esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of the Evolution of Energy Loss Rate in Cyclic Load Testing of Marine Soft Soil
## 4 Stabilized State of Marine Soft Soil Based on Envelope Surfaces of Energy Dissipation Rates
### Stability Evaluation  
Marine soft soil unit No. 87, located on the tunnel axis and 5 m away from the tunnel vault, was selected as the research object. First, the strain distribution of the marine soft soil at the top of the tunnel under the influence of train load was obtained through numerical calculation. Next, the vertical strain and stress values for unit No. 87 were extracted, and the corresponding relationship curve was plotted, as shown in Figure 9. Finally, the energy dissipation rate of the marine soft soil in this unit was calculated using Equation (7), and the result was compared with the energy dissipation rate envelope for marine soft soil at a confining pressure of 100 kPa and 55% water content, as shown in Figure 10.  
Figure 10: Schematic diagram of energy dissipation rate versus envelope surface for marine soft soil in unit No. 87. Different colors represent different values of energy dissipation rate, from blue to red represents the gradual increase of energy dissipation rate in marine soft soil.  
Figure 9: Vertical strain characteristics of marine soft soil unit under train running loads.Different colors represent the formation deformation value, and the formation deformation value gradually increases from blue to red.  
From Figure 9, the following observations can be made: Before the train load is applied, the stress distribution is relatively uniform and low. When the train load begins to act, the strain starts to concentrate in the area directly affected by the load, forming a local stress peak. As the train load is fully applied, the strain distribution becomes more pronounced, revealing areas of high stress and potential destabilization. Meanwhile, as the vertical strain increases, the stress amplitude in unit No. 87 gradually rises, indicating the marine soft soil's response to the cyclic loading. Using Equation (7), the energy dissipation rate for the unit No. 87 body was calculated to be 28.46%. This value was then plotted in the same coordinate system as the energy dissipation rate envelope surface for a perimeter pressure of 100 kPa, as shown in Figure 10.  
As shown in Figure 10, the energy dissipation rate of the marine soft soil in unit No. 87 falls within the envelope surface, indicating that the soil is in a stable state and no significant deformation or damage has occurred. For this project, the marine soft soil met the operational requirements of the train, with no substantial impact on the tunnel structure, ensuring that the shield tunnel remained in compliance with safety standards.
## 2 Conventional Edge-Based MTF Measurement Methods  
### Imaging Degradation Process of Edge Target  
The optical RS imaging system can be considered a linear system with spatial shift invariant versus radiance. According to the theory of imaging, the image obtained by a linear spatial shift-invariant imaging system can be represented by the convolution of the imaging system's response and object target. Considering the noise, the imaging process in the spatial domain and spatial frequency domain can be expressed as the following equations according to the convolution theorem:  
\[i(x,y)=l(x,y)\otimes h(x,y)+n(x,y) \tag{1}\]  
\[I(f_{x},f_{y})=L(f_{x},f_{y})\cdot OTF(f_{x},f_{y})+N(f_{x},f_{y}) \tag{2}\]  
where \(i(x,y)\) represents the image target, \(l(x,y)\) represents the object target, \(h(x,y)\) is the Point Spread Function (PSF) of the imaging system, \(n(x,y)\) represents the additive noise, \(\otimes\) is the convolution operator, \(I(f_{x},f_{y})\), \(L(f_{x},f_{y})\), \(OTF(f_{x},f_{y})\), \(N(f_{x},f_{y})\) stands for the Fourier Transform of \(i(x,y)\), \(l(x,y)\), \(h(x,y)\), \(n(x,y)\), respectively. \(OTF(f_{x},f_{y})\) is called the Optical Transfer Function (OTF, complex function), and its modulus is MTF.  
The object target in the edge method can be approximated as the Heaviside step function, which is expressed by \(hea(x)\) or \(hea(y)\)[1]:  
\[l(x,y)=a\cdot hea(x)\cdot unit(y)+b\cdot unit(x)\cdot unit(y) \tag{3}\]  
where \(hea(x)\) is the Heaviside step function centered on, \(x=0,\ unit(x)=1\) for all values of \(x\), \(b\), and \(a+b\) represents the pixel values on both sides of the edge.  
Moreover, for sensors using a CCD in the image plane, the system is no longer strictly shift-invariant; it is necessary to take sampling into account. Generally, the Dirac comb sampling function \(comb(x/p_{x},y/p_{y})\) is used to represent the discrete sampling effect of CCD, where \(p_{x}\) is the size of IFOV and \(p_{y}\) is the GSD of the push broom imaging system.  
To sum up, after blurring, noise, and sampling degradation, the model of the edge target imaging process in the \(x\)-direction spatial domain and spatial frequency domain is as follows:  
\[i(x)=ESF(x)=\{[a\cdot hea(x)+b\cdot unit(x)]\otimes LSF(x)+n(x)\}\cdot comb(x/p _{x}) \tag{4}\]  
\[I(f_{x})=\{[a\cdot Hea(f_{x})+b\cdot\delta(f_{x})]\cdot OTF(f_{x})+N(f_{x})\} \otimes Comb(f_{x}/f_{sx}) \tag{5}\]  
where \(LSF(x)\) is the cross-sectional curve of \(PSF(x,y)\) in the \(x\) direction, and \(f_{sx}=1/p_{x}\) is the sampling frequency for the \(f_{x}\) axis.
## 5 Implementation and Results  
The following is a chapter on the actual implementation of the proposed system. In this paper, a small test-bed was designed and implemented for interlocking and interoperability test of the system between system devices.  
Figure 12 shows an AR-based smart building disaster management service system board. A total of 10 system devices are installed on the board. There were electronic leak detectors, temperature and humidity sensors, network cameras, multi gas and dust detectors, flame detectors, gas leak detectors, a damper breaker, distribution board, and a gas valve breaker installed in a board and data was transmitted by the smart gateway. A Quick Response (QR) code was embedded in the side of each device, and it provided the monitoring and control interface for status information representation and actuator control through device self-recognition or QR code recognition on smart pad.  
Figure 13 shows the AR-based application development tool \"Unity\" and Figure 14 shows the AR-based fire situation guideline simulation UI screen of the proposed system developed by the Unity tool. Figure 14a is a simulation UI screen of the AR based safe path guideline for rescuers, and Figure 14b,c shows the UI screen that monitored the building room information and fire hazard area.  
Figure 13: Unity, AR software development tool screen: (**a**) “Unity” Development Software; (**b**) Source Code Development of the Proposed Solution.  
The following Figure 15 shows the Smart Building Model. The purpose of this model is to provide evacuation guideline service to the occupants and rescuers by scanning the building structure using smart pads in case of fire inside the building (Figure 16). The Smart Building Model consists of scenarios for reacting and resisting from fire in actual buildings, and it can be verified with AR-based virtual building model motion simulation. As shown in Figure 13, the rescuers can run the AR-based Smart Building Disaster Management Service application and click on the 1 st floor to 3 rd floor drawings to view the floor plans, identify the fire point and the danger area, and confirm the location of the refuge persons. They can check the rescue route and the situation information according to the location of the refuge person when clicking on the refuge person. Through this system, victim rescue and fire suppression can be done within a short time by providing evacuation and rescue guides most suitable for occupants and rescuers. Table 9 shows the device specifications implemented in the AR-based Smart Building Disaster Management System. Figure 14 shows the actual implementation picture of this system board and Smart Building Model.  
The following subsection shows system operation steps of the occupants and rescuers.
## 1 Introduction
[PERSON] et al. [30] devised a pipeline for mobile devices, which incorporates MobileNetv2-SSD [31] for screen detection, EAST [32] for text detection, and Rosetta [33] for text recognition; the entire pipeline requires 1500 ms on Samsung Active Table 3 device. The above-mentioned deep learning methods often have a complex network structure and a large amount of calculation, resulting in a trade-off between detection accuracy and inference speed. Additionally, missed detections or misidentifications still occur with state-of-the-art lightweight networks in complex real-life scenarios, such as lighting conditions, specular reflection, and shadow occlusions. In order to combat these challenges in practical applications and enhance network accuracy and efficiency, we propose a lightweight network called DAMP-YOLO. The main work and contributions of this paper are as follows:  
1. The proposal of a deformable CSP bottleneck (DCB) module that adapts by learning the kernel offset for objects, making the network resilient to noise interference from objects with similar shapes and structures, thus achieving high accuracy;
2. The design of the aggregated triplet attention (ATA) mechanism enhances global information interaction by aggregating diverse branches, effectively capturing attention across all dimensions. 3. Aiming at the particularity of meter images: meter data augmentation (MDA) is used to generate training data that are similar to natural scenes to improve the robustness of the model in complex environments;
4. Utilize network slimming to prune unimportant network channels to achieve model compression and accelerated computing;
5. Deploy the recognition pipeline to the Jetson TX1-based intelligent car to realize meter reading recognition in real-world scenarios;
6. Our model can still enhance accuracy even with reduced parameters in general object-detection datasets. The remainder of this paper is organized as follows. In Section 2, we review related work on meter reading recognition and deep learning models for embedded deployment. Section 3 presents the proposed DAMP-YOLO network architecture and its components. In Section 5, we evaluate the performance of our model on the meter reading and VOC datasets, compare it with state-of-the-art methods, and discuss the deployment of the proposed model in the Jetson TX1 platform. Finally, Section 6 concludes the paper and the future research directions.
###### Abstract  
A general near-field (NF) in-band synthesis strategy is presented for the direct layout optimization (DLO) of reflectarrays with application to the generation of plane waves for the mm-wave band. The technique relies on the definition of relevant figures of merit (FoM) and the use of a gradient-based minimization algorithm. To this end, the volume where the NF is computed is divided into a number of disjoint regions where the FoM are defined. These FoM define the performance of the antenna and their direct optimization enables improvement compared to previous approaches described in the literature, while reducing the memory footprint of the algorithm and accelerating computation. The optimization procedure is divided into several stages to facilitate convergence towards a successful outcome. First, a phase-only synthesis is carried out at a single frequency. Then, a reflectarray layout is obtained using a method of moments based on local periodicity, accounting for mutual coupling between elements. Finally, an in-band DLO is performed at a number of frequencies directly optimizing the FoM. The results show that the obtained reflectarray layout complies with the requirements in the frequency range 27 GHz-29 GHz within the 5G new radio n257 band.  
reflectarray antenna; near-field synthesis; near-field pattern; gradient-based algorithm; plane-wave generator; compact antenna test range (CATR); dual linear polarization +
Footnote †: journal: 2022  
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Footnote 0: Copyright © 2022 by the author. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Digital-Twin-Based Monitoring System for Slab Production Process
## 4 Experiment and Verification  
### Experimental Environment and Validation Scheme  
To realize the monitoring of the slab production process based on digital twinning in harsh environments, this paper has developed a digital twinning monitoring system for slab production processes.  
The on-site standard configuration of the digital twinning monitoring system primarily consists of three components: the Monitoring Control Unit (MCU), the Field Control Unit (FCU), and sensors, including the SA-S6016 dual-color infrared thermometer temperature sensor, the T701E weighing sensor from Right Corporation, the ADAM-3600 humidity sensor from Advantech Corporation, and the GZ-XDS-1 illuminance meter from Jiandanren Technology. The system was implemented on an 11 th Gen Intel(R) Core (TM) i7-11700K @ 3.60 GHz 3.60 GHz CPU with 32.0 GB memory, using Unity3D 2021.3 and Visual Studio 2013.  
Figure 5 illustrates the experimental verification process. Initially, cameras and various functional sensors are employed to gather environmental and slab image data. Subsequently, the defect recognition algorithm is applied to determine the location and category of slab defects. The identified defects with the data acquired from various functional sensors are integrated, enabling the modeling of slabs along with defects and the establishment of a defect database. Additionally, this integration allows for predicting the probability of defect occurrence and monitoring production data.
## 4 Analyses
### Q1: How Much Information Is Lost with Reduced Temporal Resolution?  
Understanding the effect of temporal aggregation, quantifying the induced information loss, and identifying the signals which are affected the most are important steps to find a trade-off between data volume and information content. Hence, storage policies can be defined and possibly additional space can be allocated for the signals that are not recommended for aggregation. The methods and approaches in this subsection address the guiding question, if data when sampled at a higher frequency contains richer information. In this study simple approaches based on statistics indicators and tests are replicated. Then, given their limitations a different method based on the analysis of the aggregation error is devised.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 3 Classifications of Cyber-Attacks in SGs
### Denial of Service (Dos) Attacks  
DoS (Denial of Service) means disrupting the service or destroying the function of the service. It does not allow users to access or offer prolonged service. The purpose of the DoS attack type is to exceed the limit of resources and disable the system. The attack usually occurs over a single Internet Protocol (IP), and in this case, it can be prevented by using a Firewall [86; 93].
# Generating Fingerings for Piano Music with Model-Based Reinforcement Learning
## 3 Environmental Model
### Quantification of Fingering Transition Difficulty
#### 3.4.2 Hand Movement Distance  
Hand position refers to the position of the hand on the keyboard. Since the hand covers an area of multiple piano keys, it is not convenient to be precise about specific keys. However, by observing the anatomical diagrams of the human hand, we can approximate the hand position with the positions of middle finger fingers when the hand is naturally relaxed on the keys. In cases where multiple fingers are involved in playing chords and other techniques, the hand position can be approximated by averaging the positions of the thumb and little fingers. The hand displacement distance can be calculated by considering the changes in hand position before and after fingerings, as shown in Figure 4b.  
For the left hand, \(h=-1\), and for the right hand, \(h=1\). In the case of single-note fingerings, where \(f_{n}\) represents the finger pressing the note \(n\), the hand position can be calculated as \(p_{s}(f_{n},n)=d_{A0}(n)+h\cdot(3-f_{n})\). For chord fingerings with multiple notes, let us consider the finger \(f_{l}\) and note \(n_{l}\) of the lowest pitch in the fingering, as well as the finger \(f_{h}\) and note \(n_{h}\) of the highest pitch. The hand position for the chord fingering can be determined as \(p_{c}(f_{l},n_{l},f_{h},n_{h})=(p_{s}(f_{l},n_{l})+p_{s}(f_{h},n_{h}))/2\).  
If the positions before and after the fingering transition are denoted by \(p_{pre}\) and \(p_{next}\), respectively, the hand position movement distance can be calculated as \(d_{h}=p_{pre}-p_{next}\). A larger hand position movement indicates a stronger discomfort during the fingering transition, resulting in a lower reward.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 3 Multi-Parameter Optical Analysis and Design of PM Scanning Heads
### Scanning Function and Velocity
From this analysis, one can conclude that the distance \(L\) has the highest impact on the linearity of \(h(\theta)\), while the parameter \(x\) has the smallest impact. However, by combining the influence of all three parameters, the non-linearity of \(h(\theta)\) can be further improved. The issue is that, as \(R\) results from calculus (such as the one in Section 6) and \(x\) from design equations (such as those in this section and in Section 3.5), the options regarding these two parameters are limited. Therefore, one must find solutions to increase \(L\), as addressed in Section 4. This is also favorable from the point of view of the scanning velocity \(v\), which is proportional to \(L\)--Equation (5) and Figure 7(c2). Furthermore, \(v\) is proportional to the rotational velocity  
Figure 7: Study of the scanning function (**1**) and the scanning speed (**2**) of PM scanning heads with regard to their characteristic parameters: (**a**) Apothem \(R\); (**b**) \(x=e/R\) parameter; (**c**) length \(L\) from the fixed (vertical) incident ray to the lens. \(\omega\): As \(\omega\) increases, it has a portion of approximately \(\theta=\pi/4\), where it is approximately constant, as observed from the second row of Figure 7.
# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
## 2 Materials and Methods
### Gated Recurrent Unit (GRU)  
The gated recurrent unit (GRU) is a recent development in RNN architecture, designed as a more straightforward alternative to LSTM [49]. The GRU also aims to address the vanishing gradient problem and handle long-term dependencies in sequential data [50]. Although developed from LSTM, the GRU is considered more efficient due to its simpler structure and fewer parameters [51]. Unlike LSTM, which has separate memory cells and gates, the GRU integrates the forget and input gates into a single update gate, simplifying the architecture and reducing the number of parameters [49; 50]. The GRU uses only two gates: the update gate and the reset gate. The reset gate determines how much of the hidden state should be considered [52]. This simpler structure enables the GRU to perform well in tasks involving shorter sequences while remaining robust for longer sequences [53]. Architecture of GRU has significant differences from LSTM [47] and GRU has been successfully applied to machine translation, speech recognition, and other time-series applications [54; 55; 56].  
Stacked LSTM and GRU architectures are more sophisticated variants of the LSTM and GRU models, where multiple LSTM or GRU layers are arranged in sequence to enhance the model's ability to detect complex patterns in the data and improve prediction accuracy [47; 57]. These stacked layers integrated the benefits of a single LSTM or GRU layer. In the lower layers, multiple LSTMs or GRUs are stacked to capture short-term dependencies and extract features that reflect the various sources of variance in the input data. These extracted representations are then combined in the higher layers, which capture long-term dependencies [56]. In stacked LSTM and GRU models, the input for each intermediate layer is determined by the computations performed in the preceding layer. Stacking can be achieved by combining two or more layers, with options such as 2-stacked (two layers) or 3-stacked (three layers) [47].
# Utilization of the Nutritional Potential of Wheat Bran Using Different Fractionation Techniques
## 5 Bran from the Standard Mill Process and Possibilities for Its Fractionation
### Enzymatic Fractionation Methods
#### 5.1.1 Extraction of Arabinoxylans  
Currently, base-catalyzed extraction in aqueous media is used for the extraction of arabinoxylans from wheat bran. This method consists of two steps. The first is the removal of starch and protein from the bran by an enzymatic method in an aqueous environment under ideal conditions for enzyme activity. Via this route arabinoxylans lose attached functional groups such as ferulic acid [47].
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 4 Conclusions  
Aflatoxin contamination will remain a global public health concern yet challenges that each country/region faces are very diverse. Global trading, climate changes and diverse regulatory policies have further complicated the issue. Therefore, the need for analytical tools to combat mycotoxin contamination varies, prompting the development of broad ranges of chromatographic and mass spectrometric methods for the determination of aflat. This review highlights trends and novel techniques in the course of the history of aflatoxin methodology, with emphasis on applications of sample preparation, chromatography, and mass spectrometry to food analysis. Aflatoxins are heterogeneously distributed in foods and feeds so each step including sampling, sample preparation, extraction, clean-up and instrumental determination plays a crucial role in the analysis of aflat. The trend is obvious any new techniques can outperform existing ones in terms of specificity, sensitivity and throughput will be appreciated. Following the transition from TLC, LC-UV/FLD to LC-MS, advances in detection technologies are complimented by the simplification of sample preparation, transforming multistep extraction/clean-up to one step \"dilute-and-shoot\". Similarly, identification has shifted from non-specific properties (e.g., visual inspection, UV adsorption or fluorescence emission) to intrinsic properties (e.g., _m/z_, ion ratios, molecular fragmentation) and traditional single-/single-class mycotoxin analysis has been gradually replaced by multimycotoxin analysis using LC-MS. In the future development of analytical methods, these are the areas where research will focus on. Our ability to replace and upgrade current techniques in these areas will, to a great measure, determine our success to monitor aflat in foods.  
[PERSON] and [PERSON] proposed the scope and structure of the review, performed literature search and wrote the review. All authors have read and agreed to the published version of the manuscript.  
This review received no external funding.  
We are extremely grateful to the FDA Library staff for their excellence assistance in providing references (journal articles and book chapters). During the COVID-19 pandemic, their professionalism and support are very much appreciated. We thank [PERSON] at FDA, Center for Food Safety and Applied Nutrition for proofreading the manuscript and providing helpful comments. We also thank [PERSON], M.Sc., [PERSON], PhD at National Reference Laboratory, ICAR-National Research Centre for Grapes for their assistance.  
The authors declare no conflict of interest.