esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 3 Results and Discussion
### Analysis of the Correlation between Dielectric Properties and Physicochemical Indicators  
Pearson correlation analysis between the dielectric parameters and physicochemical indicators at different frequencies is shown in Figure 5. There is a significant correlation between the dielectric parameters (\(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\)) of the summer golden passion fruit and various physicochemical indicators. The dielectric parameters \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) are negatively correlated with _SSC_ and \(PP\), while they are positively correlated with _Acidity_. The frequency range where \(\varepsilon^{\prime}\) exhibits a higher correlation with the physicochemical indicators is concentrated between 0.05 and 5 kHz, while the frequency range where \(\varepsilon^{\prime\prime}\) shows a higher correlation with the physicochemical indicators is concentrated between 1 and 10 kHz. The correlation coefficients are all around 0.8 within this frequency range.  
Figure 4: Influence of frequency on the dielectric properties of passion fruit: (**a**) relative dielectric constant \(\varepsilon^{\prime}\) and (**b**) dielectric loss factor \(\varepsilon^{\prime\prime}\).  
Figure 5: Correlation analysis of dielectric parameters and physicochemical properties at different frequencies: (**a**) _SSC_; (**b**) _Acidity_; (**c**) \(PP\).  
From Figure 4, the dielectric parameters of fruit with different \(GDD\) show good discrimination within the frequency range of 0.05-5 kHz. Moreover, as indicated in Figure 5, there is a high correlation between the dielectric parameters and physicochemical indicators within the frequency range of 0.05-10 kHz. Based on the above analysis, it can be concluded that selecting feature frequencies within the range of 0.05-10 kHz may be more appropriate. However, overall, the absolute values of the correlation coefficients between \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) at different frequencies and physicochemical indicators are all between 0.6 and 0.85, indicating that the correlations are not very strong. This suggests that it is challenging to accurately predict the physicochemical indicators of golden passion fruit using dielectric parameters at a single frequency. Therefore, to better predict the physicochemical indicators of golden passion fruit using dielectric technology, modeling and analysis should be performed at multiple characteristic frequencies.
## 4 Descriptions of Different Topologies of WSNs
### Cluster-Based Topology  
Cluster-Based topologies have widely been used in WSNs for various types of protocols, such as data gathering [31], target tracking [38], one-to-many, many-to-one, one-to-any, or one-to-all communications, routing [26; 35; 45; 47], _etc_. Clustering is particularly useful for applications that require scalability to hundreds or thousands of nodes. Scalability in this context implies the need for load balancing, efficient resource utilization, and data aggregation. Many routing protocols also use clustering to create a hierarchical structure and minimize the path cost when communicating with the base station.
## 2 Materials and Methods
### Catalyst Characterization  
The specific surface areas and the pore volumes were determined from N\({}_{2}\) adsorption-desorption isotherms at -196 \({}^{\circ}\)C using a Micromeritics ASAP 2020. In addition, before the measurements the samples were degassed at 250 \({}^{\circ}\)C for 2 h. The specific surface area was calculated through the Brunauer-Emmett-Teller (BET) method applied to the adsorption curve in the standard pressure range 0.05-0.3 P/P\({}_{0}\). By analysis of the desorption curve, using the BJH method, the pore size distribution was also obtained. The total pore volume (Vp) was evaluated on the basis of the amount of nitrogen adsorbed at the relative pressure of 0.95. The uncertainty on the estimated values was \(\pm\)10%.  
The phase composition was determined by Powder X-ray diffraction (PXRD) using a PANalytical Empyrean apparatus equipped with a multichannel detector (Pixel 3D) using (Cu K 45 kV-40 mA) radiation in the 20-115\({}^{\circ}\) 2\(\theta\) range, with a scan step of 0.01\({}^{\circ}\) for 20 s. The X-ray photoelectron (XP) spectra were recorded with a VG Microtech ESCA 3000 Multilab (VG Scientific, Sussex, UK), equipped with a dual Mg/Al anode. The experimental procedure was as already described [23]. The constant charging of the samples was removed by referencing all the energies to the C 1s set at 285.1 eV, arising from the adventitious carbon. Analyses of the peaks were carried out with the software providedby VG, based on non-linear least squares fitting program with Lorentzian and Gaussian component curves after background subtraction according to [PERSON] and Sherwood [27].  
The Electron paramagnetic resonance (EPR) spectra were collected at room temperature as a first derivative of the absorption signal of a JEOL JES-FA 100 EPR spectrometer operating in X-band, equipped with a standard TE\({}_{011}\) cylindrical resonator. The samples were placed in special quartz reactor and were fixed in the cavity center. The EPR spectra were recorded at following conditions: modulation frequency 100 kHz, microwave power 1 mW, modulation amplitude 0.2 mT, sweep width 20 mT, time constant 0.3 s and sweep time 2 min.  
Temperature-programmed reduction (TPR) measurements were performed in a flow system under the following conditions: H\({}_{2}\)-Ar gas mixture (10% H\({}_{2}\)); temperature ramp rate of 15 \({}^{\circ}\)C min\({}^{-1}\), flow rate of 24 mL.min\({}^{-1}\), and sample amount of 0.05 g [16]. A cooling trap (\(-\)40 \({}^{\circ}\)C) was installed before the thermal conductivity detector for removal of water (product of reduction). Preliminary calibration of the thermal conductivity detector performed by reducing different amounts of NiO to Ni\({}^{0}\) was used for the calculation of hydrogen consumption during the TPR measurements.
# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
###### Abstract  
In weather forecasting, numerical weather prediction (NWP) that is based on physical models requires proper post-processing before it can be applied to actual operations. Therefore, research on intelligent post-processing algorithms has always been an important topic in this field. This paper proposes a model output deep learning (MODL) method for post-processing, which can improve the forecast effect of numerical weather prediction. MODL is an end-to-end post-processing method based on deep convolutional neural network, which directly learns the mapping relationship between the forecast fields output by numerical model and the observation temperature field in order to obtain more accurate temperature forecasts. MODL modifies the existing deep convolution model according to the post-processing problem's characteristics, thereby improving the performance of the weather forecast. This paper uses The International Grand Global Ensemble (TIGGE) dataset from European Centre for Medium-Range Weather Forecasts (ECMWF) and the observed air temperature of 2 m obtained from Tianjin meteorological station in order to test the post-processing performance of MODL. The MODL method applied to temperature in post-processing is compared with the ECMWF forecast, Model Output Statistics (MOS) methods, and Model Output Machine Learning (MOML) methods. The Root Mean Square Error (RMSE) of the temperature field predicted by MODL and the observed temperature field is smaller than the other models and the accuracy of the temperature difference of 2 \({}^{\circ}\)C (Acc) is higher, especially where the prediction time is in the first three days. The lightweight nature of MODL also makes it suitable for most operations.  
Keywords:temperature forecast; post-processing; numerical weather prediction; deep learning; 3D CNN; fully CNN +
Footnote †: journal: Applied Soft. 2020, 10, 5808; doi:10.3390/app10175808
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Extraction  
The diverse nature of aflatoxin sample matrices demands the use of a combination of extraction and clean-up techniques in treating a sample prior to instrumental analysis. To achieve satisfactory recoveries of aflat, extraction needs to be applied to samples first, followed by clean-up, if necessary. For dry matrices, hydration is generally required to wet and swell samples, ensuring efficient extraction and separation of aflat absorbed within the sample. For fatty matrices, additional defatting steps are critical to eliminate co-extracted fatty interferences (e.g., lipid and cholesterol). In this section, commonly used extraction solvents (e.g., acetonitrile, methanol, chloroform and hexane) and techniques are reviewed and compared for their applications in aflatoxin analysis.
## 3 System Overview
### General Specifications
#### 3.2.3 Sensing & Monitoring Session  
Via the mobile app of the SOMS, the mobile device usage data of the patients are collected and sent to the main server. General phone usage contains all general status information about a phone,  
Figure 2: Smartphone overdependence decision algorithm.  
even data regarding whether the phone is turned on or off, whether the phone is in an idle state, and whether an Internet connection exists. The most important feature is Internet, SNS, and game usage monitoring. The general application data, exact usage time and period logs are monitored via the background application. The proposed application supports only Android phones. The system architecture is shown in Figure 3.  
The installed app collects the application usage information (amount of usage and frequency of usage) and sends it to the usage collection server. The Google app store application information is sent to the app classification server. Only the \"application classification\" information provided by Google is obtained. However, when the application classification information is omitted, the researcher manually types in the classification information. If data errors occur in the app classification server, the researcher manually corrects the errors. Then, the non-errors and data that are adjusted by the researcher are integrated as classified app data and sent to the usage storage. The application usage and classification information are integrated and sent to the usage storage.  
These usage data are useful for analyzing individual application usage information but are not useful for data analysis. In data analysis, the data must be refined. This task is performed by the refine server, which optimizes the refined data for visualization or analysis. The refine server contains a computational algorithm to classify data into meaningful fields, as shown in Table 1. Note that one measurement occurs, for example, when the user begins a game application one time.  
In the general measurement information field, classification by day, hour, or ten minutes was conducted to adjust the periods when the smartphone was off or not in use. If the non-usage period is included, the overuse level of the patient is underestimated. The usage data can be analyzed without a smartphone non-usage period bias to manage the data quality. The management fields that are classified and defined based on the binge/chronic status enable researchers to categorize binge overuse and chronic overuse. Binge overuse accounts for people who play games in a certain short period (for example, weekends) but play a lot, whereas chronic overuse accounts for people who play a lot throughout an entire week or period.  
With the survey data obtained in the baseline settlement phase, the refined data are sent to a web management system, which is specifically shown in Section 4.5. Using the data from the web management server, researchers can conduct the analysis.  
Figure 3: System architecture of sensing and monitoring app.
## Theoretical Modeling
### Mixed Time-Marching Integration Algorithm
#### 2.4.2 Corrector Step with Mid-Point Algorithm  
Having solved Equation (22) for \(\Delta\mathbf{p}\), the vectors of displacement, velocity and acceleration at step \(n+1\) can be obtained by the Newmark interpolation relations; the corresponding internal and inertial force vectors are to be obtained successively.  
The values at the mid-point can be interpolated and then substituted into the mid-point dynamic equilibrium equation (Equation (11)), which will, in general, lead to a residual \(\mathbf{g}_{m}\) that is not zero. Therefore, a standard process of Newton-Raphson (or modified Newton-Raphson or quasi-Newton) iteration is to be applied, which gives:  
\[\mathbf{g}_{m}=\tilde{\mathbf{K}}_{t,m}\delta\mathbf{p} \tag{25}\]where  
\[\mathbf{K}_{t,m}=\mathbf{K}_{tstat,m}+\frac{1}{\Delta t}(\mathbf{K}_{mas1,m}+ \mathbf{K}_{mas2,m}) \tag{26}\]  
is the equivalent dynamic tangent stiffness matrix of mid-point algorithm. Note it takes a different form to that previously given for the predictor step.  
By solving Equation (25), the improvement \(\delta\mathbf{p}\) to \(\mathbf{p}_{n+1}\) can be obtained, so that:  
\[\mathbf{p}_{n+1,new}=\mathbf{p}_{n+1,old}+\delta\mathbf{p} \tag{27}\]  
The more detailed descriptions of 3D formulations for a spatial beam and the numerical implementations of the rotational updates, as well as the overall solution strategy, can be found in the co-rotational nonlinear finite element literature [37; 39; 41; 42; 43].
# Analysis of the Impact of the New Two-Lane Shield Tunnel Underpass on the Existing Tunnels
## 3 Numerical Simulation of Underpass Construction
### Influence of Construction Parameters on the Deformation of Existing Tunnels
#### 3.5.2 Influence of Grouting Pressure on Deformation of the Existing Tunnels  
To analyze the influence of grouting pressure on the vertical deformation of the existing tunnel during the under-passing construction, the other parameters are kept unchanged. Three working conditions with grouting pressures of 0.10, 0.20, 0.30, and 0.40 MPa are studied. Figure 10 illustrates the vertical displacement pattern at the bottom arch of the left line of the existing tunnel under different grouting pressure conditions during the shield tunneling process.  
For the left line of the existing tunnel, the settlement trend follows a consistent pattern as the grouting pressure increases. When the grouting pressure is 0.10 MPa, the vertical displacement value of the arch bottom reaches 5.07 mm. In contrast, with a grouting pressure of 0.30 MPa, the maximum vertical displacement of the left line of the existing tunnel is 4.19 mm, while at 0.40 MPa, the displacement increases again. Based on these results, a grouting pressure of 0.30 MPa is considered optimal during the construction process. Additionally, in the early stages of shield construction, before the tunneling surface  
Figure 9: Influence curve of tunneling pressure on existing tunnel deformation.  
reaches the crossing area, the influence of grouting pressure on the existing tunnel is minimal. The effect of different grouting pressures on the tunnel's deformation becomes more pronounced once the shield reaches the existing tunnel's bottom.
## 7 Conclusions  
Real-time monitoring of workers' health and the construction site plays a significant role in enhancing the infrastructure of the construction site. Furthermore, past research has shown that using digital technologies, such as the IoT in conjunction with wireless sensors, can improve building site safety. Inspired by these aspects, this study aims to integrate 433 MHz LoRa, 2.4 GHz Zigbee communication, and Wi-Fi for establishing an IoT-based system on the construction site for monitoring of the workers' and the construction site's  
Figure 24: LoRa and Wi-Fi Gateway Module.  
Figure 25: Overall System Setup.  
health in real-time. As part of the system implementation in real-time, a hybrid architecture is proposed in this study and implemented multiple nodes with customized hardware are based on 2.4 GHz Zigbee, 433 MHz long-range (LoRa) and Wi-Fi. Furthermore, in the analysis of the evaluation metrics of LoRa, it is concluded that the lowest sensitivity is observed for SF 12 at BW 41.7 kHz and the highest is observed for SF 7 at BW 500 kHz; the maximum value data rate is observed at BW 500 KHz at CR 1 for SF 7, and the minimum data rate is observed at BW 41.7 at CR 4 for SF 12.  
Conceptualization, G.A.K.; methodology, A.R.; validation, S.V.A. and M.R.; formal analysis, A.G. and S.S.A.; writing--original draft preparation, G.A.K.; writing--review and editing, M.R.; A.A. and A.S.A.; supervision, R.S. and A.R.; funding acquisition, S.S.A. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/215), Taif University, Taif, Saudi Arabia.  
Not applicable.  
Data Availability Statement: Data in this research paper will be shared on request to the corresponding author.  
The authors declare no conflict of interest.
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 3 Methodology
### Modified MobileNetV3 Backbone
#### 3.1.2 AIR: Adaptive Inverted Residual Block  
We propose the AIR (Adaptive Inverted Residual) block, an enhanced version of MobileNetV3's inverted residual block. The AIR block incorporates two key components:  
1. Channel attention: A lightweight attention mechanism that selectively emphasizes informative channels: \[M_{\alpha}(F)=\sigma(MLP(AvgPool(F)))\otimes F\] (2) where \(MLP\) is a two-layer MLP with reduction ratio r = 16, which provides a good balance between performance and computational efficiency.  
Figure 2: Architecture of our modified MobileNetV3 backbone with asymmetric convolutions and AIR blocks. The backbone consists of six stages with progressively increasing channel dimensions and decreasing spatial resolutions, specifically designed to capture vertical patterns in traditional Mongolian text.  
2. Dynamic feature processing: Adaptive feature refinement through \[F_{out}=\begin{cases}F_{dw}(F_{exp}(x))+x,&\text{if $s=1$}\\ F_{dw}(F_{exp}(x)),&\text{otherwise}\end{cases}\] (3) where \(F_{exp}\) and \(F_{dw}\) denote expansion and depthwise convolution layers. The overall network architecture is organized into six stages: Input \(\xrightarrow{\text{Conv}_{3:3}}\) H/2 \(\times\) W/2 \(\times\) 16 (Stage 1) Bottleneck + Asymmetric \(\xrightarrow{\text{H/4 }\times\text{W/4 }\times\text{24 (Stage 2)}}\) Bottleneck + Asymmetric + AlR \(\xrightarrow{\text{H/4 }\times\text{W/4 }\times\text{24 (Stage 3)}}\) Bottleneck + Asymmetric + AlR \(\xrightarrow{\text{H/8 }\times\text{W/8 }\times\text{40 (Stage 4)}}\) Bottleneck + Asymmetric + AlR \(\xrightarrow{\text{H/16 }\times\text{W/16 }\times\text{80 (Stage 5)}}\) Bottleneck + Asymmetric + AlR \(\xrightarrow{\text{H/16 }\times\text{W/16 }\times\text{96 (Stage 6)}}\)  
From this backbone, we extract a feature pyramid with four levels of features that capture different aspects of the document:  
* \(F_{1}\) (16 channels): High-resolution features from stage 1, capturing fine details;
* \(F_{2}\) (24 channels): Medium-resolution features from stage 3, representing character structures;
* \(F_{3}\) (48 channels): Medium-high-level features from stage 5, encoding text block layouts;
* \(F_{4}\) (96 channels): High-level semantic features from stage 6, representing page organization.  
This design effectively captures hierarchical patterns in traditional Mongolian documents while maintaining computational efficiency through the combination of asymmetric convolutions, channel attention, and dynamic feature processing.  
The AIR block uses an expansion ratio of 6 for all layers, following MobileNetV3. The channel attention module consists of a two-layer MLP with reduction ratio \(r=16\), which provides a good balance between performance and computational efficiency:  
\[MLP(x)=W_{2}\delta(W_{1}x) \tag{5}\]  
where \(W_{1}\in\mathbb{R}^{C/r\times C}\), \(W_{2}\in\mathbb{R}^{C\times C/r}\), and \(\delta\) is the ReLU activation function. We use h-swish activation for all convolution layers except the last layer, which uses sigmoid activation.
## 1 Introduction  
The manipulation of electronic states in atoms with intense electromagnetic fields has been studied theoretically for several decades [1, 2]. One of the many investigated cases has been the existence of an excited state energetically embedded in an ionization continuum, nowadays known as an autoionizing Fano state [3], for which resonant couplings with strong fields have been studied in detail [3, 4]. Also, transient-absorption spectroscopy experiments have been carried out more recently with sensitivity to strong-coupling dynamics of Fano resonances in rare gas atoms [5, 6, 7, 8, 9]. These experiments employ broadband extreme-ultraviolet (XUV) attosecond pulses in combination with time-delayed intense femtosecond laser pulses in the near-infra red (NIR) spectral regime. Characteristic absorption lines are observed in the XUV-pulse spectrum after transmission through a moderately dense cloud of atoms or molecules. Hereby, measuring the transient absorption signal allows one to access the real-time dynamics of the XUV-excited system when it is driven by the NIR laser pulse [10] while retaining state-specific spectroscopic resolution. With the advent of XUV and x-ray free-electron-lasers (FELs) [11; 12], new possibilities opened up for ultrafast nonlinear light-matter interaction at high photon energy and intensity [13; 14; 15]. Regarding transient absorption spectroscopy with intense XUV-FEL pulses, signatures of strongly driven XUV resonant transitions have been observed [16; 17]. Hereby, the intense XUV-FEL laser pulses may strongly couple the ground state directly to a highly excited state of an atom. Predicting the ionization yield and photoelectron emission fur such a transition in helium, XUV-FEL absorption spectroscopy at high intensity has been studied theoretically [18; 19; 20]. In this work, we computationally model intense-field XUV absorption spectroscopy by directly observing the intense XUV light after its transmission through a resonant medium and use it to explain and predict line-shape asymmetry changes of resonant transitions strongly coupled by XUV light. To properly account for the broad spectral bandwidth of the ultrashort pulses, we solve the underlying few-level system of resonant couplings numerically and further compare it to an analytical approximation [1; 2] for monochromatic fields as a function of the detuning in terms of the generalized Rabi frequency. Our numerical findings for the autoionizing 2s2p doubly excited state in helium further support recent experimental results that have been obtained with intense XUV-FEL pulses [16].