esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
###### Abstract  
Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure's performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building's behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.  
damaged buildings; earthquake safety assessment; soft computing techniques; rapid visual screening; Machine Learning; vulnerability assessment +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Biocatalytic Synthesis  
The synthesis of NPGDL was carried out in a 50 mL jacketed open-air batch reactor. All reactions were carried out in a solvent-free medium. For that purpose, 20 g of a mixture of LA and NPG in stoichiometric ratio (2:1, acid:alcohol) was placed in the reactor and allowed to melt while stirring with an IKA(r) RW-11 basic Lab-Egg vertical stirrer set at 350 rpm. Amounts of 0.375, 0.75, and 1.5 g (concentrations of 1.88, 3.75%, and 7.5% \(w/w\) referred to substrates) of both immobilized derivatives were tested. Temperatures ranging from 50 to 80 \({}^{\circ}\)C were evaluated for Novozym(r) 40086 but experiments with Novozym(r) 435 were performed using temperatures from 60 to 90 \({}^{\circ}\)C. In order to follow the progress of the reaction, the acid value of several samples withdrawn from the reactor during the esterification process was measured.  
The acid value (AV) is defined as the milligrams of potassium hydroxide necessary to analyze the free acids contained in 1 g of sample [31]. According to this, conversion can be calculated as:  
\[\text{Conversion }(\%)=\frac{\text{AV}_{0}-\text{AV}_{\text{t}}}{\text{AV}_{0 }}\times 100\]  
where AV\({}_{0}\) is the acid value determined at the beginning of the reaction; and AV\({}_{\text{t}}\) is the acid value of a sample taken at a certain time.  
All experiments were performed in triplicate and the results shown in this paper are the mean value of the three measurements, including within the graphs the error bars (\(\pm\)standard deviation). When stoichiometric substrates ratio is used and high conversions (>95%) are achieved in a solvent-free reactor, the final product is the ester of interest and can be used without further purification.  
Figure 1: Scheme of the enzymatic esterification process between NPG and LA.
# Transformation of Trigonometric Functions into Hyperbolic Functions Based on Cable Statics
## 4 Application of Hyperbolic Function Transformation
### Electricity  
Determine the voltage of the system knowing that \(I=(2+j3)\) A and \(Z=(30-j70)\)\(\Omega\), where \(V=IZ\), and the hyperbolic angle is derived as:  
\[\theta_{hI}=\sinh^{-1}\frac{C.O_{\cdot I}}{C.A_{\cdot I}}=\sinh^{-1}\frac{3\; \mathrm{A}}{2\;\mathrm{A}}=1.1947. \tag{65}\]  
The value of \(I\) is determined as:  
\[|I|=\frac{C.O_{\cdot I}}{\tanh\theta_{hI}}=\frac{3\;\mathrm{A}}{\tanh 1.1947}=3.605 \;\mathrm{A}. \tag{66}\]  
The hyperbolic angle of the impedance is determined as:  
\[\theta_{hZ}=\sinh^{-1}\frac{C.O_{\cdot Z}}{C.A_{\cdot Z}}=\sinh^{-1}-\frac{70 \;\Omega}{30\;\Omega}=-1.5835. \tag{67}\]  
The magnitude of the impedance is calculated as:  
\[|Z|=\frac{C.O_{\cdot Z}}{\tanh\theta_{hZ}}=-\frac{70\;\Omega}{\tanh-1.5835}=76. 1577\;\Omega. \tag{68}\]  
For the sum of angles, the following expression is used:  
\[\theta_{hV}=\tanh^{-1}\frac{\sinh\theta_{hI}+\sinh\theta_{hZ}}{\cosh\theta_{ hI}\cosh\theta_{hZ}}=-0.184. \tag{69}\]  
The following equation expresses the voltage value as the product of the current and the impedance:  
\[V=IZ=|I||Z|(\mathrm{sech}\,\theta_{hV}+i\tanh\theta_{hV}). \tag{70}\]  
By substituting the values, the result is obtained. The values have been calculated with the decimals of a fx-9750 GII calculator from the manufacturer Casio (Tokyo, Japan) to obtain the exact value:  
\[V=IZ=|3.605\;\mathrm{A}||76.1577\;\Omega|(\mathrm{sech}\,-0.184+i\tanh-0.184) =(270-i50)\;\mathrm{V}. \tag{71}\]  
To verify the solution, the following calculation can be used:  
\[V=(2+i3)\;\mathrm{A}\times(30-i70)\;\Omega=(270-i50)\;\mathrm{V}. \tag{72}\]
# A Feature-Based Cost Estimation Model for Wind Turbine Blade Spar Caps
## 4 Results and Discussion
### Manufacturing Cost Data  
Manufacturing cost data was supplied as upper and lower limits for both CFRP and GFRP (Table 8, Figure 5a,b). Model data for CFRP and GFRP plotted on the same graphs showed the same quadratic relationship. Correlation coefficients are 0.99 and 1 for the model and manufacturing data, so the assumptions of equal Cost Factor Indices for both CFRP and GFRP, and constant complexity with spar cap length appear valid. Mid-point manufacturing data was compared with the model to derive cost curves for GFRP and CFRP (Table 9, Figure 5c,d). Figure 5a,c show good agreement between model-derived CFRP cost data and the manufacturer's data, with an average difference of 6.8% between the model and mid-point data. GFRP shows a significant 20.6% deviation between the model and mid-point data (Figure 5b,d), as the model increasingly underestimates the cost for GFRP spar caps above 55 m compared with the manufacturer's estimate.  
Figure 5: Comparison of model-generated manufacturing cost data with cost data from a spar cap manufacturer. (**a**) carbon reinforced polymer composite (CFRP) model and manufacturer data. (**b**) glass fiber reinforced polymer composite (GFRP) model and manufacturer data. (**c**) CFRP model and mid-range manufacturer data. (**d**) GFRP model and mid-range manufacturer data.
# System for Indoor Comfort and Health Monitoring Tested in Office Building Environment
###### Abstract  
The use of smart technologies and the Internet of Things (IoT) is becoming more and more popular in order to enhance the overall building performance by monitoring parameters related to occupants' comfort and health in the built environment. A new modular, custom-made and replicable IoT system is proposed based on an Arduino development board (MKR WiFi 1010) connected to the Arduino IoT Cloud. An Application Programming Interface (API) enables the integration of this system with other possible ones, thus making the system modular, custom-made and replicable. A series of parameters were simultaneously monitored over a 7-day period in two office spaces and a photovoltaic (PV)-testing laboratory. While the meteorological and comfort parameters (temperature, relative humidity, CO\({}_{2}\)) were monitored in all three spaces, the health parameters (total volatile organic compounds--TVOCs; formaldehyde--HCHO; particulate matter--PM; and radon--222 Rn) were monitored only in an office setup located right next to a Chemical Analysis and Testing Laboratory. Generally, the registered values of the health parameters fell within the recommended thresholds. However, the thermal comfort parameters were constantly exceeded: over 90% of the working time in the two office spaces and 83.33% in the PV-testing laboratory. Still, the optimal relative humidity values in the monitored spaces contributed to the discomfort reduction in the occupants. Also, CO\({}_{2}\) and TVOCs had some exceptions in particular conditions. CO\({}_{2}\) values of up to 1500 ppm due to poor ventilation and TVOC levels of up to 1000 ppb related to chemical experiment development were registered. Also, several other peaks were recorded when monitoring HCHO as well as PM. Thus, special attention must be paid to natural ventilation or to the improvement of building characteristics. Also, the time intervals when experiments in the Chemical Analysis and Testing Laboratory are carried out should be communicated to other personnel from the nearest offices. The testing of the monitoring system over a one-week period showed that the proposed solution operated adequately, representing a reliable tool for data acquisition via the Arduino IoT Cloud.  
IoT; monitoring system; comfort; indoor air quality; carbon dioxide; formaldehyde; total volatile organic compounds; suspended particulate matter; radon; Arduino board +
Footnote †: journal: Applied Sciences
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 2 Related Work
We demonstrated LoRa's feasibility as a multihop relay technology without introducing additional delay at relay nodes. Lastly, we discussed the effectiveness of gossip routing by exploring various transmission probabilities.
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 2 CDRs and Development
### Calibration and FCDR  
The cross- and inter-satellite calibration along with reprocessing is critical for the generation of all the FCDRs, since observations from multiple satellite platforms need to be smoothly and seamlessly stitched together to form a long-term homogenized record [6]. Current satellite instruments generally  
Figure 1: Schematic diagram of production pathways for various climate data record (CDR) products (ICDR—interim CDR, FCDR—fundamental CDR, TCDR—thematic CDR, and CIR—climate information record) and their relative dependence along with their relationship with near-real time products: Sensor Data Records (SDRs) and Environmental Data Records (EDRs). Dash lines indicate optional paths.  
have more advanced on-board calibration technology, which leads to better calibration accuracy and precision, so they are used to retrospectively calibrate their predecessor instruments [7,8].  
Aside from the on-board calibration, vicarious calibration is normally needed to characterize the current satellite instrument performance and bring their radiometric measurements to climate quality before being used to cross- and inter-calibrate with legacy satellite instruments. Both artificial and natural sites, such as the under-orbit airplanes, deserts, ice domes, deep convective clouds (DCC), and the Moon [9, 10, 11, 12, 13, 14, 15] have been used for the post-launch vicarious calibration of satellite sensors. This vicarious calibration and on-board calibration strive to match the satellite radiometric measurements to the international standards (SI) [16, 17, 18].  
Reprocessing inter- and cross-calibrated satellite radiometric measurements using Sensor Data Record (SDR) production schemes will bring the SDRs to climate quality FCDRs. FCDRs are generally produced for orbital swath since they are mainly used as the inputs for the follow-on production of TCDRs. However, some FCDRs are produced by merging and mapping several observations into a defined grid to enhance its spatial and temporal coverage and resolution, such as the Geostationary Grided Satellite (GridSat) IR Channel Brightness Temperature FCDR [19].
# Analysis of Drone Flight Stability for Building a Korean Urban Air Traffic (K-UAM) Delivery System
## 1 Introduction
The Ministry of Land, Infrastructure, and Transport has established the 'K-drone delivery promotion team' to commercialize delivery services using drones in urban areas and to present a standard model, and has announced 'Drone Delivery Guidelines' to conduct pilot projects targeting some local governments. The above 'Drone Delivery Guidelines' stipulate that drones used for delivery services must conduct flight tests to ensure that they maintain their flight path and altitude as part of safety measures to prevent damage caused by collisions with ground personnel or obstacles in urban areas, and that a decision is made on whether or not to deploy them for delivery work based on the results of the tests. Therefore, in this study, we collected and analyzed drone flight data from selected demonstration cities as part of the K-drone delivery commercialization project. The drone flight was measured for flight and hovering along the delivery route, and an error analysis on the stability of the flight was performed for submission to a national certification agency. The structure of this paper is as follows: Section 2 describes the K-drone delivery demonstration project. Section 3 introduces coordinate transformations to facilitate flight data calculations. Section 4 details the path flight of the drone, and Section 5 provides an in-depth description of the analysis procedures and error analysis related to hovering.
## 5 Results and Discussion
### Runtime Comparison  
After applying different clustering methods and feature selection algorithms on the spike sequences, we observe that \(k\)-means and \(k\)-modes are performing better than the other clustering methods in terms of weighted \(F_{1}\) score and \(k\)-means and fuzzy in terms of other clustering quality metrics. However, it is also important to study the effect of runtime for these clustering approaches so that we can evaluate the trade-off between the \(F_{1}\) score and  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithm** & **Number of Clusters** \\ \hline HDBSCAN & 2016 \\ HDBSCAN + Boruta & 1419 \\ HDBSCAN + LASSO & 1838 \\ HDBSCAN + RFF & 1947 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Number of clusters for HDBSCAN clustering with different feature selection methods.  
Figure 5: (**a**) \(t\)-SNE plots for the original variants as labels; (**b**) \(t\)-SNE plot with labels obtained after applying HDBSCAN without any feature selection method on the frequency vectors.  
Figure 6: \(t\)-SNE plots for HDBSCAN without and with feature selection methods.  
the runtime. For this purpose, we compute the runtime of different clustering algorithms for both one-hot embedding and \(k\)-mer feature vectors without and with feature selection methods. Figure 7 shows the runtime for all five clustering methods without applying any feature selection on the data. We can observe that applying \(k\)-modes is very expensive in terms of runtime and \(k\)-means takes the least amount of time to execute. Similar behavior is observed in Figure 8-10 for RFF, Boruta, and LASSO regression, respectively. This behavior shows that, although \(k\)-modes and fuzzy are performing better in terms of \(F_{1}\) score and clustering quality metrics, \(k\)-modes is an outlier in terms of runtime, and fuzzy is more expensive than \(k\)-means. This behavior also shows the effectiveness of the \(k\)-means algorithm in terms of \(F_{1}\) score, clustering quality metrics, and also in terms of runtime.  
Figure 8: Runtime for different clustering methods (random Fourier feature selection). The x-axis shows number of clusters.  
Figure 7: Runtime for different clustering methods (no feature selection method). The x-axis shows number of clusters.
## 3 Results  
### Metagenomic Insights into Microbial Diversity of the Antarctic Ocean  
Metagenomic analysis was employed to characterize the marine microbial diversity at two distinct vertical depths (16 and 25 m) at the phylum level. The predominance of _Pseudomonad_ was observed at both depths, followed by an assemblage of unclassified bacteria, namely _Plantomycetota_, _Acidobacteria_, and _Bacillusota_ (Figure 1A), corroborating findings from previous studies [25]. Intriguingly, the phylum _Candidatus Sumerlaeva_, which has only recently been described and seldom reported [26], along with _Nitrososphueroa_[27], demonstrated a higher relative abundance at the 25 m depth compared to 16 m. Additionally, differential microbial abundance analysis, employing transcripts per million (TPM) as a metric, identified unique _Candidatus_ bacterial and archaeal taxa at both depths (Figure 1B,C).  
Diversity of Carotenoid Biosynthetic Enzymes in the Antarctic Ocean Revealed by Metagenomic Analysis  
Metagenomic analysis revealed a diverse array of genes encoding enzymes associated with the carotenoid biosynthesis pathway at depths of 16 and 25 m, annotated according to the KEGG pathway database (ID ko00906) and illustrated in Figure 2. These carotenogenic genes were broadly present across both sampled depths, with certain genes exclusively identified at specific depths (in Figure 2, genes unique to 16 m are denoted in red and genes unique to 25 m in purple).  
Figure 1: Relative microbial abundances at depths of 16 and 25 m. (**A**) Overall relative distribution of microbial taxa by phylum. (**B**) Phyla with differentially higher dominance at 16 m relative to 25 m, quantified using transcripts per million (TPM). (**C**) Phyla with differentially higher dominance at 25 m relative to 16 m, quantified using TPM.  
Subsequently, the relative abundance of these genes was quantified. Notably, genes encoding \(\beta\)-carotene ketolase, which is essential for the biosynthesis of C40 carotenoids, was predominant at both depths, representing 58.6% of the gene population at 16 m (Figure 3A) and 50.7% at 25 m (Figure 3B). Other genes in the pathway exhibited a varied presence, accounting for between 10.9% and 0.40% of the carotenogenic gene population. Further differential abundance analysis using TPM identified several significantly more abundant carotenogenic genes at the 16 m depth. These include genes encoding lycopene \(\beta\)-cyclase and zeaxanthin glucosyltransferase, showing more than a two-fold increase compared to the 25 m depth (Figure 3C). In contrast, genes encoding enzymes such as 15-cis-phytoene desaturase and carotenoid cleavage dioxygenase were found to be more than twice as abundant at 25 m (Figure 3D).
## 2 Calculation Theory Revision  
### Beam-Spring Method  
The beam-spring method simplifies the main section of the segment to a circular arc beam or a linear beam frame, regarding the segment joint as a rotating spring and the ring joint as a structural model of a shear spring, and using the finite element method for elastic analysis. [PERSON] and [PERSON] added the effect of staggered joints to the elastic hinge ring model and used springs to evaluate the shear resistance between the rings to explain the rotation and shearing resistance of the segment joint. They proposed an analytical method (also known as the \(M\)-\(K\) method) for the shear effect of tube segment longitudinal joints [1]. This model also considers the stiffness of the segment joints, the joint position, and the effect of the staggered joint assembly, which is a reasonable calculation model, as shown in Figure 1.  
This calculation model includes a multi-hinged ring calculation method and a uniform stiffness ring algorithm. At the same time, the shear stiffness of the segment ring joint can be used to characterize the splicing effect of the staggered joint, which is an effective method of explaining the load-bearing mechanism of the pipe ring [14; 15; 16; 17; 18; 19]. During the calculation, despite that the existing related beam-spring model stiffness matrix, coordinate transformation, formation reaction research, and calculation program development all assumed that the beam-joint displacement is continuous, but this paper considers the beam-joint deformation in the beam-spring model to be discontinuous, the derivation process is as follows:  
Divide the tube of shield tunnel into \(N\) straight beam section with equal length \(l\) and central angle of \(\theta\), then:  
\[\theta=\frac{2\pi}{N},l=2 rsin(\frac{\theta}{2}) \tag{1}\]  
where \(r\) is the radius of the tunnel section.  
By considering the angle changes in different beam elements, the vertical and lateral distributed forces acting on each beam element are converted into equivalent node forces.
## 5 Results and Analysis
### Ablation Experiment  
In order to achieve precise and efficient detection of seedling planting status, this study replaced the original YOLOv8n model with ASPP, added the SimAM attention module, and introduced AFPN. Through ablation comparative experiments, the changes and improvements of various parameters before and after the improvement can be more intuitively analyzed. The experimental results are shown in Table 1.  
As shown in the table, the introduction of the AFPN pyramid structure effectively improves the efficiency of network feature extraction, with an increase in accuracy by 2%. Meanwhile, there is a significant decrease in model weight size and computational complexity, maintaining the lightweight principle of the model. When using the ASPP module as the new model with the AFPN structure, there is a slight increase in recall rate and mAP. If the SimAM attention module is directly added to the end of the backbone network, there is an improvement in accuracy and recall rate, but the FPS decreases to 92. Ultimately, the improved YOLOv8n model simultaneously introduces three modules,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Precision/\%**} & \multirow{2}{*}{**Recall/\%**} & \multirow{2}{*}{**mAP/\%**} & \multirow{2}{*}{**FPS**} & **Model** \\  & & & & & **Size/MB** \\ \hline YOLOv8n & 91.9 & 91.8 & 93.5 & 120 & 6.3 \\ \hline \�  
resulting in improvements in accuracy, recall rate, and mAP by 3.6%, 0.9%, and 1.7%, respectively. However, compared to the original model, there is a slight decrease in inference speed, an increase in weight size, and computational complexity. This indicates that the introduction of each structure increases the complexity of the model but enhances its information extraction capability and feature fusion properties.  
The four optimization methods in the table above all show improvements in parameter metrics compared to the original model. The accuracy, recall rate, and mAP curves of the five models are depicted in Figure 14. Through ablation experiments, it is concluded that optimizing both the backbone network and the neck network of the YOLOv8n model simultaneously, while slightly decreasing detection speed, does not compromise the real-time detection requirements for seedlings. Additionally, it enhances the model's ability to recognize the planting status of seedlings.
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 6 Discussion
Possible attacks include for example, interrupting the communication, entering false information or removing information about current traffic-threatening situations. In addition to communication, it is also necessary to take into account of the cybersecurity of IoT devices that are implemented in vehicles.