esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Stability Regions
To better understand the mechanism of stability loss, we proceeded to calculate the bifurcation diagram of the duty cycle in the interval \(V_{ref}\in[42\;V,50\;V]\) at fixed \(V_{in}=10\) V. As depicted in Figure 4B, we expect a stability change at \(V_{ref}\approx 42.9\) V. The bifurcation diagram of the duty cycle shows the transition to a Period 2 orbit at that same value, as shown with the red vertical line in Figure 6A. Such orbit remains stable, as testified by \(\lambda_{max}=0\) (see Figure 6B), up to the value \(V_{ref}\approx 43.54\) V, where the duty cycle starts saturating and producing seemingly chaotic values of \(d\). The chaotic nature of the solutions after this critical value of \(V_{ref}\) is indeed revealed by \(\lambda_{max}>0\) after this point. From the definition of \(\lambda_{max}\), it is possible to deduce that two nearby trajectories will be completely uncorrelated after a few milliseconds. The transition to chaos due to saturated \(d\) has been described previously as the route to chaos via skipped cycles in the literature [51]. Despite the fact that the chaotic regime is definitely undesired in any real application, it should be noticed that, nonetheless, the values of the error remain rather small (<2%) in the whole studied interval (see inset).
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 2 Related Work
However, most deep learning-based methods are largely focused on single-modal data, limiting the effective exploitation of complementary information between distinct modalities and failing to meet the requirements for physiological signal assessment in complicated real-world scenarios. Multimodal fusion methods aim to combine information from various modalities into a single one; the information contained in fused features will become more abundant. For example, RGB video contains richer information about physiological signals, and physiological signals in NIR video are less susceptible to changes in illumination. Therefore,many researchers have focused on using multimodal information to estimate heart rate. [PERSON] et al. [21] discussed a method for estimating HR from RGB and NIR videos that automatically selects relevant facial regions in both spatial and spectral domains. [PERSON] et al. [22] proposed an adaptive fusion of RGB and NIR signals for HR estimation, based on analyzing changes in background illumination. Then, they combined the physiological BVP model into a multispectral HR estimation framework to suppress ambient light noise and obtain accurate BVP signals, thereby improving heart rate measurement performance [23]. [PERSON] et al. [24] proposed an end-to-end fusion video vision transformer network to integrate the spatiotemporal representations of complementary RGB and NIR, enabling robust HR estimation under complex conditions. However, existing multimodal fusion methods often rely on manually crafted features for spatiotemporal characteristics, which limits their generalization and robustness, and prevents them from fully exploiting compensatory information between different modalities.
## 2 Materials and Methods
### Treatment Pattern and Irradiation  
As depicted in Figure 2a, the outer rim of the laser test pattern contained 25 LPC marker lesions. These marker lesions were applied in CW mode (200 ms pulse duration and 200 mW pulse power). The marker lesions served as an orientation guide in the evaluation process of the RPE explant. The microsecond laser probe region was placed inside the demarcation frame consisting of a pattern of 10 \(\times\) 10 lesions. Laser pulses with durations of 2, 6, 10, 14 and 20 \(\upmu\)s with increasing energy were applied. The exact radiant exposure values for each lesion can be found in Table 1 in Appendix A. The pulse energy was measured with a calibrated energy meter (J-10 MB-LE, Coherent, Santa Clara, CA, USA) prior to the experiments in front of the laser aperture of the system. The energy meter has a measurement accuracy of 2.8% at 532 nm. The radiant exposures reported in this work were calculated using the following equation (using a spot size on the explant of A = 120 \(\times\) 120 \(\upmu\)m\({}^{2}\) (Figure 2b) and an IMF of 1.3):  
\[\mathrm{H}=\frac{\mathrm{E}_{\mathrm{pulse}}}{\mathrm{A}}.\mathrm{IMF} \tag{1}\]  
Figure 2a shows that the pulse duration was increased from top to bottom (2 to 20 \(\upmu\)s), and the radiant exposure was increased from left (135 mJ/cm\({}^{2}\)\(\doteq\) 15 \(\upmu\)J) to right (maximum: 1354 mJ/cm\({}^{2}\)\(\doteq\) 150 \(\upmu\)J at 20 \(\upmu\)s). Every laser setting appeared twice within the pattern to investigate reproducibility due to local variability in the RPE melanin distribution. The useful range of target energies for each pulse duration within the treatment pattern was pre-evaluated by multiple RPE explant tests.
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 3 Proposed Method
### Online: Temperature Estimation Using Infrared-LiDAR  
After going through the above offline process at least once, the temperature can still be estimated using the online process. In this process, distance and digital count are obtained through LiDAR and Infrared camera for an object, and temperature is estimated by configuring a mapping function.  
The \"STEP 3: Object Measurement\" process uses LiDAR and infrared cameras to measure objects as shown in Figure 11. The distance to the object can be obtained using LiDAR, and the digital count (\(G_{obj}\)) by the radiation of the object can be obtained through the infrared camera. The distance(d) obtained through LiDAR is given as input to the linear interpolation functions \(G_{min}(x)\) and \(G_{max}(x)\) previously defined in STEP 2 to obtain the minimum and maximum digital counts (\(G_{min},G_{max}\)) according to a specific distance. Afterwards, the radiation (\(L(T_{max}),L(T_{min})\)) calculated in STEP1 and the digital count (\(G_{max},G_{min}\)) of a specific distance obtained through the interpolation function allows the Radiation-Digital count mapping function to be newly defined. \(L(G_{obj})\) can be obtained by applying the digital count (\(G_{obj}\)) measured by the infrared camera to a newly defined mapping function, and the value of \(T\) that satisfies the pre-calculated \(L(T)=L(G_{obj})\) is estimated as the object temperature \(T_{obj}\).  
Figure 11: [STEP 3: Measurements of the Objects and Temperature Estimation] Digital count and distance acquisition for objects using an infrared camera and LiDAR, and temperature estimation using a newly defined mapping function.  
Figure 10: Radiation-Digital count mapping curve that changes with distance through actually obtained data.
## 5 Applications of Proposed Solution in Geotechnical Problems
### Interpretation of Pressuremeter Tests  
The in situ SBPM test results reported by Rouainia et al. [45] were selected to further illustrate the applicability of the present solution in structured soils. These SBPM tests were performed at depths of 14 m and 20 m in London Clay, and the relationships between the cavity strain and pressure on the inner borehole wall were well documented.  
The relevant parameters of London Clay can be found from Gasparre and Coop [46] and Rouainia et al. [45], i.e., \(M=0.87\), \(\lambda=0.097\), \(\kappa=0.003\), \(v=0.3\). The bulk unit weight below the water table (5.6-m depth at the test site) is 20 kN/m\({}^{3}\). The over-consolidation ratio at the depths 14 m and 20 m are 3.0 and 4.0, respectively. According to the isotropic compression test results, the values of \(e_{IC}^{*}\) is estimated to be 1.4. The lateral stress coefficients for normally consolidated and over-consolidated conditions are estimated using [47]:  
\[K_{0 nc}=1-\sin\varphi^{\prime} \tag{49}\]  
\[K_{0 oc}=K_{0 nc}OCR^{\sin\varphi^{\prime}} \tag{50}\]  
where \(\varphi^{\prime}\) is the effective friction angle. For London Clay, \(\varphi^{\prime}\) varies in the range of 20-25\({}^{\circ}\)[48], and its average value is adopted here.  
As suggested by Liu and Carter [35], the value of b is smaller than 1 for stiff clays, and therefore, 0.8 is adopted for the stiff London Clay in the calculation. The values of \(c\), \(\gamma\), and \(\omega\) refer to Carter and Liu [37], and are taken as 0.005, 1.0, and 1.5. Because the initial structure is enhanced with depth, \(R_{s}=3.0\) and 3.2 are adopted for 14 m and 20 m, respectively.  
The variations of the pressure on the inner borehole wall with the cavity strain were predicted using the present solution. Adopting the aforementioned parameters, the relationships between the total internal cavity pressure and cavity radial can be calculated using the present solution, as shown in Figure 8. It is known that the pressure applied by the probe to the inner borehole wall is equivalent to the total internal cavity pressure, and the cavity strain is \((a-a_{0})/a_{0}\). Thus, the results of SBPM tests can be predicated from the cavity expansion curves by converting the cavity radius into the cavity strain. As shown in Figure 17, the general trend of SBPM test response curves are well captured, and the present solution predicted a pressure on the inner borehole wall very close to the measured data from the SBPM tests.  
Figure 16: Calculated against measured total radial stress and pore pressure during the casing installation.
## 5 Simulation and Experiment
### Simulation Analysis
The expected values for the stator active power and reactive power were 4000 W and 0 Var, and the capacitance voltage was maintained at 400 V.  
As shown in Figure 12, when the power grid SSO was generated, both the DFIG stator side output power and the capacitance voltage oscillated, with the power oscillation being the most severe. When the oscillatory inhibition strategy proposed here was adopted, the DFIG stator-side reactive power oscillation was basically suppressed and, while the active power still oscillated, the amplitude of the oscillation was reduced by nearly 60% compared with before the inhibition. After being subjected to the control of the adaptive quasi-resonant controller, the sub-synchronous component in the DC bus voltage was also reduced. As the DFIG grid side was still affected by the SSO, the oscillation was still generated in the DC bus voltage, but the impact of the DC bus voltage fluctuation on the RSC control was weaker. In summary, the simulation analysis verified the effectiveness of the control strategy proposed in this paper. In the case of uncertain perturbations in the system, the response speed controlled by the improved controller was faster than that of a traditional PI control, and the DFIG output power could be adjusted quickly and smoothly. The adaptive quasi-resonant controller showed a good inhibition effect on the SSO component in the DFIG stator current, and when the SSO frequency of the grid changed, the SSO frequency could be tracked and the variable frequency SSO component could be suppressed. Figure 11: Comparison of inhibition effects between two resonant controllers with SSO frequency of 10 Hz: (**a**) quasi-resonant controller; (**b**) stator current single phase magnification; (**c**) adaptive quasi-resonant controller; (**d**) stator current single phase magnification.
## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Distributed Deployment  
This option allows us to achieve decentralized intrusion detection by means of local processing at IoV nodes, either automobiles or roadside equipment. By processing data locally rather than forwarding it to a central server, this method reduces latency, improves real-time threat detection, and preserves data privacy. In a distributed deployment system, [PERSON] et al. [61] combined federated learning with ensemble approaches. Using this method keeps data locally and centralizes model updates to help solve privacy issues. The study showed how this method reduced data breach risk while nevertheless enabling scalability for large IoV networks. Reducing the links between nodes and the central server helped to reduce the communication overhead, therefore improving the system's practical relevance. [PERSON] et al. [75] examined distributed ensemble models inside edge computing environments. Using ensemble classifiers on edge devices, this work addressed the scalability problem by enabling real-time intrusion detection free of major reliance on central servers. Studies in [63; 65] are good examples of federated, gossip-based, and privacy-aware distributed models. The distributed design of this approach lets the IDS effectively manage several vehicle network circumstances. Moreover, the study in [76] proposes a Deep Reinforcement Learning (DRL)-based federated self-supervised learning framework tailored for task offloading and resource allocation in Integrated Sensing and Communication (ISAC)-enabled vehicle edge computing environments. The approach enhances security and efficiency in decentralized IoV systems by enabling vehicles to collaboratively learn optimal policies while preserving data privacy. It effectively addresses challenges such as limited bandwidth, dynamic environments, and data heterogeneity, making it highly relevant to privacy-preserving and intelligent intrusion detection and decision-making in IoV.
# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 5 Challenges and Future Work  
Given the advent of wireless sensor networks and the recent growth in the methods of wireless sensor networks during the last decade, authors have proposed creative methods to meet the existing challenges. However, several challenges can be discussed for future work as stated in the following [31].  
Time is a critical challenge in wireless sensor networks. WSNs deal with a real-world environment. In many cases, sensor data must be delivered to the base station under time constraints so that proper observations are made or immediate actions are taken. To date, few studies have been done to meet the real-time requirements in WSNs. Not only do most protocols overlook the real-time requirements, they also attempt to take prompt actions that are fast enough to meet the deadline. There are some initial findings regarding real-time routing. Some other functions of data transmission, data fusion, classification, security, query processing, target, and event detection must also face real-time limitations. Developing real-time protocols for WSNs, as well as the associated analysis techniques is important.  
Another challenge for WSNs is power management or the energy constraint. The slow growth in battery capacity development and the inhibition of battery replacement by the untended nature of sensor nodes and the perilous sensing environment lead to some difficulties in facing this constraint. Furthermore, the vigilant nature of sensor network applications entails a long lifetime. On the other hand, finding an energy effective surveillance service for a geographic region is of great importance. Therefore, the focus of current research is on the ways of providing either full or partial sensing coverage within the context of energy conservation.  
The next challenges can be the time-varying nature and the network scale in WSNs. Sensor nodes function with limited capabilities of communication, storage, and computing under severe constraints on energy. Depending on the application, there are varying densities of WSNs ranging from sparse to highly dense. The sensor nodes show highly adaptive and dynamic behaviour. The need to be self-organized and conserve energy make sensor data constantly adjust their behaviour to the current level of activity. Furthermore, the behaviour of sensor nodes may need to be adjusted in response to the haphazard and unpredictable behaviour of wireless connections arising from the high level of noises and interference in the radio frequency. This adjustment is for inhibiting severe degradation of the performance of the supported application.  
The last challenge may be distance management. Sensor nodes may be implemented in other fields such as subway stations. The direct management of the network will be difficult, and thus, an indirect management or distant control system is needed.
###### Abstract  
This in vitro study aimed to evaluate the fracture resistance and mode of failure of endocrowns with proximal extension design compared to the conventional design, and to assess the agreement of evaluators on the failure mode. Twenty mandibular third molars free of caries with approximately similar root lengths and crown dimensions were sectioned parallel to the occlusal plane 2 mm above the cementoenamel junction (CE). Then, pulp chambers and canals were accessed, cleaned, and smoothed for the path of insertion. To achieve a symmetrical pulp chamber with an average depth of 3 mm, chambers were filled with flowable resin composite. Then, teeth were randomly divided into two groups (n = 10). The control group has all the walls at the same level. The test group had a box extension on the proximal wall 2 mm apical to the buccal and lingual wall level. Endocrowns (n = 20) of two different designs (conventional and proximal extensions) were fabricated using lithium disilicate (IPS e.max CAD, Ivoclar Vivadent, Schaan, Liechtenstein). After cementation, specimens were loaded with a round-end vertical loading tip at a rate of 0.5 mm per minute until fracture (INSTRON, Norwood, MA, USA). Then, the fractured specimens were collected for evaluation and assessment. Statistical analyses were performed using the Mann-Whitney U-test (\(\alpha\) = 0.05) for fracture test analysis and Cohen's Kappa for inter-rater reliability. The Mann-Whitney U-test showed a non-significant difference between the two groups (\(p\) = 0.142). However, the mode of failure of the endocrown with proximal extension showed more catastrophic failures than the conventional design. Adding proximal boxes to the endocrown design did not negatively impact the fracture resistance of the restoration. Visualization of the fracture line and tracing their apical end by direct vision or other diagnostic tools is an essential part of the evaluation of failures of endocrowns. Endodontically treated molar teeth with proximal caries extension might be restored with an endocrown with proximal extension. Clinicians should take extra care when visualizing the fracture lines and tracing their apical end as it might be very misleading.  
adhesive dentistry; failure load; lithium disilicate; preparation design; post and core; endocrown; load at fracture; fracture resistance; proximal boxes +
Footnote †: journal: Applied Sciences  
1
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 6 Experiment Results and Analysis
### Hyperparameter Setting  
During the training process, the model was configured with a batch size of 32, a learning rate of 0.01, and the optimizer was set to SGD (Stochastic Gradient Descent). The number of training epochs was set to 100.
## 2 Research Area
For example, it is known that ozone depletion tends to accumulate in the Antarctic and Arctic regions [22] where we would expect depletions to be seen, but we would also anticipate localized depletion closer to the fire even possibly within a shorter time scale and the focus is to test this in our study. Statistical evidence indicates that bushfires have been increasing in frequency in Victoria since 1995 [23] and also nationally. The Australian bushfires of 2019-2020 severely affected the air quality of the eastern half of Australia. Vast plumes of smoke were projected high into the atmosphere and weretransported around the world, even reaching across the Pacific Ocean into the continent of South America [24]. Measurements taken from satellites have recorded that the smoke in these plumes has caused noticeable changes in the composition of the upper atmosphere and, more importantly, a decline in stratospheric levels of ozone [20; 25]. But the mechanism for how smoke from the fire might lead to, or exacerbate, ozone depletion is still uncertain. [PERSON] et al. [19] suggested that the particulate matter of this smoke does contribute to the destruction of stratospheric ozone contrary to the atmospheric warming hypothesis suggested by [PERSON] et al. [15; 21]. The mechanism for ozone depletion from bushfires is depicted in Figure 3, taken from [26]. Briefly, oxygenated organic compounds of the type found in aerosols from bushfires can absorb hydrochloric acid and carry it into the stratosphere. Here, ultraviolet light from the sun breaks down the hydrochloric acid to release atomic chlorine, which, in turn, breaks down the ozone molecules. Our study introduces new methods into this particular area of geospatial and temporal ozone level prediction. Tree-based methods, specifically, random forests have been applied in other areas of geophysical research (see [27]) but as far as we are aware, these methods have not yet been applied in any work specific to bushfire-induced ozone level change research. Furthermore, we apply State Space Models (SSMs) to the time series of Total Sum Ozone and Total Sum Aerosol columns. SSMs provide a general framework for analyzing deterministic and dynamical systems observed through a stochastic process [28]. The mathematical framework of SSMs includes latent processes and Hidden Markov models (HMMs), as applied in this study. We refer the reader to a recent study [29] that used a specific SSM, namely the Dynamic Linear Regression Model (but without accommodating so-called intervention points) to study the impact of total ozone level depletion on increased ultraviolet radiation associated with increased malignancy and cancer events [30; 31; 32; 33].
## 4 Experiments and Results
### Detection Examples
#### 4.6.1 SSD with Context Information Scene Perception Modules and the Feature Fusion Modules  
To estimate the contribution of different components of CISPNet, we further constructed three variants and tested them on the remote sensing image dataset tests to verify the impact of each module on the detection performance. The results are shown in Table 3. The first step is to validate the effect of CISP module. We insert four CISPs at positions shown in Figure 3a, and the first two rows of Table 3 illustrate that the CISP module has a higher promotion, and the mAP on the testing data set achieves 79.38%, which is improved by 5.37% compared with the conventional SSD. Second, we insert two FFMs at bottom positions shown in Figure 3a, the mAP is 76.15%, which is better than the conventional SSD. Finally, we insert four CISPs and two FFMs at positions shown in Figure 3a and the last row of Table 3, and the mAP is increased to 80.34%, which shows the effectiveness of the added four CISPs and two FFMs for enhancing the overall performance. These experiments have proved the importance of each component of CISPNet.