esa-sceva/satcom-chunk-collection huggingface dataset:
# Field and Numerical Investigation of Taihu Resort Cut Slope Failure in Suzhou, China
## 1 Introduction
Based on the field investigation analysis, landslide activation was highly correlated with the reduction in safety factors caused by rainfall. [PERSON] et al. (2013) [49] and [PERSON] et al. (2023) [50] found that excavation followed by rainfall was a direct cause of collapses; therefore, excavation was the main exciting reason for landslides and a major trigger factor for the instability of slope problems. A major cause of slope instability is rainfall infiltration quantity and duration, which can lead to a series of influences such as soil strength reductions, matric suction, and an increase in pore pressure on soil surfaces. Moreover, the rainfall amount, duration, intensity, and terrain slope have serious impacts on slope stability [24], making the slope more prone to failure; therefore, many scholars have carried out research on this problem, focusing on the stability of slope under different rainfall patterns [51; 52; 53] and different rainfall durations [54; 55; 56; 57]. The scholars found a large proportion of landslides are caused by high pore-water pressure occurring under heavy rainfall or lengthy periods of moderately intense rainfall. [PERSON], [PERSON] et al. (2022) and [PERSON], [PERSON], [PERSON] et al. (2023) [58; 59] found that an increase in rainfall intensity is a significant impact factor on the development of soil slope damage, which reduces the matric suction, while the increase in water saturation in the slope has a significant effect on the factor of safety (FoS). [PERSON] et al. (2020) [60] studied slope instability induced during the construction of the expressway in Guangdong Province under excavation and rainfall conditions. Their results revealed the main cause was the continued rainfall infiltration, which significantly increased the pore pressure of the slope and triggered the collapse. These studies have provided varying advice and guidance for the ideal excavation of slopes to a certain extent, including some investigations of slope stability and rainfall-induced failure. However, less attention has been paid to systematically studying the slope stability of gradually excavating slopes based on different parameters and working conditions. In this paper, a case study investigation of the Taihu cut slope along the Taihu Lake in Suzhou, Jiangsu Province, China, is taken as the engineering object (Figure 1). Therefore, a comprehensive field investigation of the slope has been carried out first. Following that, two-dimensional numerical modeling was developed based on the finite element method using MIDAS GTS V1.1 software to estimate slope stability influenced by excavation and rainfall scenarios using strength reduction FEM. According to the field investigation and numerical analysis of the slope failure mechanism, this study provides the strength reduction mechanism of slopes, thereby contributing to the literature on the effect of excavating slopes and soil-water interaction on slope stability.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Vulnerability Parameters (Inputs to the Proposed Model)
#### 3.3.5 Heavy Overhang Effect  
Identifying the difference between ground and above-ground floors is necessary. Multistory reinforced concrete buildings with heavy balconies and overhanging floors shift their mass center upward, causing higher seismic lateral forces and overturning moments during a seismic event. Buildings with large, overhanging cantilever spans enclosed by concrete parapets are more likely to suffer heavy damage [53]. Furthermore, this effect is more noticeable in buildings with four, five, or six stories with heavy overhangs [54].
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 2 Literature Survey
### Various Approaches for Predictive Maintenance
#### 2.1.3 Artificial Intelligence (AI)-Based Approaches  
This category includes the techniques of AI using machine learning or deep learning for predictive maintenance. An artificial neural network (ANN) is a representative AI model utilized for classification and forecasting, and [PERSON] et al. (2016) predicted the expiration of fuel cells by establishing an ANN model. [PERSON] et al. (2017) considered a self-organizing map (SOM) as one of variants of ANNs to detect faults of rotating components. Additionally, neural network models with advanced structures used for deep learning have been used. For instance, a deep neural network (DNN) contains many hidden layers in order to trace complex relationships between input and output ([PERSON] and others, 2018) and a convolutional neural network (CNN) uses convolutional filters to learn latent features effectively ([PERSON] and others, 2018). Furthermore, a recurrent neural network (RNN) ([PERSON] and others, 2018) and long short-term memory (LSTM) model ([PERSON] et al., 2017) are specialized for time series data due to their unique sequential structure. In addition to ANNs, there have been studies utilizing the modification of a support vector machine (SVM) originally designed for classification. [PERSON] and [PERSON] (2018) calculated the failure probability of machinery components and determined failure or healthy status by using the SVM. Support vector regression (SVR) based on the result of the SVM was also considered for predictive maintenance by [PERSON] et al. (2018). [PERSON] et al. (2019) proposed a predictive maintenance system using an ensemble model of deep learning methods such as LSTM and autoencoder with a Cox proportional hazard model. [PERSON] and [PERSON] (2018) suggested a dynamic framework utilizing historical data to train an LSTM model and making decisions based on the model output of on-line data. [PERSON] et al. (2018) devised LSTM autoencoder-based predictive maintenance of a cyber physical production system (CPPS) imitating a real production system. Since AI approaches can pursue latent patterns of data and be capable of dealing with complicated features, they have high potential for accurate prediction. However, the requirement for computational capability is quite high and their performance can vary according to hyperparameter settings.
# Identification and Quantification of Valuable Compounds in Red Grape Seeds
## 1 Introduction
### The Antioxidant and Antiradical Capacity of Grape Seeds  
Most of the compounds with antioxidant effect that can be found in grapes are anthocyanins, catechin, gallic acid and resveratrol [33]. Resveratrol is a polyphenol found in grapes in greater quantities than in other fruits. This compound is found in vines in roots, seeds and stems, but the largest amount is found in the skin of grapes. The content of polyphenols with antioxidant action in wines is variable. It varies depending on the grape variety, the geographical location of the vineyard, the cultivation system, the climate, the type of soil, the harvest time or the oenological methods [34].  
Catechin has a higher concentration of resveratrol in grapes. Malvidin 3,5-diglucoside is an anthocyanin compound found in grapes, which has a higher antioxidant activity than alpha-tocopherol. In red wine, anthocyanin compounds increase their antioxidant effect. The antioxidant effects of red wine are 50 times higher than that of white wine, so regular consumption of red wine reduces the risk of cardiovascular disease [34].  
The polyphenolic content of wine, responsible for the antiradical activity, consists of two classes of components, flavonoids and non-flavonoids. They depend on the grape variety, the location of the vineyards, the cultivation system, the climate, the type of soil, the cultivation of the vines, the harvest time, the production and ageing process [35].  
In a study conducted in 2016 by [PERSON] et al., five grape varieties were analyzed, and in all cases, the highest antiradical capacity was identified in seeds [36]. In 2016, [PERSON] and [PERSON] analyzed a local variety of wine grown in the Lazio region and concluded that macerated wines showed the highest antiradical capacity using the DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2\({}^{\prime}\)-Azinobis-(3-Ethylbenzthiaozolin-6-Sulfonic Acid)) methods [37]. In 2009, [PERSON] et al. carried out a study in which they wanted to determine the antiradical capacity of Italian red wines. All types of wine analyzed showed high antiradical activity, always greater than 50% and a high antioxidant capacity, over 80%. Following the results obtained, it was concluded that the antiradical properties of the wine depend on the total polyphenolic content, especially on anthocyanins [35].  
This research aims to highlight the importance of existing compounds in grape seeds. Most of the seeds left after processing the grapes are considered waste and remain unused. To carry out this study we analyzed seven varieties of grapes grown exclusively in Romania. We aim to highlight natural alternatives obtained from plants for use in the food and pharmaceutical industry.
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Universal Filtered Multi-Carrier (UFMC)  
UFMC is a type of sub-band filtering based on multi-carrier waveforms, combining the simplicity of OFDM with the advantages of FBMC. However, these advantages involve an increase in the complexity at the transmitter caused by the implementation of a filter and by applying a fast Fourier transform (FFT) for each sub-band, whereas at the receiver it requires doubling the size of the FFT. As a result, the total band composed of \(N\) subcarriers is divided into \(K\) sub-bands. Therefore, the UFMC transmitter performs the related \(K\) inverse FFTs (IFFTs) of size \(N\) separately, one per sub-band, by introducing zeros in the subcarriers not belonging to the sub-band. Each resulting signal is then filtered according to its frequency band and added to the other outputs [17].  
On the other hand, the UFMC receiver recovers the signal through a size \(2N\) FFT by adding zeros on the edge. Due to the size of the FFT, the use of CPs is avoided to correctly recover the signal, and thus a very high spectral efficiency can be achieved. However, this fact causes an additive noise increase in the receiver, thus obtaining a worse performance compared to OFDM [17].
## 2 Related Work
[29] propose convolutional neural network (CNN)-based anomaly detection system to detect obstacles in an agriculture field. Their anomaly detection framework uses features extracted by a CNN detector. [PERSON] and [PERSON] [30] propose an autoencoder-based approach to detect abnormal operations of a complex machine using operational sound. They adopt a general convolutional autoencoder architecture and use residual errors to detect anomalies. [PERSON] et el. [31] propose a novel approach for anomaly detection in streaming sensors data which takes advantage of both statistical and deep learning-based techniques. They use statistical ARIMA model and a convolutional neural network (CNN) model to forecast the next timestamp in a given time-series. The forecasted value is further passed to an anomaly detector module that marks a data point as normal or anomalous based on the distance between the predicted value and the actual value. Despite the effectiveness of those abovementioned deep learning approaches, they are either supervised or semi-supervised models which need labelled normal or abnormal data for training. The proposed hybrid framework is designed to be trained in a truly unsupervised fashion without any labels indicating normal or abnormal data. The architecture is robust enough to learn the underlying dynamics even if the training dataset contains noise and anomalies. The main distinction between other deep learning-based methods and the proposed approach is that they perform on either univariate or multivariate time series data, and none of them is actually designed for spatio-temporal multivariate datasets with both spatial and temporal contextual attributes.
# Tailoring Asymmetric Lossy Channels to Test the Robustness of Mesoscopic Quantum States of Light
## 2 Materials and Methods
### Experimental Setup and Data Preparation  
A sketch of the employed experimental setup in shown in Figure 3. The fundamental (at 1047 nm) and the third harmonic (at 349 nm) of a Nd:YLF laser (IC-500, High Q Laser, Rankweil, Austria and 2003) regeneratively amplified at 500 Hz are sent to a \(\beta-\)barium-borate crystal (BBO1, cut angle = 37\({}^{\circ}\), 8-mm long) in order to produce the fourth-harmonic (at 262 nm) field by noncollinear sum-frequency generation. The generated beam is used to pump spontaneous parametric downconversion in a second BBO (BBO2, cut angle = 46.7\({}^{\circ}\), 6-mm long) crystal.  
Two twin portions at frequency degeneracy are selected both spatially and spectrally by means of two irises (I) and two bandpass filters (BF), respectively. The filtered portions are then focused into two multimode fibers (600-\(\mu\)m-core diameter) and delivered to two hybrid photodetectors (HPD, mod. R10467U-40, Hamamatsu Photonics, Hamamatsu City, Japan and 2010). These are photon-number-resolving detectors endowed with a partial photon-number-resolving capability and a good linearity up to 100 photons. Each detector output is amplified, synchronously integrated and digitized. As extensively explained in previous papers, by applying a self-consistent method to each detector output it is possible to have access to detected photons and thus to the statistical properties of the measured states [33; 34].  
In order to introduce a variable transmittance coefficient (from 0 to 1) only in one arm of the twin-beam state, a half-wave plate (HWP) followed by a polarizing cube beam splitter (PBS) is inserted in that arm. During the measurements, the half-wave plate is rotated in steps of 2\({}^{\circ}\) and 50,000 acquisitions are saved for each angle value.  
To build a given distribution \(P(t)\) of the transmission coefficient, for each measured value \(t_{i}\) in the interval (0,1) we select a dataset of \(P(t_{i})\) elements on both arms and join all the chosen datasets preserving the correspondence of the single data in the two arms. In such a way, the statistics of light in the arm without variable transmittance remains unchanged, while that on the other arm gets modified. This determines a degradation of the nonclassical photon-number correlations between the two arms, which can be quantified by evaluating the noise reduction factor.  
Note that this procedure allows us to check, starting from the same datasets, the effect of different distributions of \(t\) by simply changing \(P(t)\) and choosing different values of \(t_{0}\) and \(\sigma_{0}\).
## 3 Results and Discussion
### Modulation Efficiency
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Integration Approach**} & \multirow{2}{*}{**Wavelength**} & \multicolumn{2}{c}{**Total Footprint**} & \multirow{2}{*}{\(V_{\pi}L_{\pi}\)} & \multirow{2}{*}{**ER/IL**} \\  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [9]} & Two different SiN\({}_{x}\) layers & \multirow{4}{*}{632 nm (visible light)} & N/A & \multirow{2}{*}{\(\sim\)0.5 \(\times\) 10\({}^{-2}\)} & \\  & for passive optical & 632 nm (dimensions of MMIs and & & \(V\)-cm & N/A \\  & components and active & (visible light) & integration structures were & _V-cm_ & \\  & phase shifter LC region & not available) & & \\ \hline \multirow{4}{*}{This work} & Same SiN\({}_{x}\) layer for & \multirow{2}{*}{1530–1565 nm components and active phase shifter LC region} & \multirow{2}{*}{20 \(\times\) 600 \(\mathrm{\SIUnitSymbolMicro m^{2}}\) (Figure 7a)} & \multirow{2}{*}{\(\sim\)0.3 \(\times\) 10\({}^{-2}\)} & \\  & passive optical & 1530–1565 nm components and active phase shifter LC region & (C-band) & (Figure 7a) & _V-cm_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the designed LC-based MZI optical modulators with recently published state-of-the-art works. Figure 6: (**a**) A cross-sectional schematic view of the investigated liquid-crystal-based phase shifter section (from Figure 2a) between the two aluminum (Al) contacts with a distance \(S\) between the Al contact and the trench. (**b**) The minimum electric field obtained in the LC-filled trench region with different reverse bias voltage values. (**c**) An electric field can be effectively and uniformly applied across the LC-filled trench region with 1 V (\(S\) = 0.5 \(\mathrm{\SIUnitSymbolMicro m}\)). (**d**) Projected modulation efficiency, \(V_{\pi}L_{\pi}\), of the modulator over the entire C-band optical wavelength range.
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 3 Rotor Position Measurement for a PMSM
### Analysis of Rotor Motion in a PMSM  
When the PMSM was working in the maximum motion range of three-DOF with the maximum tilting angle of \(37.5^{\circ}\), the output shaft tip of the rotor produced a spherical trajectory (gray color) and the midpoint LED in the LSGM also produced another spherical trajectory (red color), as shown in Figure 7. The sphere centers of the two trajectories were the same, the two tilting angles were also the same, but the radii of them were different. Therefore, the spherical trajectory generated by the midpoint LED could be used to determine the position of the rotor shaft.
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 2 Material and Methods
### MODIS fPAR Products, and Land Use Maps  
The fraction of absorbed IPAR (400-700 nm wavelength), fPAR, can be derived from MODIS satellite images, and MODIS fPAR products (MCD15A2H) were provided by the U.S. Geological Survey [38; 39]. The MODIS fPAR product had a resolution of 500 \(\times\) 500 m and images are available at an 8-day interval. In total, 16 fPAR scenes between DOY 145 and 265 for the North Central Iowa counties were obtained. The quality of the fPAR scenes was checked with the Q/C layers provided by USGS, and areas of low quality were excluded.  
The fPAR scenes were cropped to the 90% footprint area (i.e., two to four data points) of each EC station, as well as for the corn and soybean fields in each county. Corn and soybean fields were identified using the 2016 county land use maps (Figure 1) provided by the Agricultural Conservation Planning Framework (ACPF) [18]. Only fields with a land use area >55% designated for corn and soybean production were used.
## 3 Materials and Methods
### Agent-Based Simulation Using Anylogic
Figure 3 illustrates this dynamic relationship between Order/Task and Vehicle, detailing the workflow from order reception to final task execution. Orders received by the process_order block are placed into the queue, where they are organized and processed according to the First-In, First-Out (FIFO) policy, ensuring that requests are handled in the order they arrive in the system. The queue manages the orders until a vehicle is available for pickup and delivery. The wait_execution block holds the order in a standby state until a vehicle agent is assigned. Once a vehicle is ready to operate, the transition to the next simulation stage begins, and the order proceeds to the execution process. After the delivery is completed, the order is removed from the system through the sink block, freeing resources for new operations. The simulation operates through behavioral rules that determine how orders are assigned to vehicles, considering availability and transport capacity. The decision-making logic incorporated into the model ensures that orders are allocated to available and priority drivers or those closest to them, respecting volume and time constraints. If a vehicle reaches its maximum capacity or there is a time mismatch between pickup and delivery, a depot can be used for temporary storage. Additionally, the model allows vehicles to function as mobile storage units, optimizing travel distances and minimizing unnecessary storage costs. At the agent level, the operational flow starts with the wait_order state, where vehicles remain idle until a task is assigned to them. Once a new order is allocated, the task_planning phase is triggered, determining the best pickup and delivery strategy based on vehicle availability and operational constraints such as time windows and capacity limits. Subsequently, the vehicle transitions to the go_to_task phase, where it navigates to the pickup location following the predefined routing policy. Upon arrival, the execution_task phase is activated, representing the process of loading luggage and preparing it for delivery. If necessary, the flow can return to the initial state to process new tasks, ensuring the continuity of the logistics system. The combination of this sequential structure with asynchronous event management allows efficient control of the transport flow, optimizing vehicle allocation and minimizing waiting times. The implementation of the model ensures  
Figure 3: Task execution and vehicle dispatch flow in the ABS. adherence to the predefined operational rules, promoting a realistic simulation aligned with the logistical challenges of the studied scenario.