esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
###### Abstract  
Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power transmission, with precise insulator positioning being a prerequisite for successful detection. To overcome challenges such as intricate insulator backgrounds, small defect scales, and notable differences in target scales that reduce detection accuracy, we propose the AC-YOLO insulator multi-defect detection network based on adaptive attention fusion. To elaborate, we introduce an adaptive weight distribution multi-head self-attention module designed to concentrate on intricacies in the features, effectively discerning between insulators and various defects. Additionally, an adaptive memory fusion detection head is incorporated to amalgamate multi-scale target features, augmenting the network's capability to extract insulator defect characteristics. Furthermore, a CBAM attention mechanism is integrated into the backbone network to enhance the detection performance for smaller target defects. Lastly, improvements to the loss function expedite model convergence. This study involved training and evaluation using publicly available datasets for insulator defects. The experimental results reveal that the AC-YOLO model achieves a notable 5.1% enhancement in detection accuracy compared to the baseline. This approach significantly boosts detection precision, diminishes false positive rates, and fulfills real-time insulator localization requirements in power system inspections.  
deep learning: image processing; insulator; defect detection; YOLOv5 2023
## 2 Analysis of the State of the Art
### Device Models  
There are many and different approaches in the literature for devices modeling. Devices capabilities might determine the boundaries of an adaptation process or the consumable resources. [PERSON] and [PERSON] is considered that an independent device model would probably reduce adaptation process efforts for different context situations [62]. In fact, the World Wide Web Consortium (W3C) reinforces this perspective by warning about the wide range of device capabilities and sizes [63] which define the boundaries of the content that each device can handle. Several techniques, like Device Descriptors, content transformation guides, devices APIs and CC/PP systems help developers to optimize the user experience. These modeling techniques are analysed in the following section.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 5 Simulation Test
### Sensitivity-Active Power Imbalance  
The sensitivity tests are performed based on both the suggested default settings and the stable settings, as following: \(\Delta Ang\_set=1.0^{\circ}\), \(\sum Ang\_set=18^{\circ}\), \((df/dt)\_set=0.5\) Hz/s (default settings); \(\Delta Ang\_set=2.0^{\circ}\), \(\sum Ang\_set=45^{\circ}\), \((df/dt)\_set=1.0\) Hz/s (stable settings).  
Before the islanding condition, the reactive power was well balanced between SM and local load. The active power imbalance each varied from 50% surplus to \(-50\%\) deficit. Totally, 12 test cases were created. For all test cases, the trip times of composite PAD solution with default settings and the stable settings, the minimum value of three voltage phase angle changes, the duration when ROCOF value was larger than 0.5 Hz/s and the duration when ROCOF value was larger than 1.0 Hz/s, were recorded, as shown in Table 3.  
The minimum angle change values from the simulation tests were compatible with Equation (3). The angle setting in the conventional VVS relay was usually no less than 6.0\({}^{\circ}\). In that case, the conventional VVS relay was not sensitive and the non-detection zone was worse than 40% power imbalance. Besides, the VVS relay usually was susceptible to initiate nuisance tripping due to  
Figure 8: (**a**) System frequency measurement change; (**b**) maximum and instantaneous rate of change of frequency (ROCOF) values before and after loss of main grid.  
Figure 7: (**a**) Angle change; (**b**) accumulated phase angle drift before and after loss of main grid.  
external system fault. Hence, the conventional VVS relay cannot meet the technical requirements of islanding detection.  
From Table 3, even if the power imbalance was as high as 50%, the duration when the value of ROCOF was larger than 1.0 Hz/s was only 480 ms. So in this test environment, based on the recommended settings 1.0 Hz/s and 500 ms time delay for ROCOF relay from G59 [23], ROCOF relay cannot detect the islanding condition with 50% power imbalance.  
With the default settings, the non-detection zone of the proposed PAD solution was less than 10% power imbalance. If the local load is very close to the generation, this proposed solution cannot detect the islanding quickly.With the stable settings, the non-detection zone of the proposed PAD solution is less than 20% power imbalance, which is much better than the existing passive islanding detection methods VVS relay and ROCOF relay. Besides, the tripping time of PAD method is much faster than ROCOF relay with 0.5 s time delay.
## 7 Discussion
### Effects of Driver's Adherence to TWM  
In our previous works [12; 34], a driver's adherence to TWM was already identified as a critical factor for TWM success, both in static assignment and in dynamic behavior using econometric models of utility functions and fuzzy logic.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Figure** & **Dj-kSP** & **Disjoint Criterion** & \(\mathbf{\phi}\) & **Edge Policy** & **Adherences** & **Comments** \\ \hline Figure 9 & 3 & Edge costs & 60\% & minWeight & 25\%, 50\%, 75\%, 100\% & Compare with kSP assignment \\ \hline Figure 10 & 3 & Edge costs & 60\% & maxStretch & 25\%, 50\%, 75\%, 100\% & \\ \hline Figure 11 & 6 & Edge costs & 60\% & maxWeight & 25\%, 50\%, 75\%, 100\% & \\ \hline Figure 12 & 6 & Edge costs & 30\% & maxStretch & 25\%, 50\%, 75\%, 100\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: MTTS Figures index.  
Figure 12: MTTS for MLT scenario with 30% disjoint level, “Edge-costs” disjoint policy, 6-kSP and maxStretch edge discard. (UE(*), estimated user equilibrium) (Created with Matlab R23A [71]).  
The effectiveness and impact of Dj-kSP-based TWMs depend on the adherence factor \(\psi\) as it is shown in Figure 9. Our experiments covered driver adhesion based on quartiles with \(\psi=25\%\), \(50\%\), \(75\%\), \(100\%\).  
Even with low adherence of \(25\%\), the traffic assignment using TWM significantly impacts the overall traffic scenario since the vehicles that use alternative paths relieve the most congested links (Table 5). As the level of adherence increases, the impact on traffic assignment becomes greater, though not linearly, as the alternative paths also raise their occupation status, and their edge costs depend on their occupancy. In Figures 9 and 10 we observe how \(75\%\) of drivers' adherence approaches the minimal MTTS marked by Dj-kSP(opt). A complete adherence scenario hardly improves MTTS, or even worsen it, as in the case of maxWeight in Figure 11.  
The effect of optimal TWM distribution depends on the number of path flows \(numPF\), which in turn depends on the number of flows and the number of Dj-kSP in the product \(F*K\). As can be seen from the diagrams, TWM(Dj-kSP) provides sufficient variety and diversity of routes for the flows so that the optimal distribution could be providing more improvement, making the approach much more interesting for its efficiency.  
Figures 13-15 show the route dispersion for the flows in three snapshots of concrete traffic assignments with different adherence levels of \(25\%\), \(50\%\), and \(100\%\). These routes are assigned to the vehicles using the TWM map set considering the edge occupancy. In \(25\%\) adherence, few vehicles use the alternative routing with TWM, and therefore there is not a great variety of routes, obtaining \(45\) routes but with high usage in a few of them. The full adherence scenario induces disjoint paths that could be more optimal, obtaining \(41\) routes with dispersed usage. \(50\%\) adherence scenario shows how \(48\) routes are selected with high use.
###### Abstract  
Synthetic aperture interferometry (SAI) is a signal processing technique that mixes the signals collected by pairs of elementary antennas to obtain high-resolution images with the aid of a computer. This note aims at studying the effects of the distance between the synthetic aperture interferometer and an observed scene with respect to the size of the antenna array onto the imaging capabilities of the instrument. Far-field conditions and near-field ones are compared from an algebraic perspective with the aid of simulations conducted at microwave frequencies with the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) mission. Although in both cases the signals kept by pairs of elementary antennas are cross-correlated to obtain complex visibilities, there are several differences that deserve attention at the modeling level, as well as at the imaging one. These particularities are clearly identified, and they are all taken into account in this study: near-field imaging is investigated with spherical waves, without neglecting any terms, whereas far-field imaging approximation is considered with plane waves according to the Van-Citter Zernike theorem. From an algebraic point of view, although the corresponding modeling matrices are both rank-deficient, we explain why the singular value distributions of these matrices are different. It is also shown how the angular synthesized point-spread function of the antenna array, whose shape varies with the distance to the instrument, can be helpful for estimating the boundary between the Fresnel region and the Fraunhofer one. Finally, whatever the region concerned by the aperture synthesis operation, it is shown that the imaging capabilities and the performances in the near-field and far-field regions are almost the same, provided the appropriate modeling matrix is taken into account.  
imaging radiometry; antenna array; aperture synthesis; near-field; far-field Article  
A Comprehensive Comparison of Far-Field and Near-Field Imaging Radiometry in Synthetic Aperture Interferometry
## 2 Equipment and Data  
Gonggar Airport is located in the southeastern part of the Qinghai-Tibet Plateau, in the river valley area of the middle reaches of the Yarlung Zangbo River, at an altitude of 3570.8 m. The airport's runway is aligned parallel to the river valley, with an east-west orientation. The runway has a length of 5 km and is designated as runway 09 (with a magnetic heading of 89\({}^{\circ}\) at the western end) and runway 27 (at the eastern end). The airport primarily uses runway 09 for takeoffs and landings (Figure 1a).  
Since May 2023, an FC-III-type 3D DWL has been deployed near the touchdown zone of runway 09 (Table 1). Its wind measurement stability and accuracy have been validated, and it has accumulated extensive application experience in the airport environment [18; 19],  
Figure 1: (**a**) The surrounding topography of Gonggar Airport. The white dot indicates the location of the DWL installation, and the arrow marks the airport’s primary landing direction. The numbers 09 and 27 represent the runway designations. (**b**) FC-III-type DWL.  
demonstrating it to be mature and reliable technology. To comprehensively obtain wind field information, the DWL alternately performs Doppler beam swinging (DBS) scanning (with a scan cycle of 85 s), plan position indicator scanning at elevation angles of 4\({}^{\circ}\) and 6\({}^{\circ}\) (PPI mode, with a scan cycle of 120 s), glide path scanning (LNDCHL mode, with a scan cycle of 15 s), and range-height indicator (RHI) scanning (with a scan cycle of 50 s, conducted both parallel and perpendicular to the runway). Among these scan modes, except for DBS mode, which has a step size of 30 m and a maximum detection range of 5 km, all other modes have a step size of 100 m and a maximum detection range of 10 km. Due to a three-month calibration period from May to July 2023, during which the accuracy of the measurement data was validated, the system has been operating steadily since August 2023. Therefore, this study only utilizes data from August 2023 to April 2024, totaling 9 months. The signal quality of the DWL is related to the carrier-to-noise ratio (CNR). When the CNR falls below 2 dB, the signal quality and reliability are significantly affected. The DWL software (Version 2.7, developed by Sichuan Xiwu Laser Technology Co., Ltd., Chengdu, China) treats signals with a CNR below this threshold as missing values. In this study, the ratio of valid data points to the total number of data points along a scan line is defined as the detection efficiency [15], which is used to assess the DWL's performance regarding signal missing values and their impact at Gonggar Airport.  
In addition, this study also utilizes manual observations of low-cloud cover and automatic meteorological observation data from Gonggar Airport. The automatic meteorological data include parameters such as temperature, pressure, humidity, wind direction, wind speed, precipitation, and visibility.
###### Abstract
Therefore, a surface rescue process (SRP) of amphibious aircrafts for medium/distant maritime rescue based on mathematical methods is proposed in this study. This method combines the adaptive k-means clustering algorithm (k-means*) with the optimal genetic algorithm (GA*). In addition, to improve the effectiveness and applicability of this method in practical situations, this study considers the relative motion of the amphibious aircraft and of the PDTs, differing from the traditional rescue methods which are based on stationary position [36; 37]. Furthermore, the change in the PDTs' position and the change in the PDTs' health uncertainty are taken into consideration. In summary, a time-domain planning method (TPM) for the SRP of amphibious aircrafts in medium/distant maritime rescue, for the first time, is proposed in this study. To simulate the SRP of amphibious aircrafts, one common method is to develop a simulation environment. Most existing research of simulation methods includes agent-based modeling and simulation (ABMS), discrete event system specification, and the system dynamic model [38; 39; 40]. The amphibious aircraft surface rescue simulation environment is constructed based on ABMS to solve the impact of the time-domain results on the simulation results in this study. The amphibious aircraft agent and the PDT agent are built to simulate the SRP of the amphibious aircraft for medium/distant maritime rescue. The rest of this article is organized as follows: Section 2 introduces the TPM for SRP of the amphibious aircraft for medium/distant maritime rescue. Section 3 mainly describes the construction of the simulation environment based on ABMS. In Section 4, the rescue time and the successful rescue rates of the PDTs in a capsized ship are calculated based on the TPM for model verification. Finally, a summary of the article and the future work are given in Section 5.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 4 Multiple Feature Extraction Methods for Target Identification
### Gradient Direction Histogram (HOG) Feature Extraction Method  
HOG is a gradient direction histogram feature that is used to detect objects in image processing in the field of computer vision. HOG is a histogram feature formed by counting the gradient direction of each pixel in the local neighborhood of the image. The implementation method of HOG is as follows:  
Firstly, Gamma space and color space are normalized. In the texture feature of a ship image, the weight of the local surface exposure factor is large. Therefore, the Gamma compression algorithm can effectively reduce the local illumination variation of the image. The ship image is transformed into a gray scale, then the gamma compression formula \(I(\text{x},\text{y})=I(\text{x},\text{y})^{\text{gamma}}\) is adopted, where gamma is the compression parameter, and gamma = 0.5 is desirable. The result is shown in Figure 4, which shows the normalized Gamma space and color space of the ship images, which can reduce the influence of light factors.  
Then, the image is divided into small unit intervals that are called cell units. The direction histogram of the gradient of each pixel is obtained from the cell unit. Finally, the collected histograms are combined to form a feature descriptor. The image of ship HOG feature extraction is shown in Figure 5.  
From Figure 5, it can be seen that the information of the HOG gradient mainly exists in the edges of the image, which can describe the appearance and shape of the ship target with the edge or gradient directional density distribution. Therefore, the HOG features have good invariance to the geometric and optical changes of the image. In addition, under the conditions of strong local light normalization and rough spatial sampling and fine direction sampling, fine deformation can be allowed without affecting the detection result.  
Figure 4: Gamma compression image of the ship.
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 4 Experiment and Analysis
### Parameterization and Evaluation Indicators  
Setting a single-prediction model, combined-prediction model, and the method of this paper in the above experimental data, a comparison experimental analysis was conducted. The specific parameter settings of the supply quantity prediction model of this paper's method are shown in Table 3.  
To validate the performance of the supply quantity prediction model, the fit on the supply quantity test set was evaluated. In this paper, the prediction performance was comprehensively assessed using two indicators, the mean square error (MSE) and the mean absolute error (MAE), which were used to assess the difference between the predicted values of the model and the actual observed values. This is one of the most-used indicators for assessing the performance of regression models.  
\[MSE=\frac{1}{N}\underset{i=1}{\overset{N}{\sum}}(\hat{y_{i}}-y_{i})^{2} \tag{17}\]  
The mean absolute error is a common measure of the accuracy of a model's predictions, characterizing the average size of the absolute value of the difference between the model's output predictions and the actual values.  
Figure 5: Improvement of Informer model structure.  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}|\hat{y}_{i}-y_{i}| \tag{18}\]  
where \(y_{i}\) represents the true value, \(\hat{y}_{i}\) represents the predicted value, and N represents the sample size of the test set.
# Investigation into the Construction Response of Tunnels through Fault: Model Test
## 3 Design of the Model Test
### Preparation of Test Materials  
The model test conducted in this study has a similarity ratio of 1:30. To simulate the rock mass material in the test, a mixture of clay, fine sand, water, and petroleum jelly was used. For the fault structure, a mixture of medium coarse sand, water, and petroleum jelly was used. The final material parameters of the simulation are presented in Table 2.  
Figure 4: Test apparatus.  
To represent the primary support and secondary lining of the tunnel, a mixture of gypsum, water, and barite powder was used in a ratio of 1:1.1:0.1. The designed dimensions of the support model were 40 cm in width, 34 cm in height, and 2.5 cm in thickness. The support model was prepared in advance by pouring the mold, as shown in Figure 7.  
As per the experimental setup, the tunnel lining model was poured one month prior to the test. The formwork was removed once the lining strength reached a certain level. Subsequently, the prepared lining model was placed in a well-ventilated area with adequate lighting to allow the lining model to reach the required strength. The material parameters used in the model test are listed in Table 3. The initial determination of the material parameters was based on a similarity ratio of 1:30 and the final determination was carried out through material testing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Rock Mass** & **Bulk Density** & **Elasticity Modulus** & **Poisson’s** & **Frictional** & **Cohesion** \\  & **Classification** & **\(\gamma\) (kN/m\({}^{3}\))** & **\(E\) (MPa)** & **Ratio \(\mu\)** & **\(\varphi\) (\({}^{\circ}\))** & **\(\alpha\) (MPa)** \\ \hline Prototype & IV & 20–23 & 1.3–6 \(\times\) 10\({}^{3}\) & 0.3–0.35 & 27–39 & 0.2–0.7 \\ rock mass & V & 17–20 & 1–2 \(\times\) 10\({}^{3}\) & 0.35–0.45 & 20–27 & 0.05–0.2 \\ \hline Model & IV & 20 & 67.5 & 0.35 & 27 & 0.2 \\ rock mass & V & 18 & 33.5 & 0.4 & 30 & 0.1 \\ \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical parameters of rock mass.  
Figure 5: Schematic diagram of the model test.  
Figure 6: Schematic diagram of the measuring point distribution.  
During the model test, the rock mass material was prepared by blending the above-mentioned proportions and filled in the model box layer by layer. Each layer was compacted to a standard density of 2000 kg/m\({}^{3}\). Multi-point extensometers and earth pressure cells were set in the rock mass at positions L1 and L5, in accordance with the test scheme, as shown in Figure 8a. Furthermore, a simulated fault was set at different dip angles according to the designed scheme when the mixed material was filled above the tunnel vault to a depth of about 2 cm, as depicted in Figure 8b. Once all of the rock mass material had been filled, dial gauges were fixed on the top cross bar of the model box to measure the surface displacement, as shown in Figure 8c. Finally, after all preparations were completed, the initial readings of each sensor element were recorded, and the surrounding rock model was then ready for testing, as illustrated in Figure 8d.
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 3 Distributed State Estimation Algorithm with False Data Injection Attacks
### Hybrid Consensus
The information consensus is as follows:\[\Omega_{i}^{t+1}(k|k) = \sum_{s\in E_{i}}\pi^{s,i}\Omega_{s}^{t}(k|k) \tag{42}\] \[\mathbf{Y}_{i}^{t+1}(k|k) = \sum_{s\in E_{i}}\pi^{s,i}\mathbf{Y}_{s}^{t}(k|k) \tag{43}\]  
To improve the estimation accuracy, we combined the CM and CI methods to obtain a hybrid consensus algorithm, in which each group broadcasted its information \(\{\mathbf{i}_{i}(k),\mathbf{I}_{i}(k)\}\) and \(\{\Omega_{i}(k|k-1),\mathbf{Y}_{i}(k|k-1)\}\) to its neighbors and received information \(\{\mathbf{i}_{s}(k),\mathbf{I}_{s}(k)\}\) and \(\{\Omega_{s}(k|k-1),\mathbf{Y}_{s}(k|k-1)\}\) from its neighbors \(s\in E_{i}\). Then, we employed the consensus approach to the exchanged information:  
\[\Omega_{i}^{t+1}(k|k-1) = \sum_{s\in E_{i}}\pi^{s,i}\Omega_{s}^{t}(k|k-1) \tag{44}\] \[\mathbf{Y}_{i}^{t+1}(k|k-1) = \sum_{s\in E_{i}}\pi^{s,i}\mathbf{Y}_{s}^{t}(k|k-1)\] (45) \[t_{i}^{t+1}(k) = \sum_{s\in E_{i}}\pi^{s,i}t_{s}^{t}(k)\] (46) \[\mathbf{I}_{i}^{t+1}(k) = \sum_{s\in E_{i}}\pi^{s,i}\mathbf{I}_{s}^{t}(k) \tag{47}\]  
Therefore, each group updates its information state and information matrix:  
\[\mathbf{Y}_{i}(k|k)=\mathbf{Y}_{i}(k|k-1)+\rho_{i}(k)\mathbf{I}_{i}^{T}(k)\]0 \[\mathbf{Y}_{i}(k|k)=\mathbf{Y}_{i}(k|k-1)+\rho_{i}(k)\mathbf{I}_{i}^{T}(k)\]1  
**Remark 2**. : _Note that Equations (45) and (46) are a combination of the CM and CI methods. The combination of the prior information is maintained to ensure fast consensus, and the consensus on measurements is maintained to alleviate the weight deficiency caused by the CI method. In addition, note that if no attack exists in the system, the novel hybrid consensus method becomes the traditional HCMCI method._  
The complete DSE algorithm with FDI attacks is summarized in \[\mathbf{Y}_{i}(k|k)=\mathbf{Y}_{i}(k|k-1)+\rho_{i}(k)\mathbf{I}_{i}^{T}(k)\]2