_{1}+u_{2})/2, \tag{13}\]  
Compare the initial threshold \(T_{0}\) and the new threshold \(T_{1}\); if the absolute value of the difference between the two thresholds is less than the given value, the algorithm ends, and the newly obtained threshold is the final iteration threshold of the algorithm. Otherwise, assign the new threshold \(T_{1}\) to the initial threshold \(T_{0}\), then divide the image into two parts and repeatedly calculate the average gray level of the two parts until the difference between the thresholds is less than the given value; the value of \(T_{n}\) is the threshold of the gradient image, denoted as \(T\).
## 4 Conclusions  
Overall, the IRMS and DNA-SIP analyses clearly demonstrated the predominance of iron-reducing bacteria as the primary drivers of nitrogen fixation in native paddy soil and the enhancement of their nitrogen fixation by iron application. To be specific, (1) in native paddy soil, atmospheric nitrogen was incorporated into the soil primarily by diazotrophic iron-reducing bacteria belonging to _Geomonas_, _Anaeromyxobacter_, and _Geobacter_. (2) In the paddy soil treated with iron, the amount of nitrogen incorporated into the soil increased as a result of enhanced biological nitrogen fixation. (3) Nitrogen fixation by iron-reducing bacteria belonging to _Geomonas_, _Anaeromyxobacter_, and _Geobacter_ and possibly by iron-reducing _Bacteroides_ was enhanced by iron application, and these bacteria were the primary drivers of nitrogen fixation in iron-applied paddy soil. (4) In addition to these iron-reducing bacteria, _Anaerolinea_, _Cyanobacteria_, and Candidatus _Solibacter_ are active nitrogen fixers in paddy soil. This study provided a novel picture of active nitrogen-fixing microorganisms dominated by diazotrophic iron-reducing bacteria in paddy soil, and directly proved the effectiveness of iron application to enhance their nitrogen fixation and to increase the incorporation of atmospheric nitrogen into soil. The enhancement of biological nitrogen fixation in paddy fields by iron application may lead to novel and unique paddy soil management strategies to increase soil nitrogen fertility and ensure rice yields with reduced nitrogen fertilizer input and lower environmental nitrogen burdens. To achieve this, further studies are necessary to clarify the effects of iron application on rice growth and yields and the possible reduction of nitrogen fertilizer in paddy fields with different types of soil and environmental conditions. It will also be important to investigate the persistence of the effects of iron applied to soil.  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed the experiment. [PERSON] performed most of the laboratory work and the data analysis. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was supported by JSPS KAKENHI Grant Numbers JP20H05679, JP20H00409, JP20K15423, JP18K19165, JP18K14366, and JP17H01464 Japan, the Canon Foundation, and JST (Japan Science and Technology Agency)--Mirai Program Grant Number JPMJMI20E5. [PERSON] thanks the financial support from the China Scholarship Council (CSC).  
Not applicable.  
The nucleotide sequences generated by amplicon sequencing in this study have been deposited in SRA (Bioproject: PRJNA881308).  
We greatly appreciate the technical staff at Niigata Agricultural Research Institute working on field management. The measurement by the IRMS was performed by [PERSON], Center for Asian Bioresources and Environment, the University of Tokyo.  
The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 4 Experimental Results
### Comparing Algorithms
#### 4.1.4 Gradient Boosting Machine Results  
Gradient Boosting Machines (GBMs) are ensemble learning methods that build models sequentially, with each new model correcting errors made by previous ones, thus improving predictive accuracy.  
Figure 9(a) displays the GBM's training phase performance, showing the relationship between actual and predicted 'Time Factor' values, Figure 9(b) represents the GBM's performance during testing, illustrating its predictive capability, while Figure 9(c) shows how well the GBM model performs on validation data, emphasizing its accuracy.  
The Gradient Boosting Machine (GBM) regression model achieved a Mean Squared Error (MSE) of 2589.08 and a Root Mean Squared Error (RMSE) of 50.88, reflecting relatively low prediction errors. The Mean Absolute Error (MAE) is 36.89, and the Median Absolute Error (MedAE) is 28.54, indicating moderate accuracy. The model's Mean Bias Deviation (MBD) is also close to zero 2.57, indicating minimal bias.  
GBM provided robust predictions with relatively low errors, indicating its strong potential for accurate predictive modeling in this context, as seen in Table 6. The sequential boosting allowed the model to learn from previous mistakes, improving overall performance. However, the model's complexity necessitated careful tuning of hyperparameters to avoid overfitting.  
Figure 9: ANFIS Time Factor performance.