esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this paper, an improved distributed minimum variance distortionless response (IDMVDR) beamforming method is proposed to realize the bird audio enhancement, in which the continuous spectrum update algorithm is introduced to estimate the noise PSD, and then the average metropolis weight algorithm is proposed to update each signal of all nodes to converge to the optimal solution of the centralized beamformer. At last, simulation experiments with bird audios were performed to validate the effectiveness of the proposed method. This paper is organized as follows. In Section 2, firstly, the optimal centralized beamformer is discussed. Then, the average metropolis weight local average consensus algorithm is briefly reviewed. Subsequently, the distributed MVDR (DMVDR) beamformer is described in detail. Finally, the improved noise PSD algorithm is stated. After that, the iterative procedures of IDMVDR are listed. In Section 3, the simulation experiments, results, and analysis of the convergence rate and noise reduction performance are presented. Conclusions are drawn in Section 4.
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Evaluation and Discussion
#### 3.4.2 The 5-Fold Cross-Validation  
Furthermore, we use 5-fold cross-validation to demonstrate the generalization ability of the models. After dividing the datasets into five parts on average, we choose each part as the testing set and the rest as training set. The detection accuracy of training is shown in Table 8 while that of testing is shown in Table 9.  
As shown in Tables 8 and 9, SSENets achieves the highest average detection accuracy in both training and testing. In order to make the data more intuitive, we use a histogram to draw the results of the 5-fold cross-validation. The histograms are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Sensitive** & **Specificity** & **F\({}_{1}\) Score** \\ \hline SSENets & 97.77\% & 95.45\% & 100\% & 95.83\% & 97.67\% \\ Xu’s Model & 96.37\% & 93.94\% & 100\% & 91.66\% & 96.88\% \\ Resnet18 & 93.56\% & 88.46\% & 100\% & 89.96\% & 93.88\% \\ Resnet34 & 94.89\% & 89.47\% & 100\% & 90.91\% & 94.44\% \\ Resnet50 & 95.71\% & 93.33\% & 100\% & 88.89\% & 95.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluate results of different models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **1** & **2** & **3** & **4** & **5** & **AVG** \\ \hline SSENets & 99.28\% & 99.90\% & 94.59\% & 99.79\% & 99.69\% & 98.65\% \\ Xu’s Model & 98.04\% & 99.28\% & 93.92\% & 99.59\% & 99.28\% & 98.02\% \\ Resnet18 & 99.07\% & 99.49\% & 87.44\% & 99.79\% & 99.59\% & 97.07\% \\ Resnet34 & 98.76\% & 99.17\% & 84.34\% & 99.59\% & 99.79\% & 96.33\% \\ Resnet50 & 99.48\% & 99.49\% & 91.97\% & 99.69\% & 99.28\% & 97.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The 5-fold cross-validation of training.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **1** & **2** & **3** & **4** & **5** & **AVG** \\ \hline SSENets & 98.10\% & 99.18\% & 88.57\% & 99.79\% & 99.79\% & 97.09\% \\ Xu’s Model & 94.74\% & 97.94\% & 79.81\% & 99.79\% & 98.66\% & 94.19\% \\ Resnet18 & 95.47\% & 92.89\% & 79.81\% & 99.48\% & 98.89\% & 93.31\% \\ Resnet34
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 4 Experiment
### Comparative Experiments
This improvement highlights the FAM's role in enhancing accurate segmentation and the model's ability to localize change regions more precisely. Further results showed that when both the HFAM and SDAM were used, performance reached new heights: the IoU was 88.213%, precision was 91.936%, the recall was 95.611%, and the F1 score was 93.737%. This combination demonstrates that the HFAM and SDAM complement each other, enhancing both accuracy and the model's comprehensive perception of the change regions. When all three modules--the HFAM, SDAM, and FAM--are used together, the model's performance reaches its optimal state: the IoU is 90.263%, precision is 94.605%, the recall is 95.161%, the F1 score is 94.882%, OA is 99.714%, and the Kappa is 94.735%. This configuration results in high overall accuracy and Kappa while also achieving optimal values in key metrics such as the IoU, precision, recall, and F1 score. The improvement in the F1 score demonstrates the model's excellent balance between precision and recall, with strong change detection capability and a low false alarm rate. In summary, the ablation experiments show that combining the HFAM, SDAM, and FAM significantly improves AMMNet's performance in change detection. Each module positively impacts different performance metrics, particularly the IoU, precision, recall, F1 score, OA, and Kappa, greatly enhancing model accuracy and stability. This suggests that combining different attention mechanisms enhances the model's fine-grained recognition and robustness in complex scenarios.
## 3 Deterioration Features and Data Description for East Japan
### Data Arrangement and Description
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Name of Variable** & **Unit/Code** \\ \hline \multirow{2}{*}{Objective variable} & Bridge age & Year \\ \cline{2-3}  & Event & 0 = rating a and b \\  & & 1 = rating c, d, and e \\ \hline \multirow{4}{*}{Numerical covariates} & \multirow{4}{*}{Number of vehicles} & Unit: vehicles per day (whole cross section) \\  & & Mean: 14,401.73 \\  & & Standard deviation: 8260.05 \\ \cline{2-3}  & \multirow{4}{*}{Span length} & Unit: m \\  & & Mean: 38.57 \\  & & Standard deviation: 10.15 \\ \cline{2-3}  & \multirow{4}{*}{Slab thickness} & Unit: cm \\  & & Mean: 22.39 \\  & & Standard deviation: 1.66 \\ \cline{2-3}  & \multirow{4}{*}{Winter precipitation} & Unit: mm per season \\  & & Average precipitation from December to \\  & & February \\  & & Mean: 287.34 \\  & & Standard deviation: 178.83 \\ \hline \multirow{4}{*}{Categorical covariates} & \multirow{4}{*}{Crossing condition} & River: crossing over river and pond \\  & & Overpass: crossing roads and railways \\  & & Unknown \\ \cline{2-3}  & \multirow{4}{*}{Edge} & Yes: the panel is at the edge of the slab \\  & & No: the panel is at the centre of the slab \\ \cline{2-3}  & \multirow{4}{*}{Near expansion} & Yes: the panel is near the expansion joint \\  & & No: the panel is not near the expansion joint \\ \cline{2-3}  & \multirow{4}{*}{Slope} & Below 1.0: slope less than 1.0\% \\  & & Over 1.0: slope more than 1.0\% \\  & & Unknown \\ \hline \multirow{2}{*}{De-icing salt} & Below 20: less than 20 t/km \\  & & Over 20: more than 20 t/km \\ \hline \multirow{2}{*}{Waterproof layer} & Yes: installed waterproof layer \\  & No: not installed waterproof layer \\ \cline{1-1} \cline{2-3}  & & \\ \end{tabular}
\end{table} 
Table 2: Variables used in analysis in East Japan. In addition, as stated earlier, the inspection years of the data were 2003 to 2013, which is more than 20 years later than the construction years. Thus, some groups constructed earlier show longer survival time distribution. To solve this problem, the Cox stratified model was introduced to the analysis. This model allows a factor to be adjusted for without estimating its effect. The construction year was stratified in this analysis. For parameter estimation, if the data are stratified for g groups, the likelihood function can be written as follows:  
\[L(\beta)\ =\prod_{g=1}^{G}L_{g}(\beta) \tag{9}\]  
\(L_{g}(\beta)\) is the likelihood from stratum \(g\).
# Dimensioning of Reactive Power Compensation in an Autonomous Island System
## 5 Dynamic Security Analysis
### Generator Loss  
The simulated disturbance consists of a three-phase fault at the HV side of the step-up transformer of the generator with the highest reactive power absorption capacity, i.e., one  
Figure 11: Sensitivity of optimal reactor size with respect to investment cost.  
of the new diesel units in S. Rhodes TPP. The fault is cleared in 100 ms with tripping of the transformer and generator. Two older WPPs (not complying with low voltage ride-through requirements) also trip in the simulation. The system is stable in all three cases, as shown in Figure 13, where the plots of voltage and frequency at the HV bus of the Soroni TPP are shown. The frequency nadir, however, ranges from approximately 49.5 Hz in the case with 4 MVAr compensation and six units in operation to less than 49.2 Hz in the case with 12 MVAr compensation and four units, as the system dynamic performance deteriorates with decreasing inertia, as expected. This results in 3.5 MW of load shed in the 12 MVAr case due to the activation of the UFLS scheme, while the latter is only marginally activated in the 8 MVAr case (1.1 MW load shed) and not activated at all in the 4 MVAr case. As a re-tuning of the UFLS is considered necessary after the latest radical system changes, no load shedding at all may be achieved also in the 8 MVAr case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reactive Compensation** & \multicolumn{3}{c}{**Thermal Units Online**} & **Wind Power** \\ \cline{2-4}
**Level (MVAr, Inductive)** & **Soroni TPP** & **S. Rhodes TPP** & **Total Number of Online Units** & **Generation (MW)** \\ \hline
4 & Steam#1, ICE#1-#2 & ICE#6-#8 & 6 & 5 \\
8 & Steam#1, ICE#1 & ICE#6-#8 & 5 & 8 \\
12 & Steam#1 & ICE#6-#8 & 4 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: UC-ED results at minimum load conditions for three reactive compensation levels.  
Figure 12: Simulated HV bus frequency at Soroni TPP for a single diesel unit trip.  
Figure 13: HV bus voltage (**a**) and frequency (**b**) at Soroni TPP—generator trip.
## 3 Methodology  
### Feature Extraction Methodology  
Feature extraction is the key to establishing the relationship between flow patterns and signals, and it is also the basis of machine learning technology to distinguish and recognize flow patterns. Appropriate feature extraction is important to developing flow pattern identification technology. In this paper, statistical methods and empirical modal analysis (EMD) technology are used to extract the time domain and time-frequency domain characteristics of the signal.
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 1 Introduction
Vietnam has two disaster databases; the first database is maintained by the Vietnam Disaster Management Authority, and the information is published in Vietnamese. The second Vietnamese database is maintained by the General Statistics Office of Vietnam (Supplementary Table S5). No centralized depository for loss data was found for the other Southeast Asian countries. Among the national databases, only four databases reported agricultural losses: NDRRMC for the Philippines, DIDB for Indonesia, and the two Vietnamese databases. The NDRRMC database reported economic losses in Philippine peso (PHP), while DIDB and the two Vietnamese databases reported areal losses in hectares. The inconsistent reporting format made it difficult to make direct comparisons. To address the unavailability and the non-standardization of agricultural loss data, we first compiled a list of typhoons that came within 500 km of Vietnam and the Philippines. Thereafter, we collated information on rice-related damage from typhoons from published databases and other online sources to create a database with information on (i) the size of rice area damaged, (ii) the volume of rice production lost, and (iii) the value of rice crop lost. We compiled data from 1970 to 2018, and we focused on Vietnam and the Philippines, as these two Southeast Asian countries are frequently affected by typhoons [12; 13]. As different crops respond differently to wind and rain [4], we limit the scope of our research to rice as it is a major staple food in these two countries, and agricultural losses may affect local and regional food security [14]. In both countries, rice fields have been repeatedly damaged by typhoons [15; 16; 17], but the areas that are prone to agricultural damage remain unclear, while the overall magnitude of damage on a national level is uncertain. This is partly because research on crop losses tends to be event-specific and data from multiple events were rarely aggregated and analyzed as a whole [12; 18]. By building a comprehensive database on rice damage in two countries, we hope that the information will allow vulnerable hotspots to be identified so that risk reduction effortslike the planting- of lodging-resistant varieties can be considered [6]. In addition, insurance and financial companies can use the loss data for risk analyses, developing risk transfer products, and determining compensation payouts [19]. Lastly, we used the data we have compiled to assess how typhoon intensity in terms of landfall wind speeds had influenced rice damage to provide insights on how typhoon characteristics may influence the extent of rice damage.
## 3 Application Domains
### Quality Control in Manufacturing  
As introduced above, data collected during asset utilization monitoring can be analysed to improve the quality of industrial processes, e.g., through fault detection, predictive maintenance and energy consumption monitoring. Accordingly, critical conditions will be detected and _real-time yield optimization_ solutions will be identified [31]. _Statistical Process Control_ (SPC), _Advanced Process Control_ (APC) and _Six Sigma_ strategies can be used on such data to enhance process control [32]. Big data will play a key role, as _advanced analysis techniques_ will be used to compare and interpolate data collected from sensors with related information that is made available on the cloud, so that more efficient solutions can be elaborated [33]. For example, data collected while monitoring some unusual behaviour of a machine can be analysed with data on the cloud to find similar patterns that suggest possible solutions already implemented by others in similar situations.
# Compliant Detachment of Wall-Climbing Robot Unaffected by Adhesion State
## 1 Introduction  
Geckos are representative of animals with outstanding climbing abilities, and many kinds of wall-climbing robots have been developed to imitate the movement patterns of these animals. Among them, the robots based on dry adhesion have the advantages of low noise and low power consumption compared to the wall-climbing robots based on such things as vacuum suction cups [1], magnetic attraction [2, 3], and gripping claws [4] (shown in Table 1). Based on gecko setae, various structures and materials [5, 6, 7] with high adhesion strength and excellent durability have been developed. The adhesion of wall-climbing robots, such as Tankbot [8], Mini-Whegs [9], Waalbot II [10], and RiSE [11], has been extensively studied. Nevertheless, the wall-climbing ability of these robots is still not on a par with the gecko. To meet the needs of different driving force and load capacity, the adhesion state is different. At the same time, smooth adhesion and low fuselage disturbance are required during detachment, which requires the robot to be capable of flexible detachment under different adhesion states.  
Wall-climbing robots, such as Stickybot [12], Stickybot III [13], and CLASH [14], use an anisotropic adhesive. The normal adhesion force can be controlled by adjusting the tangential force to realize stable detachment. Large normal preloading, required for stable adhesion, can be achieved by adding a long tail that exerts normal pressure on the foot. For the symmetrical robots AnyClimb [15] and AnyClimb II [16], the left and right adhesion units perform simultaneous detachment in the detachment stage, so that the rolling moments generated by detachment cancel each other, thus reducing their influence on robot stability. However, this kind of robot has requirements regarding thesynchronization of these rolling moments. RiSE [11] uses force tracking to achieve stable vertical climbing on rough, but not smooth, surfaces.  
The above studies were limited by the ease of detachment, which can be improved via the online regulation of adhesion strength. Related studies mainly focused on gecko-inspired directional adhesion [6; 17; 18], micro-vibrations [19], and adjustable peeling behavior [20; 21; 22; 23]. Peeling regulation is a universal solution. But the adhesion state is influenced by many factors, making it difficult to predict, and it places a high requirement on the movement control of the wall-climbing robot.  
Based on the variable flexible joint motion of animals, we propose an online impedance adaptive controller to realize the active control of adhesion peeling angle. A gecko-mimetic dry-adhesion robot IBSS-8 is built to validate the proposed strategy. A three-dimensional strain platform for the peeling angle and an adhesion strength test are used to verify the ability of the proposed algorithm to regulate peeling behavior. The vibration performance of the robot on various surfaces with various slope angles and the climbing success rate are used to verify robot movement stability.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 2 From WBANs to BBNs
### Overview of WBAN  
A WBAN is a wireless body area network composed of various types of sensors that monitor the body functions. These sensors could be fixed on the body or could be implanted under the skin. The implanted ones are comfortable and do not disturb normal activities. Figure 1 illustrates an example of a patient monitoring system using a WBAN.  
The main tasks of the sensor nodes are to collect body activity information and to forward them to remote servers for analysis. Actuator sensors could also process the collected data and perform subsequently. A WBAN allows continuous health monitoring in real time and we can access to the medical records remotely. If an emergency is detected we can immediately inform the patient/the medical staff by sending messages or alarms.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### The Relevance of Cyber Resilience in Various Segments
#### 3.2.2 Critical Infrastructures  
Another segment where resilience is viewed with strong concern is critical infrastructures such as electricity, water treatment, and transportation. The growing adoption of cyber-physical systems in modern industrial plants for water treatment and distribution must consider the perspective of cyber resilience as a way of complementing a traditional assessment of physical resilience, this point of view being a central issue for critical infrastructures, considering the potential social and economic consequences that a disturbance can represent [38]. Risks associated with the integration of the computational, communication and physical aspects of critical cyber infrastructures, whose potential risks associated with failures affect the guarantee of resilience [39]. As the world becomes increasingly interconnected, the use of and dependence on Internet of Things (IoT) technology and other self-adaptive systems increases, requiring greater cyber resilience of critical infrastructures, at the risk of catastrophic impacts on society as a whole, due to society's growing dependence on these systems [40]. Detecting, preventing, and mitigating cyber threats is a concern for most organizations, especially those in critical infrastructure sectors [41].
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with Hidden Nodes  
This section presents simulation results obtained by using the optical channel model based on directed LOS link configuration. Directed LOS link configuration results in the occurrence of hidden nodes in the network, due to the directivity of transceivers and limited optical transmission power of mobile devices (30 mW). It was found that, in all simulation scenarios, devices can only communicate with the coordinator. None of the devices in the network can detect the transmissions of any other device. Hence, in VLC networks with star topology employing directed LOS link configuration, all devices are hidden nodes to each other.
## 3 Implementation  
The key idea of the cooperative method is to share sensor data obtained from vehicles in different locations via V2V communications. Here, video images of forward vehicles are transmitted to host vehicles through DSRC technology, hence enhancing the ability of the host vehicle to see the occluded objects. The implementation involves five steps: (1) create 3D projection model between front and back vehicle views; (2) select feature pairs from paired images (front and rear vehicle image obtained synchronized); (3) obtain the depth map of the rear vehicle (host vehicle); (4) calculate and optimize the parameters in the affine transformation matrix; and (5) fuse images to augment the view of the host vehicle. All steps are described in the following sections.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 6 Characterizing the Key Recovery from ReRAM PUFs
### Latencies for the Key Recovery Protocols with ReRAM PUFs  
As part of the experiment presented above, we completed the full key recovery protocol, including the RBC-light. When the Hamming distance exceeded one, an additional key generation cycle from the PUF was performed to enable the recovery of the initial 256-bit long keys. A total of 5000 key recovery cycles were performed to quantify latencies. The results are as follows, as shown in Figure 16:  
* The average latency to recover 4290 keys without error is 2.11 s, which is mainly due to the time it takes to read the 256 addresses from the pre-formed PUFs.
* The average latency to recover 643 keys with one error is 2.56 s. This includes an additional 40 ms for the RBC-light.
* The average latency to recover 58 keys with two errors is 7.3 s. The additional delays are due to the need to read the PUFs several times.
* The average latency to recover 8 keys with three errors rose to 10.6 s for the same reason.
* The average latency to recover the last key with four errors was more difficult and took 35.1 s. The difficulty here was the necessity to handle several cells that had responses that always differed from the initial response. We suspect that the initial read was noisy. This type of problem can be resolved by reading the key, multiple times, during the initial cycle and erasing the bad ones. However, in most use cases, a latency of 35 s every 5000 cycles is perfectly acceptable.  
Despite the fact that the hardware that we designed for this study is far from being optimized in terms of stability, noise, and latency, the overall performance of the key recovery schemes is in line with the study objectives. With this protocol, 100% of the 5000 keys were recovered, with an average latency of 2.25 s. Considering that the RBC-light, with a Hamming distance of one, takes less than 40 ms, the bulk of the delays were due to the response generated by the PUF, which takes about 2.0 s.  
Figure 15: Plotting the occurrence of erratic 256-bit long keys generated from the ReRAM PUFs. Out of 5000 key recovery cycles, 4290 keys had zero errors, 643 keys had one error, 58 had two errors, eight had three errors, and one key had four errors.