esa-sceva/satcom-chunk-collection huggingface dataset:
### Relevant Literature
For more information about optimal sizing of hybrid systems, significantly detailed literature surveys on different methodologies, solution techniques, modeling schemes, and discussions are given in [26; 27; 28; 29; 30; 31; 32]. The vast majority of studies have also been carried out to investigate the harmonic contents of household appliances and their impacts on system operation. Among them, [PERSON] et al. carried out an experimental analysis for measuring the parameters of THD, displacement power factor (DPF), and transient current of several pieces of equipment by using a multi-function meter in India [33]. [PERSON] and [PERSON] grouped the domestic appliances as lighting, power electronics, resistive, motor, and aggregated loads for establishing a complete bottom-up model to assess voltage stability, harmonics, voltage sag, swells, and unbalance conditions [34]. Also, the load current characteristics were studied in [34]. Harmonic analysis was conducted by [PERSON] et al. in [35] in which the parameters were measured for a desktop computer, fluorescent luminaries, and compact fluorescent lamps and voltage harmonic distortion impacts on current harmonics were also investigated. [PERSON] et al. monitored the time-varying harmonic behavior of non-linear residential loads such as a printer, photocopier, cell phone battery charger, and compact fluorescent lamp [36]. Characterization methodology was proposed in [37] for modeling the loads based on their harmonic series composition. [PERSON] et al. in [38], investigated the potential of using PV interface inverters to reduce the effect of harmonics from households. A model for house load and distributed production was created and the model was confirmed by simulations. [PERSON] et al. proposed a model to determine energy consumption with high accuracy for typical Irish dwellings in [39]. However, the effect of harmonic was not taken into consideration in this study. [PERSON] et al. proposed a model to improve the reliability of the power system during a power failure of a shared PV system in [40]. One-year data for the PV system and 10 home consumers for loads were considered. It has been seen that the common use of the PV system makes more profit than the individual use. These aforementioned references with many other studies not referred here have provided valuable contributions to the literature on the optimal sizing strategies of hybrid systems from different points of view. Various methods, solution techniques, and modeling frameworks have been presented for achieving different objectives. Nevertheless, it is important to emphasize that most of the reported techniques for optimal sizing of hybrid systems assume sinusoidal operating conditions which do not take the voltage and current harmonics of the demand side into consideration.
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
## 4 The New Method of Treating Saline-Alkaline Soil Mainly with Wood Vinegar
### Wood Vinegar  
An effective example involves the application of wood vinegar to treat salt-affected and high-pH soils. [PERSON] et al. found that wood vinegar effectively reduced ammonia volatilization by regulating soil pH and urease activity, while maintaining a high rice yield. [PERSON] et al. [103] also discovered that wood vinegar effectively reduced soil alkalinity by neutralizing acid and alkali, and boosted soil fertility by increasing nutrient levels (C, N, P), ultimately altering soil microbial abundance and diversity [103]. Additionally, [PERSON] et al. demonstrated that wood vinegar significantly boosted soil enzyme activity, lowered soil acidity and conductivity levels, while consequently enhanced soil fertility and improving the plant growth environment [104].
###### Abstract  
This study investigates the behavior and performance of a newly proposed cone-top pile foundation designed to improve stability in layered, deformable, or strain-sensitive soils. Traditional shallow and uniform conical foundations often suffer from excessive settlement and reduced capacity when subjected to vertical loads and horizontal soil deformations. To address these limitations, a hybrid foundation was developed that integrates an inverted conical base with a central pile shaft and a rolling joint interface between the foundation and the superstructure. Laboratory model tests, full-scale field loading experiments, and axisymmetric numerical simulations using Plaxis 2D (Version 8.2) were conducted to evaluate the foundation's bearing capacity, settlement behavior, and load transfer mechanisms. Results showed that the cone-top pile foundation exhibited lower settlements and higher load resistance than columnar foundations under similar loading conditions, particularly in the presence of horizontal tensile strains. The load was effectively distributed through the conical base and transferred into deeper soil layers via the pile shaft, while the rolling joint reduced stress transmission to the structure. The findings support the use of cone-top pile foundations in soft soils, seismic areas and areas affected by underground mining, where conventional designs may be inadequate. This study provides a validated and practical design alternative for challenging geotechnical environments.  
cap-top pile foundation; bearing capacity; load-settlement behavior; soil-structure interaction; hybrid foundation system; underground mining areas 2020
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
## 3 Hybrid Modulation Mode under Non-Unity Power Factor  
### H6 Grid-Tied Inverter with Modulation Mode II  
Modulation mode II (MMI), illustrated in Figure 6a is proposed to effectively suppress current distortion and achieve reactive power provision in the regions of operation where current and voltage are in opposite polarity. The operation of this modulation is illustrated in Figure 6. Take for example the case where the terminal voltage \(V_{ab}\) is in the positive half cycle, but the current is in the negative cycle. In this situation, S1 and S4 are switched as before, however now S6 is switched in a complimentary manner to S1 and S4. This allow for a new freewheel path through D2, S6, when S1 and S4 are off, thus allowing the instantaneous terminal voltage to be zero and preserving the unipolar modulation. Conversely when the terminal voltage is in the negative half cycle, but the current is in the positive, switch S5 can be used with a complimentary switching to S3 and S2 to provide the necessary freewheel path.  
This MMI could be used for the entire output voltage cycle, however doing so would incur the extra switching loss associated with the high frequency switching of S5 and S6. To avoid this MMI and MMI can be combined into a hybrid modulation mode (HMM) to solve the problem of current distortion while also minimizing the switching losses. This HMM consists of using MMI when the control voltage has the same polarity with the inductor current and using MMI in the region where the control voltage has the opposite polarity with the inductor current. This new modulation mode can be used for any range of power factor operation.  
Figure 5: Terminal voltage during the distortion of current.  
For the H6 grid-tied inverter with HMM, the common-mode voltage can be analyzed for the six switching states. This analysis shows that the common mode voltage \(V_{cm}\) can be maintained at a constant value 0.5 \(E\), no matter which mode the system is in, and hence there is no common-mode leakage current present.  
It is also worth noting that the H6 topology under MMI scheme does not need to insert a dead zone for the switches in the same phase leg. This is because the switches in the same phase leg are never all turned on during the same SPWM cycle with MMI [15]. In contrast however, under MMII, the DC voltage \(E\) will possibly be short circuited if an appropriate dead time is not inserted between the switches S1, S4, and S6. Although the instantaneous terminal voltage \(V_{ab}\) cannot be freewheel to zero during the dead time interval, the dead time is very short compared to the whole switch period, therefore, there is almost no impact to the output AC voltage.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 3 Results
### Flood Plain Mapping  
The flooded area for floods of different return periods was delineated using the model and Figure 8 depicts the areas of the river basin expected to be flooded for a flood with a 50-year return period. Figure 9 illustrates the extent of the flood according to the model in comparison to the extent observed on the MODIS satellite image. Through visual inspection, the hydraulic model simulates very well the spatial extent of the 2014 flood. Although the timing of the satellite image does not exactly correspond with the peak flood timing, field surveys in the study area indicate that the highest flood happened around August 7. However, some positions had minor deviations, the accuracy of the floodplain geometry used in the HEC-RAS model may cause those deviations. There were several islands and reservoirs on the water surface created by ArcGIS, possibly because the DEM elevations had a 1 m interval. The surface of water assumed constant elevation values. Consequently, compared to the lower reach, the flooded area in the upper section of the river and flow depth are comparatively smaller.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Distribution**} & \multicolumn{4}{c}{**Taunsa Barrage**} \\ \cline{2-5}  & **Test Statistic (D)** & **Fit** & **Ranking** & **Sample Size** \\ \hline LN & 0.065 & Yes & 3 & 0.103 \\ Gumbel & 0.063 & Yes & 1 & 0.126 \\ LP3 & 0.064 & Yes & 2 & 0.113 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of the K-S statistical test at Taunsa Barrage.  
Figure 6: The discharge of the Indus River at Taunsa Barrage using the log-Pearson type III, Gumbel, and log-normal probability distributions.  
Figure 8: Areas in the Muzzaffargarh District expected to be flooded for a flood with a 50-year return period.  
Figure 7: Water level for floods with different return periods 1118 m downstream from Taunsa Barrage.  
Near Tanusa Barrage lies the highest area impacted. It can be seen from Table 6 that for the 100-year return period, more than 147% of the surface area is expected to be flooded.  
Overall, this study indicates that the accuracy of the HEC-RAS model to simulate the spatial extent of the 2014 flood of the Indus River to be very good, through visual inspection, but better calibration of the channel roughness could potentially further increase the accuracy of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Return Period** & **Area Affected (km2)** & **Percentage w.r.t Normal Flow (\%)** \\ \hline
1 & Normal flow & 23.29 & 100 \\
2 & 5-year & 130.95 & 139 \\
3 & 10-year & 136.56 & 143 \\
4 & 50-year & 138.40 & 145 \\
5 & 100-year & 141.54 & 147 \\
6 & 150-year & 143.22 & 149 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Percentage of the surface area flooded during floods of different return periods.  
Figure 9: Comparison of the extent of the 2014 flood according to the MODIS satellite image and the hydraulic simulation of the flood.
# Integration of BIM Tools for the Facility Management of Railway Bridges
## 3 Literature Review
### Knowledge Gap and Research Objectives  
Despite the potential benefits of automating a BIM model with a standardized database to create digital twins for asset monitoring, there are substantial challenges in effectively implementing these methods. Traditional approaches to monitoring and maintenance of railway bridges often rely on subjective visual inspections, leading to data dispersion and the lack of a comprehensive view of the infrastructure. Additionally, these methods typically follow reactive strategies rather than preventive ones, which limits their reliability, accuracy, and efficiency. This is in stark contrast to the advantages provided by BIM/data automation and digital twins, which offer enhanced interoperability, collaboration, reduction of information loss, improved data accessibility, support in decision-making, and the potential for continuous and preventive monitoring.  
The primary knowledge gap lies in understanding how to effectively integrate BIM and digital twin technologies to overcome the limitations of traditional methods and fully leverage their benefits in the monitoring and maintenance of railway bridges. This research addresses this gap by proposing a systematic approach to integrate a BIM tool for the facility management of railway bridges, aiming to generate digital twins.  
To achieve this, the research is guided by the following specific objectives:  
(a) Identify the critical elements necessary for monitoring a railway bridge and the suitable BIM tools for generating digital twins.  
(b) Design a comprehensive procedure for integrating BIM tools to automate data collection and management, serving as the foundation for digital twins in facility management.  
(c) Implement and evaluate the developed BIM tool in a real-world case study to assess its effectiveness in generating digital twins and improving the monitoring and maintenance processes for railway bridges.  
This study seeks to contribute to the knowledge by developing a practical framework for integrating BIM and digital twin technologies, demonstrating their potential to enhance the accuracy, efficiency, and reliability of railway bridge monitoring and maintenance.
## 3 Research Methodology and Model Development
### Data Collection and Traffic Demand Analysis  
Relevant traffic data (vehicle counting) were collected in 15 min intervals over seven days (Saturday to Friday). Figure 3 presents an example of collected vehicle data, both in total (summarized) and by a group of lanes. The collection of pedestrian and bicycle traffic was carried out at signalized intersections and pedestrian crossings. As shown in Figure 3, different movement directions are represented by various colors: traffic coming from the west is marked in green, traffic from the southeast is marked in orange, traffic from the east is marked in yellow, and traffic from the north and northwest is marked in blue. Additionally, the thickness of the arrows visually represents the load in each direction of movement.  
Based on the traffic load illustrated in Figure 3, it is evident that the primary directions of movement during both the morning and afternoon peak hours are east-west and west-east. In contrast, the northbound and southbound directions represent secondary flows. This distribution highlights the dominant role of the east-west corridor in accommodating peak-hour traffic demand, while movements toward the north and south remain significantly less pronounced. Differences in the traffic demand in the morning (Figure 2(a)) and the afternoon peak hours (Figure 2(b)) are presented, and it can be seen that the exit flow of the west approach during the afternoon peak hour is almost 70% higher than during the afternoon peak.  
Figure 3: Example of data collection by intersection (a) Vukovar Street–Nova Cesta Street.  
Figure 2: The micro situation of the signalized intersections on the selected corridor.  
moming peak hour. This information was a trigger for a scheduled DLA implementation according to the traffic demand on the entire corridor. The trend and vehicle demand variations are the same at other intersections along the selected corridor, respectively.  
The variation in traffic load ratios between morning and afternoon peak hours is observable across all three Figures 4-6. This trend highlights the dynamic nature of traffic flows and, when considering the geometry of the traffic network, underscores the potential significance of DLA implementation. Such an implementation could substantially enhance the overall functionality and quality of the traffic network.  
Although the analysis included three intersections, a fourth signalized vehicle crossing was not graphically represented, as its traffic load primarily reflects flows from the west and east directions and does not introduce notable variation.  
In addition to enhancing network efficiency and operational performance, the implementation of DLA can also play a significant role in improving traffic safety, which is discussed in more detail in the following chapter.  
Figure 4: Vehicle count at the intersection (a) Vukovar Street–TrakoScanska Street–Ozaljska Street on the heaviest load day.  
Figure 5: Vehicle count at the intersection (b) Vukovar Street–Nova Cesta Street on the heaviest load day.
## 2 Materials and Testing Procedure  
### Materials and Mixture Proportion  
The raw materials used in the production of the SHCC were ordinary Portland cement (Type I) from Hanil Cement Co. Ltd. in South Korea. The micro silica sand with a mean grain size of 120 \(\upmu\)m was added in the cement matrix at a sand/cement (S/C) ration of 0.4 by mass. Polycarboxylate superplasticizer (SP) was incorporated into cement matrix to achieve an average flow value of about 300 mm in the fresh cement mortar. Polyethylene (PE) fibers were used at 0.75 vol.% to improve the tensile strength and strain capacity of cement composite. The PE fiber is 15 mm length and 12 \(\upmu\)m diameter, and tensile strength of 2.5 GPa. Steel fibers were added into PE-SHCC mixtures at both dosages (0.5 vol.% or 1.0 vol.%) to evaluate the effect of steel fibers on tensile and electrical properties of PE-SHCC. The steel fiber is 13 mm length, 200 \(\upmu\)m diameter and tensile strength of 2.65 GPa. A commercially available liquid-type MWCNTs were incorporated as functional fillers to give the conductivity of PE-SHCC and improve the self-sensing capacity. The MWCNTs from Chengdu Organic Chemicals Co. Ltd. in Chengdu, China have inside diameter of 5-15 nm, outside diameters of higher than 50 nm and lengths of 10-20 \(\upmu\)m.  
The detailed constituents and mass proportions of SHCC mixtures are presented in Table 1. Three mixes were prepared to investigate the steel fiber effect on the tensile and electrical properties of PE-SHCCs. Each mix is designated to identify differenced in steel fiber content in the PE-SHCC mixtures. For example, PE-SHCC-SF05 indicates PE-SHCC mixture with a 0.5 vol.% steel fibers.
# Identification of Deformation Stage and Crack Initiation in TC11 Alloys Using Acoustic Emission
## 2 AE Signal Energy Ratio  
In our testing system, the recorded AE signal contains information from AE sources, propagation media, coupling interfaces, and acquisition systems [30]. Coupling interfaces denote the bonding layer between the TC11 titanium alloy and sensors. Each characteristic system response can be dealt with the transfer function, that is, \(H_{s}\) for sources, \(H_{m}\) for propagation media, \(H_{c}\) for coupling interfaces, and \(H_{l}\) for acquisition systems. Thus, the system output of the AE signal can be given by the product of the four transfer functions in the frequency domain [30; 31; 32]:  
\[H_{AE}=H_{s}\cdot H_{m}\cdot H_{c}\cdot H_{t} \tag{1}\]  
where \(H_{AE}\) is a combination of all influencing factors.  
The effects of the propagation medium, coupling interface, and acquisition system on \(H_{AE}\) are constant within the same set of experimental data. In the tensile test of the TC11 titanium alloy specimen, the AE source mainly includes three aspects, namely the TC11 deformation, friction between the specimen and fixture, and the hydraulic system of the testing machine. The AE signals generated by these three AE sources are based on a fixed pattern, so they show fixed frequency characteristics. From another perspective, for the friction signal and the signal generated by the hydraulic system, the energy of the AE does not affect the deformation of TC11, and it remains basically constant. In contrast, the energy of the AE signal related to deformation is constantly changing with the increase of metal deforming. In the domain of frequency, the AE signal measured in TC11 testing can be decomposed into an AE signal \(H_{UAE}\), generated by deformation, and a signal \(H_{NAE}\), generated by the friction and hydraulic system. These signals also include the effects from the propagation medium, coupling interface, and acquisition system, so Equation (1) is also applicable:  
\[\begin{array}{l}H_{UAE}=H_{us}\cdot H_{m}\cdot H_{c}\cdot H_{t}\\ H_{NAE}=H_{ns}\cdot H_{m}\cdot H_{c}\cdot H_{t}\end{array} \tag{2}\]  
where \(H_{us}\) represents the AE source from deformation, and \(H_{ns}\) represents the AE from friction and hydraulic systems.  
The ratio of the total energy of \(H_{UAE}\) and the total energy of \(H_{NAE}\), \(r\), as shown in Equation (3), is employed to eliminate the influence of the propagation medium, coupling interface, and acquisition system:  
\[r=\frac{E_{UAE}}{E_{NAE}}=\frac{E_{us}\cdot E_{m}\cdot E_{c}\cdot E_{t}}{E_{ ns}\cdot E_{m}\cdot E_{c}\cdot E_{t}}=\frac{E_{us}}{E_{ns}} \tag{3}\]  
where \(E_{x}\) represents the energy of AE signals from different sources.  
We found through repeated experiments that in the actual monitoring, the ratio \(r\) has a wide distribution range, which is not conducive to intuitive judgment on TC11 deforming. Instead, the logarithm of \(r\) is used as a new AE characteristic parameter to evaluate the AE signals collected during the tensile damage of the specimen.  
\[R=\log r \tag{4}\]
## 3 System Architecture  
The system architecture [Figure 2] is built on a multi-tier web application model. It consists of distinct components for data collection, backend processing, and frontend presentation, each communicating through well-defined interfaces. The architecture was designed to be modular, enabling independent upgrades to the data engine, the AI models, or the UI without affecting other components, which is a significant improvement over the tightly coupled original Python 3.11 prototype [6].  
Figure 2 illustrates the comprehensive system architecture through a detailed block diagram that visualizes all components and their interactions. The diagram follows the C4 model notation for software architecture documentation [4], presenting the system at the container level.  
The architecture implements the microservices pattern with each component operating independently and communicating through standardized REST APIs. Data flows unidirectionally from sources through the processing pipeline to storage, with the back  
Figure 2: System diagram.  
end orchestrating requests between components. The RL engine operates as a separate Python microservice communicating with the Node.js backend through HTTP, following the pattern described by [PERSON] in \"Microservices Patterns\" [5]. This separation enables independent scaling of computationally intensive ML operations from standard web serving tasks [37].