esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 3 Results and Analyses
### Equivalent Thickness Method
#### 3.3.2 Equivalent Thickness Based on Standard Deviation of Pit Depth  
The aforementioned research indicates that neither the \(DOV-\)based (average) thickness nor the minimum cross-sectional thickness appeared to fulfill the requirement of accurately predicting the ultimate strength of randomly pitted columns. To address this issue, the standard deviation of pit depth was incorporated to refine the average thickness, leading to the modified thickness formulation \(t_{e}=t_{a}+k\sigma\). The parameter \(k\) was determined as \(-1.0\), \(-1.25\) and \(-1.5\) through empirical assumptions. Figure 14 depicts the ultimate strength of H-section columns, categorized based on the flange and web thickness variations, utilizing the \(\sigma-\)based equivalent thickness. The study illustrates that the ultimate strength of randomly corroded columns can be effectively assessed by utilizing the \(\sigma-\)based equivalent thickness. Overall, a value of \(k=-1.25\) yielded a more accurate prediction of the ultimate strength for randomly pitted columns. This was due to the fact that the mean error, being the average between errors of the results from the equivalent thickness and random pitting corrosion model under five levels of pitting intensities, was mostly less than 3% for the \(k\) value.
# Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective
## 2 Literature Review
### Driving Variables of HSR System Resilience Improvement
#### 2.1.2 Resilience Attribute-Related Variables  
Resilience theory introduces four core variables, i.e., persistence, restorative, adaptability, and transformability, which are the basis for identifying resilience attribute-related variables [5; 27]. The four core variables are also similar to variables proposed by [PERSON] (2020) [4] (i.e., response, survivability, recovery, mitigation and preparedness). In consideration of the daily inspection and maintenance of HSR systems, this study listed disturbance warning as a resilience attribute-related variable, corresponding to the variable \"mitigation and preparedness\" mentioned by [PERSON] (2020) [4]. Ultimately, the resilience attribute-related variables of HSR systems are presented as disturbance warning (DW), persistence ability (PA), function restoration (FR), and adaptability and transformability (AT).  
Disturbance warning mainly refers to the ability to take quick and effective means to give early warning and ensure control of sudden events. Based on regular phenomena or precursors, early warning can avoid the failure, so as to deal with the event effectively [22]. If small deviations cannot be warned of in a timely manner, they may cause serious accidents [48].  
Persistence ability refers to the ability to maintain normal function within acceptable ranges when uncertainty conditions occur [49]. It is highly related to technology redundancy and can be regarded as an HSR system's robustness [4].  
Function restoration refers to the capacity to restore the original condition or secondary use condition [4; 50]. After being damaged by an external force or internal system, function restoration may last a few hours up to several weeks [51]. Meanwhile, the restoration strategy and the accessibility of rescue teams will influence the total unmet demand within the time interval [52; 53].  
Adaptability means the capacity to adjust responses to changing conditions. Transformability presents the capacity to transform existing functions into new functions under unusual and unstable conditions [54]. By summarizing accident cases or past experience, HSR system operators can improve their adaptability and transformability [4].  
Ultimately, ten driving variables of HSR system resilience improvement have been identified and presented in Table 1, including six HSR system ontology-related variables and four resilience attribute-related variables.
# A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
## 6 Conclusions  
In this paper, an effective hybrid PAPR reduction scheme has been proposed. The proposed method is not simply a combination of SLM and PTS. In the proposed method, the initial PAPR signal is obtained by the SLM process with the selection of corresponding phase sequences. Then, the initial PAPR reduction signal passes through the modified PTS scheme, where the effect of multiple adjacent data blocks is considered in the following PAPR reduction process. From simulation results, it can be obtained that the proposed scheme can offer a better performance than the traditional SLM, PTS and SLMPTS schemes, and it is effective for PAPR reduction of FBMC/OQAM signals.  
This work was supported by Jiangxi Provincial Education Office Science and Technology Project (grant number: GJJ170915), and the National Natural Science Foundation of China (grant number: 61701144).  
The author would like to thank the editor and the anonymous reviewers for their valuable comments.  
The author declares no conflicts of interest.
# Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes
## 3 Results and Discussion  
### Model Parameter Validation and Results Analysis  
#### 3.1.1 Influence of Voxel Size on Simulation Accuracy  
The influence of voxel size on simulation results was investigated by comparing the filtration pressure drops under six different voxel sizes (10 nm, 15 nm, 25 nm, 50 nm, 75 nm, and 100 nm). As shown in Figure 2, when the voxel size decreases from 50 nm to 25 nm, the predicted pressure drop increases by 12.7%, approaching the experimental value. Further reducing the voxel size below 25 nm yields negligible changes in pressure drop, indicating that 25 nm is the optimal voxel size for balancing computational efficiency and simulation accuracy.  
Similarly, the effect of the x-y plane size on simulation results was analyzed. Six configurations (500 \(\times\) 500 to 3000 \(\times\) 3000 voxels) were tested with a fixed voxel size of 25 nm. As shown in Figure 3, the simulated pressure drop stabilizes when the x-y plane size reaches 2000 \(\times\) 2000 voxels. This suggests that a plane size smaller than 2000 \(\times\) 2000 voxels may not capture the full complexity of the fibrous structure, while larger sizes increase the computational cost without significant accuracy improvement.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 4 Experimental Results
### Security Analysis
#### 4.3.2 Scenario 2  
Suppose that an adversary who has \(SK^{*}_{j}\) wants to get other class's secret keys \(SK^{*}_{j+1}\). The adversary has to guess \(d^{*}_{j+1}\) to obtain \(SK^{*}_{j+1}\). Although he has always known \(n\), \(g\), and \(SK^{*}_{j}\), it is indeed a very big challenge for him to calculate \(d^{*}_{j+1}\) to get other class's secret key due to the difficulty of solving the discrete logarithm problem.
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 2 Materials and Methods
### Data Pre-Processing
#### 2.5.4 RNN Training Samples Generation  
In conventional machine-learning algorithms, each training sample is a vector, made of feature and target variables. Different from that, in a recurrent neural network, each training sample is a sequence (tensor), consisting of multiple vectors. The additional dimension is time domain. The length of the sequence, n_steps, is a hyperparameter of RNN. In this work, different values of n_steps are compared.  
The total data are divided into training data, validation data and testing data, for achieving an unbiased result. In order to increase the training data size, each training sample is generated with overlap with adjacent samples. The same method was applied to the generation of the validation samples (Figure 10). Each testing sample is generated without overlap (Figure 11), so that the final evaluation metric can be calculated on each single point of testing data only once. Please notice, to make it clearly illustrated, n_steps = 5 is used in both Figures 10 and 11, which does not stand for the final tuned value of this hyperparameter.
## 2 Literature Review
### Challenges in IoT-Based Health Monitoring  
Despite IoT benefits for health monitoring, a number of challenges prevent its widespread adoption of IoT-enabled healthcare systems. These challenges span the technical, operational, and ethical dimensions [36]:  
* _Data accuracy and reliability:_ ensuring that IoT devices provide accurate and consistent health data for effective decision making;
* _Data security and privacy:_ strong data encryption, authentication mechanisms, secure transmission in IoT health applications, and compliance with healthcare regulations like GDPR (EU) and HIPAA (USA) are required to protect sensitive patient information;
* _Infrastructure and connectivity:_ reliable internet connections (such as Wi-Fi and 5G) and sufficient network capacity are essential for real-time data transmission;
* _Energy efficiency:_ continuous data transmission in wearable devices must be optimized for low power consumption to extend battery life;
* _AI and machine learning integration:_ implementing real-time anomaly detection and predictive analytics necessitates powerful computers and optimized software;
* _Interoperability and scalability:_ the adoption of interoperability frameworks (such as FHIR) is necessary to ensure the seamless integration of various IoT devices and healthcare systems;
* _Ethical considerations:_ addressing issues with patient autonomy, consent, and data ownership is critical for responsible IoT implementation in healthcare;
* _Cost and accessibility:_ removing financial obstacles for smaller healthcare providers and guaranteeing fair access to IoT-enabled healthcare, especially in low-income or vulnerable regions, are important for avoiding a digital divide.  
Resolving these issues is essential to guaranteeing the success of connected health ecosystems and requires continuous innovation and cooperation between producers, healthcare providers, and legislators.
## 2 Materials and Methods
### Characterization of Two-Wavelength Coupling Experiment
The saturation diffraction efficiency during recording, \(\eta_{sat}\), is determined by  
\[\eta_{s}=sin^{2}\bigg{(}\frac{\pi d\Delta\eta_{sat}}{\bar{\lambda}cos\theta_{ cry}}\bigg{)} \tag{10}\]  
Here, \(\eta_{s}\) is the maximum diffraction efficiency, determined by the sample thickness (\(d\)), the recording beam wavelength (\(\lambda\)), and the refraction angle (\(\theta_{ cry}\)) of the incident light within the crystal. Figure 2: Nonvolatile two-wavelength experiment device. Photorefractive sensitivity is another critical parameter for evaluating the performance of photorefractive materials. It reflects the efficiency of the material in generating refractive index changes under light irradiation. Higher photorefractive sensitivity indicates a faster response to light intensity and more significant refractive index changes, which are beneficial for applications such as holographic storage and optical information processing. The photorefractive sensitivity \(S\) is defined as [34]  
\[S=\left[\frac{d\sqrt{\eta}}{dt}\right]_{t=0}/IL \tag{11}\]  
This equation represents the relationship between the total optical intensity (\(I\)) and the thickness of the crystal plate (\(L\)). The dynamic range \(M\)/# is another essential parameter for evaluating photorefractive materials. It describes the maximum range of light intensity changes that the material can effectively record and read. A larger dynamic range indicates a wider range of light intensity changes that the material can handle, enhancing its performance in applications such as holographic storage and optical information processing. The dynamic range \(M\)/# is defined as [35]  
\[\eta_{\omega}=\left(\frac{M/\#}{M}\right)^{2} \tag{12}\]  
Here, \(\eta_{\omega}\) represents the diffraction efficiency when the M-th hologram is stored, and \(M\) indicates the number of holograms. In the two-wave coupling experiment, only one recording is performed, the erasing process of the phase grating is recorded, and the writing time is short. Therefore, the sensitivity (\(S\)) and dynamic range \(M\)/# can be approximated as  
\[S=\left[\frac{d\sqrt{\eta}}{dt}\right]_{t=0}/IL\approx\sqrt{\eta_{s}}/\left( \tau_{w}\times IL\right) \tag{13}\]  
Figure 1:0  
These equations provide a simplified yet effective way to estimate the sensitivity and dynamic range in the context of the two-wavelength coupling experiment.
# Characteristic Volatile Composition of Seven Seaweeds from the Yellow Sea of China
## 2 Results and Discussion
### Headspace VOC Composition of Saweeds
#### 2.1.3 Headspace VOC Composition of Chlorophyta
prolifera_ were aldehydes, consisting of pentadecanal (8.49% in DVB/CAR/PDMS fiber; 24.4% in PDMS fiber), (_E_)-2-heptenal (9.64% in DVB/CAR/PDMS fiber) and tetradecanal (3.34% in DVB/CAR/PDMS fiber; 6.32% in PDMS fiber). It has been reported that significant differences were observed in the composition and types of VOCs in different harvest seasons for _E. prolifera_[24]. Differently from _E. prolifera_, the top three compounds with the highest content found in _U. lactuca_ were not aldehydes, but alkenes and a ketone, including 8-heptadecene (5.32% in DVB/CAR/PDMS fiber; 19.70% in PDMS fiber), (_E_)-\(\beta\)-ionone (8.69% in DVB/CAR/PDMS fiber; 18.13% in PDMS fiber) and 3-ethyl-1,4-hexadiene (10.17% in DVB/CAR/PDMS fiber). For _U. lactuca_, 30 VOCs were identified. Among them, 25 compounds also existed in _E. prolifera_, including 11 aldehydes (_(E)_-2-heptenal, 2,4-nonadienal, decanal, (_E_,_E_)-2-4-decadienal, undecanal, (_E_)-4,5-epoxydec-2-enal, tridecanal, tetradecanal, _Z_-11-pentadecanal, pentadecanal, (_Z_,\(Z\),_Z_)-7,10,13-hexadecatrienal); four norisoprenoids (\(\beta\)-cyclodextrin, \(\alpha\)-ionone, (_E_)-geranylacetone, (_E_)-\(\beta\)-ionone); four alkenes (3,5-dimethyl-1-hexene, 3-ethyl-1,4-hexadiene, 1,2-dimethyl-cyclohexpente, 8-heptadecene); two alkanes (1-butenylidene-cyclohexane, heptadecane); three alcohols ((_E_)-2-undecen-1-ol, 2,4-dimethyl-cyclohexanol, (_Z_,_Z_)-6,9-pentadecadien-1-ol); one ketone (6,10-dimethyl-2-undecanone) and one furan derivative (2-propyl-furan). The above common constituents in the two green algae covered almost all the VOC categories, differently from the red and brown algae. (E)-\(\beta\)-Ionone was the only compound found to existing in all seven algae characterized in this study and it is widely distributed in the plant kingdom. As a C\({}_{13}\) norisoprenoid, (E)-\(\beta\)-ionone can be produced by secondary metabolism of \(\beta\)-carotene and can also be produced by thermal degradation and photooxidation of carotenoids [25, 26]. It has been reported that (E)-\(\beta\)-ionone has a wide range of biological activities, including having a strong anticancer effect [27].
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 6 Sensitivity Analysis on Parameters
### Influence of ADTT  
The average daily truck traffic (ADTT) varied from 500 to 5000 in the analysis. The crack development was plotted against time, as shown in Figure 16. According to the result, when the ADTT increased from 500 to 5000, the whole C-F life was reduced by 35.5% in the HPS 70W and 57.3% in the 14 MnNbq. However, even under the extremely high ADTT (i.e., 5000), the whole C-F life in the HPS 70W was still significantly higher than that in the 14 MnNbq.  
For further analysis, the influence of ADTT on the initiation life and the propagation life are shown in Figure 17a,b, respectively.  
It could be found that with the increases in the ADTT, the propagation life was reduced by 73.4% in the HPS 70W and 77.1% in the 14 MnNbq steel. Meanwhile, the initiation life was reduced by 20.7% in the HPS 70W and 38.7% in the 14 MnNbq steel. Consequently, in addition to the notable impact on the propagation life, the ADTT also had a secondary effect on the initiation life in both types of steel. In this case, with the increase in the ADTT, the fatigue-related crack growth rate was increased. As a result, the competition time period \(t_{c}\) was reduced, which in turn affected the initiation life.  
Figure 15: Impact of pitting current on the C-F life: (**a**) Initiation life and (**b**) propagation life.
## 4 The Phase-Shifted Carriers Modulation
Therefore, to achieve the minimum current ripple for L2 and avoid deadlock situations, the phase shift angle should satisfy:  
\[\theta\ =\ (2\text{d}_{5}+0.25)\pi \tag{11}\]  
From the estimated inductor current ripples under different conditions presented in Table 2, a minimum of 50% reduction in the inductor L2 current ripple was achieved by implementing the PSCM technique. Similarly, the SC stage inductor (L1) current ripple can be reduced by implementing the PSCM technique. A minimum of 25% can be achieved as presented in Table 3, which represents the corresponding duty cycles for the SC and PV switches as well as the inductor L1 current ripple at each point, and the waveforms are shown in Figure 10b. It should be noted that when the voltage of the PV at MPP is 0.75 Vo, and Vsc is 0.5 Vo, the converter is forced to work at point B, away from MPP until SC charges and Vsc reaches 0.75 Vo, which may take a few tens of seconds, hence not affecting the PV energy harvesting. Figure 10: (a) battery stage gating and inductor voltage waveforms at the operation boundaries (**A–D**); (b) SC stage gating and inductor voltage waveforms at the operation boundaries (**A–C**).
## 2 The Air Operations Coordination Center  
In order to coordinate aerial activities, including those operated by drones, a coordination model was defined and tested, inspired on the lesson learned during the 2022 flood emergency experience in Emilia Romagna (Italy).  
The coordination center of overall air operations (CCOA) is established as point of reference of all flights, for the coordination of manned and unmanned operations, the operation authorization, the airspace reservation (i.e., no-fly zones, NOTAM, etc.), communications and feedback on the beginning and conclusion of each operation, etc. The CCOA, chaired and managed by the CNVF, ideally hosts representatives of the Air Force and of the Department of National Civil Protection and representatives of the Regional Civil Protection.  
The center was established at the Curno headquarters of the Lombardy Region, ordinarily managed by the CNVF for forest fire fighting activities; Air Force and National Civil Protection Department officers were only simulated for the exercise, while Lombardy Civil Protection function was delegated to the CNVF as regional coordinator of the Curno center.  
A branch office of the CCOA was established in Mantua and coordinated by the local CNVF unit, responsible for managing the manned and unmanned flights of the CNVF fleet, as well as authorizing the external institutions' unmanned flights, through the technical coordination center of the external UAS networks (UAS CCT), also based in Mantua, which managed operators and teams for activities assigned to the UAS networks by the CCOA.  
The UAS networks CCT collected requests relating to the technical activities assigned by the CCOA, monitored the progress of the activities themselves, supported the field teams (including monitoring the D-flight portal), and managed the delivery of the requested products to the relevant stakeholders.  
Figure 2 illustrates the communication diagram flow.
# Classic Discrete Control Technique and 3D-SVPWM Applied to a Dual Unified Power Quality Conditioner
## 3 Design of Controllers
### Three Dimensional Space Vector Pulse Width Modulation (3D-SVPWM)  
This work proposes the application of a vector modulator for a three-phase split capacitor inverter called three-dimensional three-branch space vector pulse width modulation (3D-3B-SVPWM) [35]. This algorithm has been applied to three-phase inverters switched in parallel connection to compensate current. However, SVPWM modulation is a technique for sinusoidal references; in the case of shunt current compensators, the reference to be generated by the inverter is highly distorted, so the results obtained from the application of this technique do not make sense to give similar results to the SPWM sinusoidal modulation.  
The simulation scheme of the controllers is illustrated in Figure 14. In the case of the current controller, the voltage error has been converted to the synchronous reference frame. This transformation allows the use of a single controller for the three phases in the power grid, controlling only the phase components, because the quadrature and zero components are null.
## 3 Results and Discussion
### Results of the Treatment System Validation Experiments
#### 3.1.2 In Vitro Validation Experiments for Treatment Accuracy  
Figure 12a shows the ear margin vein of the rabbit to be treated after fur removal, and Figure 12b reveals that the skin in the treated area is in a white degeneration state compared with other areas in the path of the vein. This indicates that the treatment area is limited to the vascular area and does not cause burning of the surrounding tissue. The white degeneration state is believed to be a type of skin burn. Figure 13a displays an ultrasound image of the marginal ear vein in an untreated rabbit ear, while Figure 8b shows the same vein after treatment. Contraction and deformation of the blood vessel wall can be observed at the treatment point in Figure 13b. Furthermore, the internal diameters of the blood vessels were measured using the built-in measurement system of the Vevo 2100. The diameter of the untreated area was approximately 0.49 mm. After treatment, the blood vessel contracted to 0.22 mm, and the shrinkage rate was approximately 55%, which is consistent with the observed vascular degeneration results in Figure 12b. This confirms the accuracy of the treatment.  
In in vitro validation experiments, due to the small size and slow blood flow speed of the rabbit ear margin veins compared to human veins, the temperature rises in the area quickly, and contraction of the superior and inferior vascular walls is observed. It is also found that if the treatment power is increased to pursue fast treatment, not only would the tissue and skin near the blood vessel be burned, but also the blood vessel would be directly ruptured and cause massive bleeding. Further research and consideration are needed to balance the treatment power and adopt appropriate cooling measures to protect the skin from burns.  
Figure 12: Comparison of rabbit marginal ear vein: (**a**) before treatment; (**b**) after treatment.  
Figure 13: The contrast image of rabbit ear vein contraction: (**a**) before treatment; (**b**) after treatment.
# Dynamic Analysis of Tethered Satellites with a Payload Moving Along a Flexible Tether
## 4 Dynamic Response Analysis
Since the payload starts at satellite B, fluctuations in the axial force begin near the 18 km position at attitude 2 of Figure 7b (Figure 11b). These fluctuations expand towards satellite A at attitudes 3 and 4 (Figure 11c,d). Similar to the observations in Figure 10, Figure 11 shows that the amplitude of these axial force fluctuations increases as the payload approaches and eventually reaches satellite A. The amplitude of the axial force fluctuation in Figure 11j is smaller than that in Figure 10j because the Coriolis force is greater when the payload approaches satellite B than when it approaches satellite A.