esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage and Failure in a Statistical Crack Model
## 5 The Fracture Process in Graphs
### Time to Failure, Damage and Stress vs. Strain Rate  
In Figure 3, time to failure measured from the beginning of the avalanche phase is plotted versus strain rate. Failure has been defined as the first propagation of a crack completely across the cell. The curve shows the monotonic decrease of the interval time as strain rate increases. This curve is accurately fit by a polynomial second-order in strain rate. The coefficients of that polynomial are constants for the \(\widehat{SCM}\) and can be extracted from the array code.  
Whereas the curve of Figure 2 describes the accumulation of damage, the additional input of Figure 3 is required to determine when damage ceases to grow. The direct relation between damage at time of failure and strain rate is exhibited in Figure 4. That curve is also accurately fit by a second-order polynomial in the strain rate.  
In principle, the residual stress left in the separate pieces of the fractured cell can be calculated directly by the finite volume computational cell, given the elastic moduli, the damage and the strain rate. However, when fracture proceeds rapidly, i.e., within a few computational cycles, that calculation may be inaccurate. The calculation within the array code proceeds at a much smaller time step and would be more accurate in the absence of interactions between the computational cell and its neighbors. In Figure 5, the stress at time of failure, \(\sigma_{yy}\) for all the calculations in this paper, is plotted versus strain rate. Although the graph shows a monotonic growth of the residual stress with strain rate, the growth is very small and is likely insignificant.  
Figure 2: The ensemble-averaged damage is plotted as a function of time for 3 imposed strain rates, \(\dot{\epsilon}=10^{4}\) s\({}^{-1}\), \(10^{5}\) s\({}^{-1}\), and \(10^{6}\) s\({}^{-1}\). The ensemble size is 100 and the process zone is 10% of the crack radius. The data is captured by a quadratic trendline with an \(R^{2}>0.995\) in each case. Higher strain rates lead to shorter times to failure and more damage at time of failure.  
Figure 4: The damage at the time of failure is plotted as a function of strain rate. The ensemble size is 100 and the process zone is 10% of the crack radius. Higher strain rates lead to more damage sustained at the time of failure. The curve is captured by a quadratic trendline with \(R^{2}=0.996\).  
Figure 3: The ensemble-averaged time to failure is plotted as a function of strain rate. The ensemble size is 100 and the process zone is 10% of the crack radius. The time to failure decreases monotonically with strain rate and the curve is captured by a quadratic trendline with \(R^{2}=0.989\).
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 2 Related Works
[25] proposed a robust handover optimization technique with FL controller (RHOT-FLC). The proposed RHOT-FLC aimed to automatically configure both HOM and TTT by exploiting the information on RSRP, reference signal received quality, and UE velocity as input factors. Additionally, the inference rules were determined based on the evaluation conducted on each rule and all of the performance metrics to achieve mobility robustness optimization at the cost of computation loads and times. Because FL can provide relatively reliable outcomes with lower computational costs and shorter times, it has been widely applied in many fields, including for making handover decisions in mobile cellular networks. The correctness of the inference engine of FL depends on the choice of input factors, membership functions (MFs), rule base size, and others. The main contribution of our work is the proposed adaptive handover decision algorithm that uses FL to effectively alleviate unnecessary handovers and reduce the ping-pong effect in 5G UDNs by dynamically adjusting TTT in addition to HOM. The inputs to the FL controller are only the signal strength and horizontal moving speed of user devices, and the outputs are the HOM and TTT values for each piece of UE.
## 2 Principle of Noise Reduction Based on Singular Value Decomposition (SVD)  
SVD is a PCA method that provides a convenient way for decomposing a matrix and extracting the principal component, which can be realized through the orthogonal decomposition of the signal into two directions. Here, the singular values corresponding to the useful signal are larger than the noise, which can be used to further separate noise and signal.
## 3 Stray Current Corrosion Morphology, Measurement Method, and Influencing Factors
### Methods for Measuring DC and AC Stray Currents
Therefore, understanding the relationship between AC and DC and the corrosion mechanism is the basis for constructing the stray current corrosion prediction model. Only by performing the preparatory work well can the corrosion prediction model be established in the future, and the set value of cathodic protection can be adjusted according to different current densities and current frequencies. Comparing the methods for measuring DC stray current and AC stray current shows that DC stray current and AC stray current share similarities in their measurement methods and basic principles, but AC stray current exhibits periodic variations compared to DC stray current. Although the corrosion hazard of AC stray current is not as severe as that of DC stray current, its corrosion distribution is more complex and exhibits uneven corrosion patterns. When measuring, factors such as the current frequency and waveform of the AC stray current must be considered, making its corrosion protection strategy more complex.
# VPP Participation in the FCR Cooperation Considering Opportunity Costs
## 1 Introduction  
Although the European Commission approved guidelines in 2017 that aimed at the creation of balancing markets [1], in 2023, Portugal and Spain did not yet have a functioning FCR market [2]. The existing situation and a possible alternative can be described according to Figure 1, as follows.  
Figure 1: Problem formulation.  
The non-existence of an FCR market in these countries implies that the costs that consumers pay regarding this service are not transparent and are probably inefficient due to the lack of competition. The only allowed balancing service providers (BSPs) of FCR in Portugal and Spain are conventional power plants (e.g., hydro, coal, natural gas). Besides not being a market subject to open competition, the BSPs are providing an FCR as a mandatory and non-remunerated service. On the other hand, as renewable energy generators, hydrogen electrolyzers (HEs) could become BSPs and generate additional revenues in the FCR market that would complement their main sources of revenues (by selling energy and hydrogen, respectively). While these actors might be interested in providing an FCR, they are naturally against providing this service if they cannot profit from it. They end up not constituting the critical mass that is needed for opening up a new FCR market. It is up to regulators of both countries to either keep this situation or to open up this sector to transparent market mechanisms. There are at least three ways for the regulator to ensure the existence of an FCR market: either a national market is created in each country, an international market with these two countries is created (like the present-day energy market), or these countries integrate an existing and wider international market. This study explored the latter option.
# Ultrasound-Guided Detection and Segmentation of Photoacoustic Signals from Bone Tissue In Vivo
PA sensing, while utilizing the acoustic resolution, can assess more spatial information in deep tissues than conventional optical-based techniques. PA signals also provide chemical information, similar to the optical method. To the best of our knowledge, the PA technique, as an adjunct to the established US system, has already demonstrated its potential in several preclinical and clinical applications [18; 19; 22; 25; 26; 27; 28; 29]. Recent studies have indicated that the PA signal from the bone can provide critical chemical information, in addition to physical information [25; 26; 27; 28; 30; 31; 32], which is important for predicting and diagnosing osteoporosis. However, most of all the previous studies that focused on the assessment of trabecular bone using PA techniques are based on ex vivo bone specimens or small animal models. The possible challenges of in vivo applications for human bone assessment, including PA signal generation and propagation modes in trabecular bone, were not considered. For example, cancellous bone, which has a high percentage of trabecular bone, consists of a mineralized, porous trabecular network embedded in the bone marrow with filament-like trabeculae. The specific structure of the bone makes the generation and propagation of the PA signal more complicated than in soft tissue. The PA wave propagation in the bone and segmentation of the PA signal from the bone are not fully understood, because of the structural complexity and inhomogeneity of the bone. Previous studies reported that the bone can support two kinds of longitudinal waves that travel at different velocities, including fast wave and slow wave. The fast wave is associated with the movement of fluid (blood and marrow) in phase with the solid (mineralized trabeculae), and the slow wave is associated with the movement of fluid out of phase with the solid [10]. Apart from the longitudinal wave, there is a Lamb wave, which travels across the cortical bone at high speed. Therefore, isolating the PA signal of the trabecular portion from the complicated PA signal is a key challenge that must be addressed for the PA bone assessment technique adopted in clinical research. In this study, we propose a US-guided PA bone assessment method for locating and segmenting the PA signal spatial information of the human calcaneus bone, which can be used to distinguish the trabecular bone signal from other signals (soft tissue, cortical bone and their echoes). Two aspects have been demonstrated in this study. First, we studied the feasibility of the detection and segmentation of the PA signal generated from different bone tissues using the US-guided PA bone assessment method based on a two-dimensional (2D) numerical simulation. Second, by conducting an experiment on a volunteer, we investigated the feasibility of detecting and distinguishing the PA signal of the trabecular bone in vivo using the US-guided method.
## 3 Results and Discussion
### PAH and Trace Metals Concentrations
#### 3.1.1 PAHs
dibenzo(a,h)anthracene) have been designated high priority pollutants by Environmental Protection Agency (EPA), while 7 (bez(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene) have been classified as human carcinogens by the U.S.EPA (2003), referred as B2 PAHs. The contribution of B2 PAHs was estimated equal to 5% of the total PAHs. Especially benzo (a) pyrene has been classified as particularly dangerous and has been extensively studied worldwide [7; 18; 30; 31; 32]. The German Federal Environmental Agency's Commission for indoor air quality has established the limit of 10 ug/g for exposure to B[a]P in house dust [18]. B[a]P concentrations in the present study were found 3 orders of magnitude lower than the above limit. Compared to the results of earlier studies, the concentrations of B[a]P found in this study were either lower [18; 31; 32] or comparable [7].
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 4 Simulation Results
### Scenario 2 Insufficient Data Transmission Resource Scenario
The optimal solution objective function value of ITSRD is 5.4% higher than that of CTSRD, and the time required for ITSRD to reach the optimal solution is 24.6% less than that of CTSRD. The optimal solution objective function values of SASRD and GASRD are slightly higher than those of CTSRD, but it takes longer to reach the optimal solution. ITSRD performs better than SASRD and GASRD in terms of the optimal solution objective function value and the optimal solution iteration time. Figure 10 presents the iterative results of different methods. The comparison between the orange dashed line and the green dashed line in the figure illustrates the impact of the improvement in tabu search. It can be seen from the figure that, before 26 iterations, the search process of the two methods was the same. After 26 iterations, the traditional tabu search method falls into the local optimum; when reaching 39 iterations, it has been improved, but the improvement is limited. In contrast, the improved tabu search method escapes from the local optimum and continues to search in the direction of a better solution, which reflects that the improved tabu search method improves the ability to escape from the local optimum. The scheduling results of SASRD and GASRD are better than those of the traditional tabu search algorithm, but it takes longer to reach the optimal solution. Figure 9: Targets and ground stations distribution in scenario 2. It can be seen from the red boxes in Figures 11 and 12 that ITSRD had more satellite-ground data transmission periods than CTSRD to complete more satellite-ground data downloading missions in the insufficient data transmission resource scenario, resulting in a better objective function value. It can be implied that the traditional tabu search method can easily fall into the local optimum due to the complexity of the subsystem coupling scheduling of the multi-satellite observation-relay transmission-download coupling scheduling problem. Additionally, by adjusting the data transmission time windows, the improved tabu search method can assist the satellite in transmitting more observation data. Hence, the objective function value is improved, which implies that the improved tabu search method is able to prevent the search method from falling into the local optimum by adjusting the states of the satellite cluster subsystem. Figure 11: Observation and data transmission event diagram of CTSRD in scenario 2. Figure 10: Iteration processes of different methods in a single condition of scenario 2.
## 3 Simulation Results
### Comparison with a Heuristic D-Type Control Parameterization  
For the sake of comparison, Figures 7 and 8 contain the results of the heuristically tuned control approach from [26], where a root locus analysis of the plant was employed to set the controller gain to \(K_{\text{D}}=-0.8\) to obtain purely negative real eigenvalues. If the low-pass filtered velocity estimate is determined by a second-order transfer function with the time constants \(T_{1}=(2\pi\cdot 32)^{-1}\) and \(T_{2}=0.5T_{1}\), excessively large control inputs can be observed, which are more or less useless in practice due to extreme actuator wear and energy consumption.  
Although this was not discussed explicitly in this paper, it is easily possible to extend the newly derived design LMIs of the output feedback according to Corollary 1 by further requirements. Especially, \(\Gamma\)-stability domains can be introduced not only to enforce real filter eigenvalues, but also to guarantee desired transition times and bandwidth limitations of the controller itself. The required steps are summarized in Appendix B.  
For a second comparison with [26], Figures 7 and 8 also contain a further velocity estimation approach. There, the same (heuristically chosen) gain \(K_{\text{D}}=-0.8\) was used for the controller parameterization; however, an LMI-based observer tuning was performed on the basis of a nominal system model. The corresponding results are well comparable with the more simple filter-based output feedback from this paper with respect to noise suppression and transient behavior of the controlled system. Obviously, however, the use of a full-scale state observer leads to a suppression of undershooting the desired target position \(x_{1}=0\) due to the fact that the velocity estimates are less affected by the lag behavior that occurs inevitably in the case of a model-free linear filter approach for derivative estimation. This becomes obvious if the Figures 6c,d are compared with Figure 7b. However, the heuristically parameterized second-order filter-based velocity  
Figure 7: Comparison of a heuristic D-type control approach with an optimized observer from the previous work [26].  
estimate in Figure 8d is by far worse than the optimized filter in the Figures 6c,d and the model-based observer in Figure 8b.  
Therefore, it should be pointed out that using the joint optimization of filter-based derivative estimators and output feedback controller gains is especially promising in practice if either a purely proportional feedback is implemented or if the use of no more than two time derivatives of the measured signals is required. In other scenarios, the Cases 1 and 2 sketched in this paper (cf. [29] for further details) are superior in transient operating conditions due to the capability of a full-scale state observer to reduce not only the effect of stochastic noise, but also to avoid large undesired lag phenomena.
## 4 Discussion  
To our knowledge, this is the first description of therapy with ANI for refractory cutaneous manifestations outside a clinical trial, demonstrating very quick clinical efficacy. It is unclear which primary outcome measure is best for trials in SLE [19]. Both SRI, used in TULIP-1 [2], and the BICLA, used in TULIP-2 [1], are robust measures of treatment response. They consist of different domains and give more weight to the SLEDAI response (SRI), or BILAG domains (BICLA), respectively. Both measures do not allow for any domain to worsen. In TULIP-1, there was no difference in the CLASI between ANI and PBO; in TULIP-2, a statistically significant difference was found at week 12 [1]. A post-hoc analysis of pooled data in patients with CLASI \(\geq\)10 at baseline confirmed these results [22]. In all published phase II and III clinical trials, the Cutaneous Lupus erythematosus disease area and severity index (CLASI) was used to assess changes in skin manifestations. The CLASI aims to distinguish between activity and damage [23]. In the activity domain, erythema is graded from 0 (absent) to 3 (dark red; purple/violaceous/crusted/hemorrhagic) in different body areas. Likewise, scales/hypertrophy are judged from 0 (absent) to 2 (verrucous/hypertrophic). Further, lesions of the mucous membranes are searched for. Lastly, alopecia is assessed as present or absent. If present, the scalp is divided into four quadrants and scored, ranging from 0 (absent) to 3 (focal or patchy in more than one quadrant). To analyze the damage, various lesions are scored: First, dyslipidemia is documented as 0 (absent) or 1 (present). Next, scarring/atrophy/panniculitis is scored, ranging from 0 (absent) to 2 (severely atrophic scarring or paniculitis). Then, the duration of dysgipmentation is considered (more or less than 12 months). Finally, scarring of the scalp is scored as 0 (absent), 3 (present in one quadrant), 4 (present in two quadrants), 5 (three quadrants), or 6 (affects the whole skull). The overall score ranges from 0 to 70, and higher scores indicate more severe skin disease. It must be noted that the CLASI response was defined as an improvement of at least 50% in participants with a minimum score \(\geq\)10. In the MUSE phase IIb trial, 77 (25%) of patients fulfilled this definition [17]. The percentage of CLASI responders at 24 weeks was 63% for 300 mg of ANI vs. PBO (30.8%), and responses were seen early on (around 50% at eight weeks) with a plateau of 60-65% response rates around week 20. In TULIP-1, the CLASI response followed the same definition, and there were 42% vs. 25% of responders favoring ANI 300 mg at 12 weeks [2]. However, the difference between ANI and PBO evened out at the end of the trial. Finally, TULIP-2 reported a CLASI responder rate of 49% vs. 25% at 12 weeks, which was maintained through 52 weeks [1]. Lastly, a phase II of subcutaneous administration of ANI in 36 patients showed no numerical differences in the CLASI response at 12 weeks (45% vs. 44%) [21]. Nevertheless,
# eHealth Integrity Model Based on Permissioned Blockchain +
###### Abstract  
(1) Background: Large eHealth systems should have a mechanism to detect unauthorized changes in patients' medical documentation, access permissions, and logs. This is due to the fact that modern eHealth systems are connected with many healthcare providers and sites. (2) Methods: Design-science methodology was used to create an integrity-protection service model based on blockchain technology. Based on the problem of transactional transparency, requirements were specified and a model was designed. After that, the model's security and performance were evaluated. (3) Results: a blockchain-based eHealth integrity model for ensuring information integrity in eHealth systems that uses a permissioned blockchain with off-chain information storage was created. In contrast to existing solutions, the proposed model allows information removal, which in many countries' eHealth systems is a legal requirement, and is based on a blockchain using the Practical Byzantine Fault Tolerant algorithm. (4) Conclusion: A blockchain can be used to store medical data or only security-related data. In the proposed model, a blockchain is mainly used to implement a data-integrity service. This service can be implemented using other mechanisms, but a blockchain provides a solution that does not require trusted third parties, works in a distributed eHealth environment, and supports document removal.  
e +
Footnote †: This paper is an extended version of our paper published in International Conference on Cyber Security and Communication Systems, Melbourne Institute of Technology, Melbourne, Australia, 10-12 December 2018.