esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response Mitigation of Base-Isolated Buildings
## 6 Conclusions  
Seismic response mitigation of base-isolated (BI) building installed with a single tuned mass damper (STMD), multiple TMDs (MTMDs), and distributed MTMDs (d-MTMDs) are investigated. Numerical analysis of two BI buildings equipped with TMDs is performed using analytical models of 5- and 10-storey buildings. Results were analyzed to investigate effectiveness of different TMD schemes in reducing displacement and acceleration demand of the BI structure. Such effectiveness was investigated in terms of percentage reduction in response as a function of ground motion frequency content, probability distribution functions of response parameters, and fragility curves corresponding to failure of the isolation device due to excessive displacement. The following conclusions can be made from the results of the analyses.  
1. TMD schemes are can be used to control bearing displacement of BI buildings subjected to earthquake ground excitations without compromising the control in acceleration response achieved by BI.
2. MTMDs and d-MTMDs are marginally better than STMD in controlling the bearing displacement of the BI building. The effectiveness of MTMDs and d-MTMDs are same as STMD for mitigating top floor acceleration.
3. The TMD schemes reduce the fragility of the structure by about 5% in a wide range of PGA of 0.5g to 1g. For weaker and stronger shaking, the reduction in fragility is not significant. It is to be noted that the TMDs used in this study are not optimized for specific type of ground motions and might experience detuning effects for some ground motions. Designing such TMDs based on  
Figure 11: Bearing displacement simplified fragility curves for 10-story base-isolated building equipped with TMDs.  
effective period of vibration of a structure for a well-established target displacement obtained from appropriate hazard analysis might provide additional benefits.  
All authors discussed and agreed upon the idea, and made scientific contributions. [PERSON] coded the MATLAB program and wrote initial draft of the paper. [PERSON] and [PERSON] improved and revised the initial paper. All authors have read and agreed to the published version of the manuscript.  
It was part of the employment activity no extra funds were given to this study.  
Authors acknowledge the efforts of reviewers and editors for improving the quality of the paper. First author acknowledges the department of Civil and Environmental Engineering, Old Dominion University, for allowing him to use the work stations and space. Second and third authors acknowledge support from the University of Iceland Research Fund.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Drip Loss  
Drip loss was analyzed via the exudate absorbed into the quantitative filter paper during the thawing process under three different conditions: room temperature, running water, and HFD. Table 2 shows the drip loss results of frozen AMPS and CSM. HFD resulted in the lowest drip loss value of both AMPS and CSM. The drip loss values of AMPS from the high-frequency defroster were significantly different from those thawed using the conventional methods (\(p<0.05\)). The highest drip loss value was observed by thawing at room temperature, followed by running water.  
The thawing process for frozen fish and fishery products should be performed as quickly as possible. Water changing from its original position during the thawing process leads to drip loss, resulting in a dry, stringy, and less tasty fish. Nutrient losses, such as proteins, vitamins, and minerals, can occur with drip loss [33], with high drip loss often linked to protein denaturation. In addition, high drip loss also decreases attractiveness, nutritional value, texture, and appearance [34].  
Applying technology during the thawing process may improve the quality of fishery products, also likely decreasing thawing time [35]. Furthermore, rapid thawing can maintain fish quality [36] and reduce mechanical damage to cell membranes by decreasing recrystallization [37]. Recrystallization, which can be problematic during both thawing and freezing, leads to cellular damage, increasing drip production [38].  
Therefore, HFD is considered an effective option to decrease drip loss and maintain the quality of raw materials in the test HMR product. The results of the present study confirm the effectiveness of HDF compared to conventional methods.
## 5 Discussion
Additionally, the removal of sampling rate flexibility resulted in slightly longer solving times, emphasizing the computational advantage of allowing variable inspection levels. This reinforces the model's ability to adapt to different operational scenarios, optimizing both quality and efficiency. The Sensitivity Analysis offers further depth by illustrating how variations in demand levels and defect probabilities influence the model's performance. The greatest cost increases from excluding machine vision occurred under both low- and high-demand conditions, highlighting the importance of automated inspection systems in both low-volume, high-efficiency contexts and high-volume, high-risk environments. These findings indicate that machine vision's speed and scalability are essential for maintaining throughput and minimizing defects in diverse production settings. Moreover, the analysis reveals that demand levels are the dominant factor affecting performance variability, with defect probabilities playing a secondary role. This suggests that production managers should align technology adoption with demand forecasting and capacity planning, ensuring that inspection strategies are tailored to the specific conditions of the production environment. While the current study already includes a comprehensive analysis of the most impactful variables, such as demand and defect rates, future research could explore extending this analysis to include additional secondary variables or multi-factorial scenarios. Such analyses could provide deeper insights into the interactions between various factors, although doing so in this paper might risk complicating the results and potentially confusing the reader. Thus, the focus on the most significant variables ensures that the findings remain clear and accessible. Future work could expand on this by incorporating a wider range of scenarios or applying the model to other industries, which would provide further evidence of its versatility and applicability. In conclusion, this study demonstrates that an optimization model incorporating technology selection and flexible sampling rates can significantly improve inspection planning outcomes. Machine vision proves to be a cornerstone of modern inspection strategies, while the ability to adapt sampling rates enhances operational efficiency and resource management. These findings suggest that smart, data-driven quality control systems, which move beyond traditional one-size-fits-all approaches, can better balance quality assurance objectives with operational constraints. This approach allows practitioners to adopt more agile and cost-efficient strategies, paving the way for more effective quality control systems in diverse production environments.
# Parametric Analysis of the Shear Lag Effect in Tube Structural Systems of Tall Buildings
## 5 Parametric Analysis
### The Impact of Wind Load Type on the Shear Lag Effect  
In this chapter, the impact of the type of the horizontal load profile on the shear lag effect will be analyzed. The constant parameters include the beams cross section dimensions b/h = 0.5 m/1.0 m, column cross section dimensions c = 0.5 m, column spacing s = 2.4 m, the entire buildings height H = 126 m, storey height H\({}_{1}\) = 3.5 m and building layout dimensions A/B = 24 m/43.2 m. The building layout dimensions were chosen differently from the previous analyses, so that the difference in effect of different horizontal load profiles is most apparent. Two horizontal load profiles shall be considered: the continuous wind load with a constant value throughout the building's height and the actual wind load profile calculated using EN 1991-1-4 [26]. The constant load profile includes the use of the maximum wind load force that is calculated for the top of the building after which that load is used throughout the building height (Figure 12b). The actual wind load profile  
Figure 11: The change in the shear lag effect with the variation of floor dimensions (\(\beta\)—A/B diagram).  
(Figure 12a) is calculated using EN 1991-1-4 [26]. As it was the case in prior analysis, the same seven storeys will be considered. The results of the analysis are shown in Figure 13.  
Figure 12: (**a**) The actual wind load profile and (**b**) the constant wind load profile.  
Figure 13: The change in the shear lag effect with the variation of the wind load profile (C-continuous wind load with a constant value, AP-actual wind load profile).  
According to the results the following can be concluded: (1) The 2 nd, 6 th and 12 th storey are found in the positive shear lag effect area (\(\beta<1\)). In this area the shear lag effect is more significant (smaller \(\beta\)) for the constant value (C) of the wind load which is most clearly seen on the 2 nd storey of the building. As the building rises the gap between two wind load profiles regarding the positive shear lag effect diminishes with the positive shear effect being more prominent for the actual wind load profile on the 12 th storey. (2) All storeys above the 12 th storey are found in the negative shear lag effect area (\(\beta>1\)). In this area the shear lag effect is more significant (larger \(\beta\)) for the constant value (C) of the wind load than for the actual wind load profile. As the building rises, the gap between two wind load profiles regarding the negative shear lag effect increases until the 30 th storey after which the gap slightly diminishes to the buildings top. It can be deduced that the positive and negative shear lag effect are more significant for the continuous wind load with a constant value in most storeys of the structure.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 2 Maritime Scenario and Problem Statement
### Coverage Status in the 3.5 GHz Band
* Finally, if there is a prolonged lack of a synchronization reference in TDD networks, there is a high risk that the network will be disabled. Robustness against this situation can be obtained by means of having multiple synchronization sources for redundancy: GPS, PTP via a transport network, operating local high-precision clocks, etc. Another solution for operating in non-synced environments is operating with extended guard bands. However, this would imply a significant reduction in usable bandwidth. * Some geographical areas have restrictions to using the 3.5 GHz band due to other equipment being operated on the same frequency.
###### Abstract  
Background: A generative adversarial network (GAN) has gained popularity as a data augmentation technique in the medical field due to its efficiency in creating synthetic data for different machine learning models. In particular, the earlier literature suggests that the classification accuracy of a convolutional neural network (CNN) used for detecting brain tumors in magnetic resonance imaging (MRI) images increases when GAN-generated images are included in the training data together with the original images. However, there is little research about how the exact number of GAN-generated images and their ratio to the original images affects the results obtained. Materials and methods: Here, by using 1000 original images from a public repository with MRI images of patients with or without brain tumors, we built a GAN model to create synthetic brain MRI images. A modified U-Net CNN is trained multiple times with different training datasets and its classification accuracy is evaluated from a separate test set of another 1000 images. The Mann-Whitney U test is used to estimate whether the differences in the accuracy caused by different choices of training data are statistically significant. Results: According to our results, the use of GAN augmentation only sometimes produces a significant improvement. For instance, the classification accuracy significantly increases when 250-750 GAN-generated images are added to 1000 original images (\(p\)-values \(\leq\) 0.0025) but decreases when 10 GAN-generated images are added to 500 original images (\(p\)-value: 0.03). Conclusions: Whenever GAN-based augmentation is used, the number of GAN-generated images should be carefully considered while accounting for the number of original images.  
Keywords:brain tumor; convolutional neural network; generative adversarial network; magnetic resonance imaging +
Footnote †: journal: _sciences_
## 4 COIN-LEO Algorithm  
Each satellite can be viewed as an independent worker node. We aim to design an algorithm that can split the model into several submodels and deploy different submodels to different worker nodes. These submodels are deployed to several worker nodes for distributed inference, forming a pipelined inference cluster.  
The proposed COIN-LEO algorithm mainly consists of two parts. The first part is to select appropriate satellites to participate in collaborative inference. The second part is to determine the specific model-splitting positions in order to deploy submodels to the above-selected satellites. To predict the inference time for different submodels of varying sizes under different resource-occupation conditions of each satellite, we design a DNN that can make accurate predictions based on the satellite's resource occupation.
## 1 Introduction
First, we evaluate the predictive capability of sparse data, including age, blood pressure, temperature, heart rate, and blood glucose, for identifying maternal health risks using publicly available vital signs data from rural Bangladesh. Next, we compare the performance of various machine learning models and sampling methods using statistical tests to determine the best performing model to predict maternal health risk. We hypothesize that machine learning algorithms utilizing sparse data will effectively predict maternal health risk. Furthermore, we hypothesize that the ensemble model utilizes stratified sampling to outperform the other models and sampling approaches. The overarching aim of this study is to contribute to developing the literature on data-driven healthcare tools that can improve maternal outcomes and health equity and reduce global health disparities. The findings could pave the way for scalable applications in LMICs, offering healthcare providers robust, predictive tools. The paper is structured as follows: we first describe the data used in the study, outline the data preprocessing steps and features, machine learning model development and hyperparameters, performance evaluation metrics and statistical analysis used for model comparison. Next, we present findings detailing the performance of individual models under different sampling techniques, the effectiveness of the ensemble approach, and insights from the comparative analysis. Finally, we provide recommendations and future research directions, focusing on deploying machine learning models and practical challenges associated with scaling the models developed in this research in resource-constrained settings for predicting maternal health risk.
## 4 Experimental Results and Analysis
### Visualization Analysis
#### 4.2.3 OD Distribution Visualization Analysis  
The Origin-Destination (OD) matrix serves as an important tool for understanding the spatial distribution of traffic demand. Through visualization analysis of the extracted OD matrix, major traffic flows and key OD pairs can be identified.  
The vehicle OD distribution diagram (Figure 15) employs a heat map format to display OD flows between intersections. Several characteristics are clearly evident from the diagram: First, OD distribution exhibits pronounced asymmetry, reflecting directional traffic flow characteristics during morning peak periods; second, several high-flow OD pairs exist, constituting the primary traffic flows within the network; finally, certain intersections (such as 1, 9, and 17) serve as major traffic generation or attraction points, maintaining strong connections with multiple other intersections.  
Spatial pattern analysis of OD distribution reveals the functional structure of the road network. Peripheral intersections (such as 1, 2, and 3) primarily function as traffic generation points, channeling traffic flow toward inner intersections; central area intersections (such as 9, 10, and 11) mainly serve as traffic attraction points, receiving traffic flows from various directions. This \"periphery-to-center\" OD pattern represents typical centripetal commuting traffic characteristics.  
Figure 14: Distribution of average flows by direction for each link.  
Based on OD distribution characteristics, signal control optimization should focus on paths of high-flow OD pairs. Through path analysis, identify the primary travel routes of these OD pairs, ensuring signal coordination along these paths to reduce stops and delays. Meanwhile, for lower-flow OD pairs, their operational efficiency can be appropriately sacrificed to ensure the smooth flow of major traffic streams.
# Laser Cooling and Trapping of Rare-Earth-Doped Particles
## 2 Laser Cooling of Bulk Rare-Earth-Doped Solids
The absorption cross-section is proportional to the Voigt spectral line \(\sigma_{a}(\
u,T)\ \sim\ g_{\
u}(\
u,T)\), which is a result of the convolution of two broadening mechanisms: one that causes a Gaussian broadening and the other that produces a Lorentzian profile. Consequently, the resonant absorption coefficient can be presented as  
\[\alpha_{r}(\
u,T)\ \sim\ g_{\
u}(\
u,T)\cdot exp(-E_{0i}/\kappa_{B}T) \tag{8}\]  
The inhomogeneous broadening is dominant for glass hosts as a result of structural disorder in glasses. It almost does not change with temperature, that is, \(g_{\
u}(\
u,T)\) is almost constant and  
\[\alpha_{r}(\
u,T)\ \sim\ exp(-E_{0i}/\kappa_{B}T) \tag{9}\]  
In glasses, the peak absorption coefficient \(\alpha_{r}\) decreases exponentially with temperature. In crystal hosts, homogeneous broadening is dominant. The homogeneous linewidth scales as \(\ \sim T^{2}\) and is associated with the two-phonon Raman scattering in the temperature range of 40-300 K. The peak absorption coefficient is proportional to  
\[\alpha_{r}\ \sim\ T^{-2}\cdot exp(-E_{0i}/\kappa_{B}T) \tag{10}\]As one can see in relations (9) and (10), when the temperature decreases, the peak absorption coefficient \(\alpha_{r}\) in crystals is higher than in glasses. Crystal hosts are the best choice for laser cooling. Today's record temperature of optical refrigeration is \(93\pm 1\) K. It was reached with a 10% Yb\({}^{3+}\):YLF sample [25].
###### Abstract
It can be modulated with a standard laser driver up to 100 kHz or with a special switching unit up to 1 GHz [26]. However, there is no further information about this unit. The main goal of this study is to experimentally determine the capabilities of currently developed state-of-the-art high-power pulsed QC lasers. The described technology enables designing data links with high-power lasing structures operating at the wavelength of 4.5 \(\upmu\)m with a peak pulse power of 3 W. Concerning the analytical part of this work, the performance parameters of the designed OWC system for different weather conditions are analyzed. Finally, some laboratory tests of this link are presented. The obtained results are \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Wavelength [\(\upmu\)m]** & **Modulation** &
\begin{tabular}{c} **Power/** \\ **Current** \\ \end{tabular} & **Mode Type** & **Sensor** & **Lit.** \\ \hline \multirow{4}{*}{A} & 8 & 2.5 Gb/s & 10 mW (0.3 + 7) A & Bias-T (cw OWC) & MCT & [17] \\  & 9.7 & Analog 1.5 GHz & 10 mW & RF injection (cw) & MCT & [1] \\  & 4.65 & Analog 23 MHz & 60 mW & Bias-T (cw OWC) & MCT & [18] \\  & 4.7 & Analog 30 MHz & 12 mW (0.35 + 0.1) A & Bias-T (cw OWC) & MCT & [19] \\  & 4.8 & Analog 10 MHz & 200 mW (0.65 + 0.4) A & Bias-T (cw OWC) & T2 SL & [20] \\  & 4.6 & Analog 1 MHz & 2.2 W & Pulsed (76\%) &? & [21] \\  & 4.8 & Analog 50 kHz & 20 W & QCL-PA & MCT & [22] \\ B & 4.36 & Analog 20 kHz & 40 W & QCL-PCDF & InSb & [23] \\  & 9.2–9.8 & Analog 10 kHz & 2 \(\div\) 10 W & QCL-MOPA & MCT & [24] \\ \hline C & 9.35 & Analog 4 MHz & 300 mW & Pulsed & MCT & [4] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MWIR radiation sources and detectors applied to transmit optical signals in a free space. unique considering both power and modulation speeds. Regarding frequencies of 5 MHz and 7 MHz, the optical peak power is 1.27 W and 0.64 W, respectively.
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
## 3 Results  
Using the reference frame described in Section 2, this section describes and evaluates the three approaches proposed for impact frequency identification using MCSA: side bands analysis, waterfall plots, and envelope analysis.
## 5 Discussion
Where coincident data exist, particular effort could be focused on comparisons and analyses that extend beyond the Tarim River Basin for many years. The multiday filling method used herein has the dual function of replacing pixels with large SZA values with reliable SCA data and removing the extensive cloud cover from the MOD10A1 dataset. The paired-date method was applied to investigate the existence of overestimation of snow cover in MOD10A1 data and to identify a linear correlation between increases in SCP and SZA. An increase in the overall accuracy also showed that our modified product constituted a significant improvement on the original dataset, and further verified the existence of the SZA overestimation effect. However, other influential factors should also be taken into consideration, such as the effects of topography, solar geometry, time of acquisition, error in mapping from projection, and the shading effect caused by the difference between forward- or back-scatter directions [37; 38; 39]. In particular, the shading effect in mountainous areas is of prime importance, because considerable subpixel topographic variation occurs within a given visible area. Nonetheless, this is likely less important for our study because of the low-resolution sensor used to collect the MOD10A1 data. The daily fluctuation of the solar zenith angle in the Tarim River Basin was more stable than that of the SZA due to the fixed acquisition time. An improvement to the sun-angle correction was also made in MOD10A1 to correct for within-orbit variation, as seen in the ground-based control point residuals [40]. As such, we did not consider these factors in this study, although long-term seasonal changes in the solar zenith angle will cause it to increase, which will also affect these variations.
## 4 Example Analysis
### Mesh Quality Analysis and Runtime Comparison
In contrast, the Delaunay method inserts the same number of points in each loop, which leads to the need to constantly judge the size of the average of the triangle and the boundary triangle area in the algorithm, and so the CPU running time will also increase. In this section, we applied the proposed Delaunay-TCOGIM algorithm and the Delaunay method to a specific typical mountainous terrain. By comparing the mesh quality metrics and CPU running time discussed in Section C, we saw that the Delaunay-TCOGIM algorithm produces a higher-quality mesh compared to the Delaunay method. The CPU running time of the Delaunay-TCOGIM algorithm is lower than the CPU running time of the Delaunay method for a certain number of cycles with the same discrete points. Therefore, under a reasonable selection of the number of cycles and multiplier, the Delaunay-TCOGIM algorithm demonstrates better applicability to the mountainous complex terrains with high terrain differences and can effectively improve the quality and efficiency of the Delaunay triangular mesh.