esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Investigation of the In Vitro Cytotoxicity of HBE and HB-AuNPs  
#### 2.5.1 Effects of HB-AuNPs on Cell Viability: WST-1 Assay  
The effect of HB-AuNPs was evaluated on human cancer cell lines (Caco-2, PC-3, MCF-7) and non-cancer cell lines (KMST-6, HEK-293) using the WST-1 assay as described elsewhere [32]. The cells were purchased from the American Type Culture Collection (Manassas, VA, USA). Briefly, the PC-3 cells were cultured in RPMI-1640, whereas the KMST-6, HEK-293, Caco-2, and MCF-7 cells were cultured in DMEM. The cells were seeded at a density of 1 \(\times\) 10\({}^{5}\) cells/mL in a 96-well microplate in their respective media containing 10% FBS and 1% penicillin/streptomycin. The cells were treated for 24 h with increasing concentrations of HB-AuNPs (0 to 100 \(\upmu\)g/mL) and HBE (0 to 8 mg/mL). The effect of the treatments on cell viability was evaluated using the WST-1 assay as previously described [32]. The absorbance of the samples was measured at 440 nm (with a reference wavelength of 600 nm) using a POLARstar Omega microplate reader. The percentage cell viability was calculated as follows:  
\[\%Cell\ viability=\frac{Absorbance\ of\ test\ samples}{Absorbance\ of\ untreated\ samples}\times 100\%\]
###### Abstract  
This study presents a hybrid modeling framework synergizing process-based crop modeling with evolutionary optimization to reconcile yield sustainability with nitrogen management in arid cotton systems. Building upon the DSSAT-CROPGRO model's demonstrated superiority over pure machine learning approaches in simulating nitrogen-crop interactions (calibrated with multi-year phenological datasets), we develop a genetic algorithm-embedded decision system that simultaneously optimizes nitrogen use efficiency (NUE) and economic returns. Field validations across contrasting growing seasons demonstrate the framework's capacity to reduce nitrogen inputs by 15-20% while increasing profitability by 8-12% compared to conventional practices, without compromising yield stability. The tight coupling of mechanistic understanding with multi-objective optimization advances precision agriculture through two key innovations: (1) dynamic adaptation of fertilization strategies to both biophysical processes and economic constraints and (2) closed-loop integration of crop physiology simulations with evolutionary computation. This paradigm-shifting methodology establishes a new template for developing environmentally intelligent decision-support systems in water-limited agroecosystems.  
Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang 212000, China; [EMAIL_ADDRESS] [EMAIL_ADDRESS] nitrogen fertilizer; cotton production; DSSAT-CROPGRO; genetic algorithm
# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 5 Results and Discussion
### Model Performance  
The training and validation accuracies are presented in Table 6. Using optimized hyperparameters, the ANN can learn the training samples and predict the occurrence of a traffic breakdown with 100% accuracy on both the training and the validation datasets. For non-breakdown cases, the prediction accuracy is 94% on training data and 96% in validation data. The overall accuracies in the training and testing data sets are higher than 97% and 96%, respectively. The high accuracy of the validation results indicates that despite the distinct traffic patterns from different months for the training and validation sets, the neural network is capable of learning and predicting the occurrences of traffic breakdown utilizing temporal and spatial variations in the traffic data. Moreover, this result signifies that the neural network model does not experience an overfitting issue.  
Table 7 presents the predicted results of the model for the testing location. By employing the same variables and model, both breakdown and non-breakdown events exhibit a high level of predictive accuracy. In particular, the accuracy of predicting breakdown and non-breakdown events is 87.5% and 97.9%, respectively. The high accuracy indicates that ANN can effectively predict the occurrence of traffic breakdowns by taking advantage of temporally and spatially distributed traffic data.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Breakdown** & **Non-Breakdown** & **Overall** \\ \hline Training Accuracy & 100\% & 94.39\% & 97.17\% \\ \hline Validation Accuracy & 100\% & 96.40\% & 96.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Training and validation accuracy.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline True Label & Prediction Label & \multirow{2}{*}{Breakdown} & Non-Breakdown & Accuracy \\ \hline Breakdown & 14 & 2 & 87.5\% \\ \hline Non-breakdown & 24 & 1118 & 97.90\% \\ \hline Overall & & & 97.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Testing accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Neuron Count** & **Batch Size** & **Horizon Timestamp** \\ \hline
20 & 16 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Selected Hyperparameters.
# Optical Fiber Current Sensors Based on FBG and Magnetotstrictive Composite Materials
## 2 Working Principle of the Sensor
### Optoelectronic Test System  
Figure 6 shows the experimental setup for evaluating the performance of the sensor. An AC-band amplified spontaneous emission (ASE) source or a tunable laser (Agilent 81640B), combined with a 3 dB coupler, was used to illuminate the MC-FBG sensor via ports 1 and 2 of an optical circulator. The reflected wavelengths via ports 2 and 3 of the optical circulator were split by a 3 dB coupler and then measured by a 125 MHz photodetector (NewFocus 1811) or an optical spectrum analyzer (OSA) (Agilent 86140A) with a 0.1 nm resolution. The electromagnet provides a quasi-static magnetic field with a maximum amplitude of 146 kA/m and a frequency of 1 Hz. The function of the spectrum analyzer is to demodulate and display signals [12].
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 3 Results
### Real-Life Cases  
In order to validate the performance of the Aquasafe web-based platform and test the applicability of the developed fish growth models to real-life scenarios, we showcased the platform's performance on three different aquaculture sites. The sites include a pilot aquaculture farm operated by HCMR in Souda Bay, Crete, South Aegegan Sea, a commercial fish farm operated by Plagton S.A. in Astakos, Aetolia-Acramania, Ionian Sea, and a commercial aquaculture farm in Agrilia, Lesvos Island, North Aegegan Sea (Figure 10). Specifically, selected model outputs for the 2022 growing season (1 April-1 October) are shown comparatively for the three sites while field data from the HCMR farm are also provided as a form of model validation. Additionally, to show typical growing scenarios, cages with excessive starting populations (overstocked) were created to demonstrate the capabilities of the tool in generating relevant alerts for oxygen availability. The experiments were conducted for the years 2022-2023, and the results are presented in Sections 3.3.1 and 3.3.2.  
Figure 8: The majority of the users were able to finish the tasks in less than 20 min. More specifically, only 18% of the users needed more than 20 min to complete the tasks, while 55% finished in 10-20 min and 27% in less than 10 min.  
Figure 9: According to the satisfactory survey, the majority of the users found the instructions clear and easy to follow and the interaction with the platform quite or completely satisfying.
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 4 Discussion
### Comparison with Existing Approaches  
Terrain classification has been extensively studied in the domain of humanoid robots and autonomous mobile vehicles; however, we have found few studies that focus on terrain classification using inertial data of human gait. In the experimental setup of [PERSON] et al. [33], an IMU was mounted on L5 vertebra of the subjects and the gait data of 35 subjects were recorded on two different types of surfaces: flat surfaces and uneven bricks. A deep learning model with long short-term memory units was used for training and prediction. They achieved a surface classification accuracy of 96.3%. [PERSON] et al. [11] used visual features captured from body mounted cameras during human locomotion. They considered three different classes of terrains, i.e., hard surfaces, soft surfaces, and unwalkable surfaces. They reported a classification accuracy of 82%. [PERSON] et al. [34] proposed a terrain identification and surface inclination estimation system for a prosthetic leg using visual and inertial sensors. They recorded data on six different surfaces and achieved an average classification accuracy of 86%. [PERSON] et al. [48] performed acoustic-based terrain classification for robots that used  
Figure 9: Bar chart showing the comparison of each classifier at each sensor location on different classification tasks.  
sound from the interaction of vehicle and terrain. They reported a classification rate of 92%. They performed sliding window operation for feature extraction, which is not efficient with respect to time, as compared to the proposed method. In comparison to these approaches, the proposed approach was tested on six different terrains, namely, carpet, concrete floor, grass, asphalt, soil, and tile, and the classification accuracies outperformed others in all cases, as shown in Table 8.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 2 Related Work  
Figure 1 shows a typical framework of singing voice separation using a DNN [4; 5; 6]. First, the input mixture in the time-domain is transformed to magnitude and phase spectra using short-time Fourier transform (STFT). DNNs take the magnitude spectra of the mixed sound as input to obtain the spectral magnitudes of both vocal and instrument sounds. The inverse STFT is performed by combining the magnitude spectra predicted by the DNN model and the phase spectra of the input mixture to get separated vocals and musical instruments. Some types of DNNs for single-channel singing voice separation are convolutional neural networks (CNNs) [1; 2; 3], recurrent neural networks (RNNs) [4; 5; 6], convolutional recurrent neural networks (CRNNs) [17; 18], and U-Net [7; 8; 9; 10].
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 3 Traffic Status Prediction
### Long-Term Traffic Data Prediction  
Regardless of the importance of long-term traffic prediction in enhancing the city roads infrastructure, most of the literature in traffic modeling is focused on short-term prediction. The time intervals of the long-term traffic prediction study the time window that ranges from several months to several years. Although studies claimed that long-term traffic prediction is not beneficial to obtain an accurate prediction, other studies that apply traffic modeling for long-term prediction highlighted the importance of long-term prediction to improve traffic management systems [16]. Because of the large time window in the long-term prediction, seasonal patterns and cycle patterns will be detected in the traffic data, therefore, models that are able to identify these patterns are strongly recommended such as the SARIMA model, and seasonal autoregressive fractionally integrated moving average (SARFIMA) model [17]. Another study indicates that exponential smoothing models are powerful in capturing seasonality in the traffic data as well as handling trends and white noise satisfactorily [18].
# Prediction of Ground Deformation during Pipe-Jacking Considering Multiple Factors
## 4 Application to Case Study
### Validation
As the field case shows that the ground movement is more irregular in the position close to the tunnel, the prediction may result in a worse result in shallower pipe construction. More case studies are expected to further compare the difference between the proposed method and other existing solutions. In addition, the disturbance caused by adjacent construction needs to be considered for the further improvement of proposed approach.
## 3 Results
### TQ-6 Enhances Nrf2/HO-1 mRNA and Protein Expression  
Nrf2 inhibits proinflammatory cytokine expression, inflammatory responses, and ROS production [20]; HO-1 is a major antioxidant enzyme mediated by Nrf2 activation [21; 22; 23]. To determine whether TQ-6 activates Nrf2 signaling, RAW264.7 cells were treated with 20 \(\upmu\)M TQ-6 for 20 min and then LPS (1 \(\upmu\)g/mL) was added. The Nrf2 and HO-1 mRNA expression was measured using RT-qPCR (Figure 4A,B) and their protein expression was measured by Western blotting (Figure 4C,D). Nrf2 and HO-1 TQ-6 mRNA and protein expressions were upregulated in LPS-stimulated RAW264.7 cells (Figure 4A-D). However, cells treated with TQ-6 alone did not change the expression of Nrf2 and HO-1, as they almost similarly expressed to normal cells (Figure 4E,F). This indicates that TQ-6 holds protective effects without inducing cytolysis.  
Figure 3: TQ-6 inhibits OH\({}^{\bullet}\) formation in LPS-stimulated RAW264.7 cells. (**A**) Cells were treated with (**a**) 0.1% DMSO or (**b**) DMSO or TQ-6 at (**c**) 10 \(\upmu\)M or (**d**) 20 \(\upmu\)M and then LPS (1 \(\upmu\)g/mL) added to trigger OH\({}^{\bullet}\) formation. An asterisk (*) indicates the formation of OH\({}^{\bullet}\). (**B**) Signal intensity was expressed as mean \(\pm\) SEM (\(n\) = 4), which analyzed by using WIN-EPR, version 921201. *** \(p\) < 0.001, compared with the control group; *** \(p\) < 0.001, compared with the LPS group. Spectra are representative examples of four similar experiments. ctl: control (0.1% DMSO). The horizontal axis means the magnetic field [G].
# Specific Modeling Issues on an Adaptive Winglet Skeleton
###### Abstract  
Morphing aeronautical systems may be used for a number of aims, ranging from improving performance in specific flight conditions, to keeping the optimal efficiency over a certain parameters domain instead of confining it to a single point, extending the flight envelope, and so on. An almost trivial statement is that traditional skeleton architectures cannot be held as a structure modified from being rigid to deformable. That passage is not simple, as a structure that is able to be modified shall be designed and constructed to face those new requirements. What is not marginal, is that the new configurations can lead to some peculiar problems for both the morphing and the standard, supporting, elements. In their own nature, in fact, adaptive systems are designed to contain all the parts within the original geometry, without any \"external adjoint\", such as nacelles or others. Stress and strain distribution may vary a lot with respect to usual structures and some particular modifications are required. Sometimes, it happens that the structural behavior does not match with the common experience and some specific adjustment shall be done to overcome the problem. What is reported in this paper is a study concerning the adaptation of the structural architecture, used to host a winglet morphing system, to make it accomplish the original requirements, i.e., allow the deformation values to be under the safety threshold. When facing that problem, an uncommon behavior of the finite element (FE) solver has been met: the safety factors appear to be tremendously dependent on the mesh size, so as to raise serious questions about the actual expected value, relevant for the most severe load conditions. On the other side, such singularities are more and more confined into single points (or single lines), as the mesh refines, so to evidence somehow the numerical effect behind those results. On the other side, standard engineering local methods to reduce the abovementioned strain peaks seem to work very well in re-distributing the stress and strain excesses to the whole system domain. The work does not intend to give an answer to the presented problem, being instead focused on describing its possible causes and its evident effects. Further work is necessary to detect the original source of such inconsistencies, and propose and test operative solutions. That will be the subject of the next steps of the ongoing research.  
adaptive structures; FE modeling; stress analysis; loads assessment +
Footnote †: journal: Applied Sciences
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 4 Crypto-Engine Implementation in the EMERGE Project
### Implementation  
The EMERGE Crypto-Engine implementation consists of a key component generator written in Python and a compiled software library written in C++11 which provides the cryptographic primitives.  
The key generator is intended to run offline to create the key components according to the defined ECTAKS ANT. In particular, the generator allows to create any number of key component sets (one set for node) with a selectable key length. The key component generation pseudo-code can be found in [47].  
The software library is the part of the Crypto-Engine that provides the ECTAKS-Encrypt, ECTAKS-Decrypt, ECTAKS-Sign, and ECTAKS-Verify functions. It has two main dependencies: _OpenSSL_[66], and _libGMP_[67]. OpenSSL library has been adopted only for the AES implementation (used by ECTAKS-ECIES) to decouple the symmetric encryption part from ECTAKS and to provide state-of-the-art performance. The same motivation holds true for LibGMP, which is instead used for the low-level modular arithmetics on large multi-word integers. Both dependencies are not strictly coupled, so they can be easily replaced by custom solutions if necessary.
## 2 Materials and Methods
### Collection of Sample Spectra  
The ASD FieldSpec4 portable ground spectrometer produced by Analytical Spectral Device (ASD, USA) was applied to measure the level of soil spectrum reflectance. Figure 3 illustrates the process of how the portable ground spectral data acquisition system works. The spectrum was in the range of 350-2500 nm, and the number of output bands was 2151. A fiber-optic probe was used, whose angular field of view is 25\({}^{\circ}\), and two 35 W halogen lamps were symmetrically fixated on both sides of the soil sample placement table as the light source. The incident angle (\(\alpha\)) was set to 45\({}^{\circ}\), and the distance (\(L\)) from the light source to the soil sample surface was set to 15 cm. Placed in a transparent glass Petri dish, the soil samples had a diameter of 9 cm and a depth of 1.5 cm. Then, the soil sample was scraped flat with the lower straight edge of the protractor, and the spectral measurement was performed in a darkroom. Before each test, the instrument was corrected in black and white. The spectrum acquisition Rs3. Software \(\copyright\) was applied to collect each measurement data automatically for the one-off acquisition of spectrum information, and the operation was conducted on each soil sample in triplicate.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Calibration Set**} & \multicolumn{4}{c}{**Prediction Set**} \\ \cline{2-9}  & **Max** & **Min** & **Mean** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Max** & **Min** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Total nitrogen (g\(\cdot\)kg\({}^{-1}\)) & 0.63 & 0.42 & 0.53 & 0.06 & 0.50 & 0.37 & 0.43 & 0.03 \\ Available phosphorus (mg\(\cdot\)kg\({}^{-1}\)) & 50.45 & 38.08 & 45.03 & 2.86 & 45.45 & 41.32 & 43.17 & 1.17 \\ Rapidly available potassium (mg\(\cdot\)kg\({}^{-1}\)) & 133.38 & 77.06 & 101.99 & 12.92 & 121.18 & 91.91 & 114.89 & 7.19 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical results of the determination of soil nutrient content values in calibration set and prediction set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Max** & **Min** & **Mean** & **Standard Deviation** & **D-Value** \\ \hline
1 h & 36.40 & 28.26 & 31.66 & 2.63 & 8.14 \\
2 h & 30.53 & 22.44 & 25.83 & 2.32 & 8.09 \\
3 h & 25.52 & 19.60 & 22.83 & 1.60 & 5.92 \\
4 h & 21.87 & 15.78 & 18.95 & 1.65 & 6.09 \\
5 h & 18.87 & 12.62 & 16.24 & 1.95 & 6.25 \\
6 h & 15.07 & 7.37 & 12.41 & 2.32 & 7.70 \\ \hline \end{tabular}
\end{table}
Table 2: Measured values of soil moisture content.
## 4 Discussion
### Distribution and Dynamics of GPP and WUE
In contrast, the regions with a high distribution of cropland in the humid zone (e.g., the Sichuan basin), GPP and ET showed an increasing trend while WUE mainly exhibited a decreasing trend, indicating that the increasing trend of GPP since 2001 was less than that of ET. A similar pattern was found in previous studies [23; 53], which may be related to irrational agricultural water use in these regions. Specifically for the five main vegetation types in China, all vegetation types exhibited an increasing trend in GPP, while wetland and shrubland WUE showed a decreasing trend. Studies have shown that a warmer climate and a longer growing season can stimulate photosynthesis and plant growth to increase vegetation GPP, yet this also increases plant transpiration and thus reduces WUE [54]. An analysis of GPP and WUE coefficients of variation since 2001 revealed that both showed large \(CV\) values in the southwestern part of the semi-humid zone (Figure 3). Since wetland was more distributed in this region (Figure 1) and its \(CV\) of GPP and WUE was prominent among all vegetation types (Tables 1 and 2), it can be inferred that the variation of wetland was the main reason for the significant fluctuation of GPP and WUE in this region. Most studies consider forests and agricultural land as the main vegetation types affecting the variations in GPP and WUE across China [7]. This study investigated the GPP and WUE dynamics of multiple vegetation types in arid-humid zones across China and found that wetland and shrubland also have their unique characteristics in specific arid-humid zones, which can be used to further decipher the mechanisms of China's GPP and WUE changes.
# Victim Detection and Localization in Emergencies
###### Abstract  
Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios.  
detection; localization; UWB; WiFi FTM; victims +
Footnote †: journal: Physics Letters B