esa-sceva/satcom-chunk-collection huggingface dataset:
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 3 L\({}^{p}\)-Order Time-Frequency Spectra (LTFS)
### Synthetic 1D Channel Data Testing  
Figure 1(a) shows the instantaneous amplitudes of the synthetic 1D channel data in Figure 1(a), which are obtained by LTFS with \(p=10,f_{1}=0\) Hz, and \(f_{2}=f_{N}=500\) Hz. Figure 1(b)shows the instantaneous amplitudes of the synthetic 1D channel data in Figure 1a, which are obtained by LTFS with \(p=0.8,f_{1}=0\) Hz, and \(f_{2}=f_{N}=500\) Hz. Figure 2c shows the instantaneous amplitudes of the synthetic 1D channel data in Figure 1a, which are obtained by LTFS with \(p=0.6,f_{1}=0\) Hz, and \(f_{2}=f_{N}=500\) Hz. LTFS has successfully detected the abrupt and gradual changes no matter which order parameter is chosen, and the LTFS results become more and more sharp with decreasing order parameter \(p\).  
According to Figures 1 and 2, we can see that GHT and LTFS results with \(p=1.0\) are the same, while GHT and LTFS results with \(p=0.6\) are the same; in addition, LTFS results with \(p=1.0\) have much higher resolutions than GHT results with \(p=1.0\), which benefits from the sign function used in LTFS. Comparison of Figure 2a-c shows that the LTFS resolutions are increased with decreasing order parameter \(p\).
# Time-Dependency in the Corrosion Process of Reinforced Concrete Subjected to a Chloride Solution
## 2 Reviews for Corrosion Detection Techniques
### Electrochemical Impedance Spectroscopy  
Unlike traditional electrochemical measurements which only provide faradaic and non-faradaic components, EIS could provide all electrical characteristics of the interface over a wide range of frequencies. Thus, faradic responses can be extracted from non-faradic signals regarding all interface information when using EIS-based techniques.  
Impedance is the complex resistance created by a resistor, capacitor, and inductor when current flows through a circuit. A small voltage, generally 5-15 mV peak-to-peak, is applied to the electrode, and then the current response is recorded [7]. The reasons for using a small voltage are as follows: (1) the linear relationship between the potential and current in the electrode-electrolyte interface system; (2) the minimisation of system  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Open Circuit Potential** & **Corrosion Condition** \\
**(mV vs. CSE)** & **Corrosion Condition** \\ \hline \(<\)\(-\)500 & Severe corrosion \\ \hline \(<\)\(-\)350 & High (\(<\)90\% risk of corrosion) \\ \hline \(-\)350 to \(-\)200 & Intermediate corrosion risk \\ \hline \(>\)\(-\)200 & Low (10\% risk of corrosion) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Possibility of corrosion risk with corrosion potential according to ASTM C876.  
disruption in the concrete. High resolution is necessary to measure the current in a concrete sample. Therefore, an increase in the sensitivity of the measurement is unavoidable.  
[PERSON] et al. [21] were the first to use EIS to study the corrosion of steel in concrete. Although the equipment used has been improved technically, the interpretation of the impedance data remains relatively challenging. AC can be used for corrosion measurement to reduce system disturbance. However, it has limitations in evaluating the corrosion of steel in concrete, including the time-consuming use of lower frequencies, the presence of concrete, and the ambiguous responses. In addition, measurement sensitivity limits the practical application of these methods.
# Improved ISR for IEEE 802.11 ah Nonlinearity Compensation via Adjustable Constellation Borders
## 4 Performance Evaluation  
As the proposed scheme mainly contributes at 16-QAM, according to the analysis in the previous section, the effectiveness of the proposed scheme was verified through numerical results based on 802.11 ah CBW1 subcarrier mapping with optimized decision region (\(\alpha=0.73\)) for \(\mathcal{M}=16\) and \(\mathrm{IBO}=3\) dB. Figure 4 compares the BER performance of ISR-I, ISR-II, and the proposed scheme for different numbers of iterations (\(\mathrm{i}=1,~{}2,~{}5\)). It can be seen that the BER of the proposed scheme is slightly degraded compared to that of ISR-II for \(\mathrm{i}=1\) only but it outperforms ISR-I or ISR-II for \(\mathrm{i}>1\), especially when the SNR is increasing. This phenomenon is highly correlated with the SNR and is more obvious in the view of convergence, which can be observed in Figure 5. For \(E_{b}/N_{0}=30\) dB, the convergence of the proposed scheme is almost the same as that of ISR-II; for \(E_{b}/N_{0}=40,~{}50\), the converged BERs compared with ISR-II are remarkably improved with only one or two additional iterations. Although for \(E_{b}/N_{0}=50\), the BER of ISR-I can be stably reduced with an increasing number of iterations, its convergence is much slower than that of the proposed scheme. According to [19], the number of iterations for the converged BER dominates the complexity of receiver-based solutions due to the usage of FFT/IFFT pairs. There are usually one or two iterations in most schemes, except for in the case of ISR (e.g., [12; 13; 14; 15; 16]). In this example, the proposed scheme further enhances the converged BER to \(10^{-5}\) within five iterations, which consumes two to five times more complexity when compared with the consumption of other schemes. However, ISR aims to reduce the converged BER as much as possible when the high quality of time-domain knowledge (i.e., high SNR) can also contribute to the compensation, taking the processing time as a trade-off for the tolerant latency in IoT networks. From Figure 5, it can be observed that the proposed scheme significantly improves the convergence of ISR but does not increase the complexity.  
Figure 4: Comparison of BER performance for different ISR schemes (\(\mathcal{M}=16,\mathrm{IBO}=3\) dB).
## 3 Numerical Simulation of Underground Tunnel Vibration Waves  
Using the FEM-SPH algorithm to establish a numerical model of complex tunnels, combined with vibration wave data collected from blasting sites, we conduct spectral characteristic analysis on the vibration wave signals collected from stress-concentrated areas of the model. This involves identifying the extreme values in the spectral distribution diagrams, maximum plastic deformations, and stress-time history curves of measurement points. Analyzing the propagation effects of vibration waves on nearby tunnels and studying the impact of vibration shock waves on underground tunnel groups in the context of in situ stress environments, this research holds significance in understanding the propagation laws of vibration waves within rock masses under complex conditions. It can provide theoretical support for the safety verification of blasting engineering and offer references for parameters such as protective distances and explosive charges in blasting projects.
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 6 Discussion  
In Section 5, we show that the proposed tool treated the atomicity violations in the synthetic program with low overhead. The synthetic program was developed in an ARINC 653 environment called Simulated Integrated Modular Avionics (SIMA), which is common practice in the industry. We used synthetic programs because airborne software is proprietary and the industry does not disclose them for security reasons. Synthetic or not, airborne software is vulnerable to concurrency errors because the ARINC 653 operating system supports Pthread library. Moreover, it is impossible to guarantee that the treatment of atomicity violations is correct. Additionally, it is difficult to guarantee that a piece of code does not have side effects on other software components unless it is tested in a control environment.  
UAVs and drones are attracting many researchers in the field, because, unlike other airborne software, many of their source codes are available as opensource projects. These software types must be inspected for any latent errors because these projects do not always strictly follow the standards.  
In RAV, we manually inspected the accuracy of the interleaving set acquired in the test phase. Since it is manually inspected, this might increase the number of false positive and false negative cases when the code is complex or when the developer makes mistakes. For future work, we need to develop an automated tool that guarantees the integrity of the correct interleaving set.
## 6 GA-Based Slot Assignment Algorithm
### Encoding
#### 6.1.2 Initialization  
Each Individual of GA population has two chromosomes. At the beginning of the initialization, a data collection scheduler generates \(N_{QF}\) QFs such that all nodes in an IWSN are collated at random in order to encode a chromosome that represents an order of node IDs. We think that the random selection provides a search diversity. Our simulation experiment showed that it did not have a big impact on the performance. After that, a chromosome of slot type is assigned. For the slot type chromosome initialization, first of all, the first queries of all nodes to collect data while a periodic cycle are assigned. The assignment of slot type chromosome denotes 1 in the chromosome as shown in Figure 7. Then, the timing of transmitting queries for retries of the collection while a periodic cycle are assigned. The assignment denotes 2 in the slot type chromosome. The number of retries depends on a path quality (end-to-end PER) between a node and a central node. A central node gets the path quality from a network manager in an IWSN, because a network manager perceives network conditions. If a path quality between a node and a central node is 80%, a central node will require at least a retry. Finally, the slot type of other slots that are assigned to the node are 3. \"3\" means that unpredictable on-demand data traffic from central nodes can be generated at the slot.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 1 Introduction
At present, research on the moment-rotation model of DC joints is relatively lacking. Most similar research focuses on frame construction, which is worthy of interest. The literature [29] put forward a multi-parameter exponential model for semi-rigid joints. The literature [30] provided a three-parameter exponential model to describe the moment-rotation curves of top-and seat-angles with or without double web-angle connections. The literature [31] fitted a four-parameter moment-rotation model of rectangular steel frames. The literature [32] proposed a method to calculate the rotational deformation of semi-rigid end plate connections of steel structures. The moment-rotation curve can be calculated accurately using this method. As the chief semi-rigid joint in the internal support system, it is necessary to study the flexural performance and moment-rotation model of DC joints. In this paper, a study of the mechanical properties of DC joints under eccentric load for steel bracing in foundation pit engineering is reported. To begin, a finite element model was established based on the test results of axial compression experiments in the literature [22], and its accuracy and reliability were verified. Then, the influence of eccentricity and key position parameters on the bearing capacity and flexural performance of DC joints under eccentric load was studied through numerical simulation. Finally, a moment-rotation model for DC joints is proposed and the initial rotational stiffness and moment resistance of the numerical results are compared with the calculation results. At present, research on DC joints mostly focus on bearing performance under axial loads, while research on bearing performance, flexural performance and a moment-rotation model of DC joints under eccentric loads are lacking. Therefore, the research content of this paper is meaningful.
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis
### Evaluation Metrics  
The standard MS COCO [47] evaluation metric is used to evaluate the quantitative IS results. The IoU of each prediction result is defined as \(IoU=\frac{P_{mask}\cap G_{mask}}{P_{mask}\cup G_{mask}}\), which is calculated based on the intersection over union of the prediction result and the ground-truth result. Setting an a priori IoU threshold criteria, precision and recall values are then calculated accordingly:  
\[Precision=\frac{TP}{TP+FP}, \tag{14}\]  
\[Recall=\frac{TP}{TP+FN}. \tag{15}\]  
The experiments use the standard MS COCO metric to quantitatively and comprehensively evaluate the performance of the IS methods in the RS images. These metrics include the average precision \(AP\), \(AP_{50}\), \(AP_{75}\), \(AP_{s}\), \(AP_{m}\), and \(AP_{L}\). The average precision (\(AP\)) for a given \(IoU\) threshold can be gained by:  
\[AP_{IoU}=\int_{0}^{1}P(r)dr, \tag{16}\]  
where \(r\) represents the recall and \(P(r)\) represents the precision corresponding to the recall. Thus, \(AP_{50}\) and \(AP_{75}\) denote the results of the above equations for the \(IoU\) thresholds of 0.5 and 0.75, respectively. The \(AP\) value represents the average of the \(IoU\) for 10 thresholds, 0.50-0.95 (steps of 0.05), and can be written as:  
\[AP=\frac{1}{10}\sum_{IoU=0.5}^{0.95}AP_{IoU}. \tag{17}\]  
In addition, \(AP_{S}\), \(AP_{M}\), and \(AP_{L}\) mean to evaluate the model's mask prediction performance on small (<32\({}^{2}\) pixels), medium (>32\({}^{2}\) pixels), and large (<96\({}^{2}\) pixels) objects. These metrics provide a comprehensive assessment of the mask-prediction quality of the model.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 3 FLATSIM Architecture
### User Access Interface  
The public data access is made via the ForM@Ter website, on the \"Terre Solide\" metacatalog page ([[https://www.poleterresolide.fr/](https://www.poleterresolide.fr/)]([https://www.poleterresolide.fr/](https://www.poleterresolide.fr/)), accessed on 10 September 2021), which is a Vuejs (javascript) application embedded on a page of a Wordpress site. The CNES public delivery web service provides an Opensearch Application Programming Interface (API) which allows access to the catalog of distributed products. The metadata of the FLATSIM collections (for each study area) are recorded by the ForM@Ter meta-catalog in a Geonetwork catalog under the ISO19139 standard. Downloading and viewing products with warehouse management system (WMS) is reserved for authenticated ForM@Ter users. Authentication is done with the ForM@Ter single sign-on (SSO).  
In Figure 10, we display the requests made at the interface between the ForM@ter portal, the ForM@ter SSO, the Form@ter catalog, and the CNES FLATSIM services:  
* Request A: Geonetwork's Q Search API is used for user searches among collections. These searches can be spatial, temporal, by data center, open term, variable, or platform. The requests are xhttp requests, invisible to the user;
* Request B: The meta-catalog's Opensearch API allows searches among the granules of a FLATSIM collection. The requests are xhttp requests, invisible to the user;
* Requests C1 and C2 (User authentication for the metacatalog): C1, the user authenticates with the Keycloack SSO and is redirected with a code to the meta-catalog page; C2, the Vuejs meta-catalog application queries the SSO with the code to obtain an access token and user information (invisible to the user),
* Requests D1 to D4 (Authentication with FLATSIM): D1, the user authorizes FLATSIM to access his user data and is redirected to the meta-catalog page with a code for FLATSIM authentication; D2, the meta-catalog application transmits the code to the FLATSIM authentication service (invisible to the user); D3, FLATSIM's authentication service queries the SSO with the code, to get user data (invisible to the user); D4, the authentication service transmits a FLATSIM data access token to the meta-catalog application (invisible to the user);
* Request E: The user now has a FLATSIM access token, he can query the WMS Flatsim service by transmitting the token. The service checks that the token is valid with the FLATSIM authentication service and sends the images back (invisible to the user).
* Request F: The user now can request a download by transmitting the token. The service checks that the token is valid with the FLATSIM authentication service and then returns the archive (invisible to the user).
# Parameter Combination Framework for the Differential Evolution Algorithm
## 4 Two-Level Parameter Combination Framework
### Search Object with Specific Parameter Region and Strategy  
A concept of search object is defined to represent an algorithm based on a specific parameter strategy design and performed by an individual group. A search object includes information and search action.  
The information of the search object _STRinfo_ is a five-tuple: _STRinfo_ = (_opstrategy_, _pastrategies_, _group_, _environment_, _model_), where _opstrategy_ represents the evolutionary operation, _pastrategies_ is a set of parameter strategy (_pastrategy_) for different parameters of the operation, and _pastrategy_ is a five-tuple: _pastrategy_ = (_pa_, _type_, _num_, _domain_, _painfo_). Here, _pa_ is the parameters that the strategy deals with, _type_ is the parameter strategy type, _num_ is the strategy ID, _domain_ is the parameter scope, _painfo_ is the information for parameter strategy model. _group_ is the group of individuals to perform the evolutionary operation: _group_ = (_size_, _indist_, _character_). Here, _size_ is the group size, _indist_ is the individual set, and _character_ is the characteristics of the set to instruct the grouping. _environment_ is the environment of the searching group that generally represents the neighborhood scope, and _model_ is the evaluation information of the current strategy.  
The parameter strategy is designed as algorithm components such as jDE strategy, SHADE strategy, random strategy, and L-SHADE strategy for NP parameter and so on. The same type of strategies uses a uniform interface to facilitate combination with algorithm operation. The operation-related parameter strategy is divided into two phases. Phase 1 is used for collecting information and establishing the model, whereas phase 2 is used for assigning parameter values according to the model.  
The action of the search object is described in Algorithm 1.  
```
Input: _STRinfo_ Output: offspring set, _STRING_model_
1: According to the _STRinfo.group_, assign the individual set.
2: foreach parameter strategy in _STRinfo.pastrategies_
3: Execute the parameter component with phase 2 to generate the parameter value set.
4: end foreach
5: Execute the operation (_STRinfo.opstrategy_) and return the offspring set.
6: Compute the objective of the offspring.
7: foreach parameter strategy in _STRinfo.pastrategies_
8: Execute the parameter component with phase 1 to collect information and construct the parameter strategy model.
9: end foreach.
10: Collect the algorithm evaluation information to generate _STRinfo.model_.
```  
**Algorithm 1**Execute search object algorithm.
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 3 Design of Planning Algorithm for Cooperative Inspection
### Determination of the Parking Point Location  
Based on the mathematical optimization model established in Section 2, it is known that the objective function is the maximum value of operating time for each UAV, comprising two components: task operating time and round-trip time for task points. Therefore, to minimize the total task operating time, the selection method for parking points needs to consider these two aspects. As a classical location selection method, the barycentric method is often used to calculate the centroid coordinates of multiple homogeneous discrete points. Since this study needs to consider UAV operating time at task points, weights are assigned to each point during calculation, with task points having longer operating times assigned greater weights. The calculation method is as follows:  
\[\overline{X}=\frac{\sum\limits_{i=1}^{m}t_{i}X_{i}}{\sum\limits_{i=1}^{m}t_{ i}},\overline{Y}=\frac{\sum\limits_{i=1}^{m}t_{i}Y_{i}}{\sum\limits_{i=1}^{m}t_{i}} \tag{7}\]  
In Equation (7), \(\overline{X}\) and \(\overline{Y}\) are the horizontal and vertical coordinates of the calculated parking point, respectively, \(X_{i}\) and \(Y_{i}\) are the coordinates of task point \(i\), and \(t_{i}\) is the operating time of the UAV at task point \(i\).
# Resource Allocation for GBR Services in D2D-Enabled Communication
## 3 Resource Allocation for GBR Services  
First, the required channel bandwidth for an arbitrary matching (CU \(n\), DU \(m\), channel \(I\)) is derived together with the channel sharing mode requiring a smaller channel bandwidth. Then, the remaining 3-D matching problem is to find the optimal matching combination which minimizes the total channel bandwidth while guaranteeing the target data rate of individual users.  
Figure 2: Channel bandwidth, channel sharing mode, and matching combinations.
# Attention Transfer Reinforcement Learning for Test Case Prioritization in Continuous Integration
## 6 Results and Analysis
### RQ3: Execution Efficiency  
To quantitatively evaluate the execution efficiency of ATRL-TCP, we compared the prediction time and the model training time of the three methods across four datasets. The training time includes the calculation of the reward function and the time for the model to update parameters based on the obtained rewards. The prediction time includes the time for the model to determine the priority of each test case and sort them accordingly.  
As shown in Tables 6 and 7, RETECS has the shortest prediction time and model training time among the three methods, which we attribute to its simpler model structure. However, it is also evident that the difference in prediction time between ATRL-TCP and RETECS is generally acceptable. Even on the GSDTSR with the largest difference, the difference in prediction time is only a few tens of seconds. In the process of CI testing, such inference time is completely acceptable, especially considering that the sorting performance has been improved by about five times. Therefore, it can be concluded that the time overhead of ATRL-TCP is relatively small, while achieving better and more stable priority performance.