esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Power Consumption of Function-Level Code Relocation for Low-Power Embedded Systems
## 3 Configuration of Entire Environment  
Programmers are able to create programs and design systems by using the following formal syntax of programming languages built for convenience. However, the system is not designed to execute the programming language itself. Because the system does not understand a high-level language, it needs to be transformed via several steps such as in Figure 2. In the general flow of  
Figure 1: Basic components and processes of code migration methodology.  
transformation, the language human beings can understand is assembly language. This language strongly coincides with machine language, which is the only language a computer understands. In this respect, analysis using assembly language, which is defined by instructions, is the ultimate and essential way to understand the execution of a program accurately.  
Ultimately, the analysis of the effect of instructions on the power consumption of a system requires an environmental setting that can measure the power consumed by a particular MCU. In other words, the implementation of low-power embedded systems can be demonstrated by accurately measuring the power consumption in the actual environment. In this study, the environment is simultaneously configured for conducting the instruction trace and also the power measurement with high accuracy. Details of the environment appear in Table 1.  
Compared to other computing systems, environmentally sensitive embedded systems should not only examine the software tools thoroughly but also hardware related devices or peripheral components. All these components influence our understanding of the overall performance and stability of the systems. In this regard, tracing the instructions and measuring the power of the actual system reflect the actual operations. Consequently, these processes create firm grounds for the subsequent results of code profiling and show the model for prior relocation-scoring to be reasonable.
## 2 Microgrid Optimization Scheduling Model
### Mathematical Model of Microgrid  
#### 2.1.1 Objective Function  
The task of multi-objective optimization for microgrids is to minimize economic costs and carbon emissions as much as possible while satisfying all constraints. The optimization and scheduling objective functions of microgrids are mainly divided into cost objective function and carbon emission objective function.  
**Cost objective function:** The target function of power generation and the purchase cost objective function is shown as  
\[\min f_{1}(X)=\sum_{i=1}^{T}\left\{\begin{aligned} &\sum_{i=1}^{N_{S}}\left[U_{i}(t)P_{Gi}(t)B_{Gi }(t)+S_{Gi}(t)|U_{i}(t)-U_{i}(t+1)|\right]-(P_{Grid}(t)B_{Grid}(t))+\\ &\sum_{j=1}^{N_{S}}\left[U_{j}(t)P_{si}(t)B_{sj}(t)+S_{sj}\big{|}U_ {j}(t)-U_{j}(t+1)\big{|}\right]-(P_{Grid}(t)B_{Grid}(t))\end{aligned}\right\} \tag{1}\]  
where \(T\) represents the total duration, \(N_{S}\) represents the generation of energy, \(N_{s}\) represents a storage unit, \(U_{i}(t)\) represents the state of the \(i\)-th generate unit at time \(t\) (on or off), \(P_{Gi}(t)\) represents the output capacity when the \(i\)-th storage device is turned on, and \(B_{Gi}(t)\) represents the energy cost of the \(i\)-th storage device at time \(t\) when it is turned on. The energy cost of the \(j\)-th storage device at time \(t\) when turned off is \(B_{Sj}(t)\), and \(S_{Gi}(t)\) and \(S_{g}(t)\) represent the costs involved in the \(i\)-th storage device and \(j\)-th storage device during the startup or shutdown function, respectively. In addition, \(P_{Grid}(t)\) and \(B_{Grid}(t)\) represent the capacity and cost exchanged with the provided market, respectively.  
**Emission mitigation:** The objective function for emission mitigation can be shown as  
\[\min f2(X)=\sum_{t-1}^{T}\left\{\sum_{i=1}^{N_{S}}\left[U_{i}(t)P_{Gi}(t)E_{Gi }(t)\right]+\sum_{j=1}^{N_{S}}\left[U_{j}(t)P_{s}(t)E_{sj}(t)\right]+\left(P_ {Gridj}(t)E_{Gridj}(t)\right)\right\} \tag{2}\]  
where \(E_{Gi}(t)\) represents the pollution level of the \(i\)-th generator unit, and the pollution level of the \(j\) storage unit is represented by \(E_{ij}(t)\). \(E_{Gr}(t)\) indicates the pollution level in the market at time \(t\).
# An Experimental Study of the Leakage Mechanism in Static Seals
## 2 Review the Leak Rate Prediction Model  
### Fractal Surface  
This model is used to calculate the static sealing surface by high-precision grinding, and studies [24; 25; 26] have shown that the seals' surfaces have the self affine characteristics that can be approximately described using the three-dimensional Ausloos-Berman function:\[z(x,y) =L\left(\frac{G}{L}\right)^{D-2}\left(\frac{\ln\gamma}{M}\right)^{1/2 }\sum_{m=1}^{M}\sum_{n=n_{1}}^{n_{max}}\gamma^{(D-3)n}\] \[\times\left\{\cos\phi_{a}-\cos\left[\frac{2\pi\gamma^{n}(x^{2}+y^{ 2})^{1/2}}{L}\cos\left(\arctan\left(\frac{y}{x}\right)-\frac{\pi m}{M}\right)+ \phi_{a}\right]\right\}, \tag{1}\]  
where \(z\) and \((x,y)\) are the height and coordinates of the surface, respectively. The parameter \(M\) denotes the number of superposed ridges and is used to construct the surface. \(L\) is the sample length; \(\phi_{a}\) is a random number in the range between 0 and \(2\pi\). The surface roughness is characterized by the frequency spectrum \(\gamma^{n}\), where \(\gamma\) satisfies \(\gamma>1\); \(n\) represents the frequency index of asperities. \(D\) (in the domain of \(2<D<3\)) and \(G\) are the fractal dimension and roughness constant, respectively, and are independent of \(\gamma\).  
Introduce a reference plane on the fractal surface as shown in Figure 1. The seals interface are divided into a region of contact and a region of leakage by the reference plane. According to the fractal theory, the fractal surface is simplified to asperities distributed above the reference plane and pores distributed below the reference plane. The fundamental wavelengths of asperities and pores in the reference plane are \(l\) and \(l_{v}\), respectively, which is called the characteristic length of the asperity and the pore [18].  
Considering the assumption [18], the cross-sectional profile of the asperity and pore are given by:  
\[z(x)=G^{D-2}\left(\ln\gamma\right)^{1/2}l^{(3-D)}\left[1-\cos\left(\frac{2\pi x }{l}\right)\right](0<x<l), \tag{2}\]  
\[z_{v}(x)=G^{D-2}\left(\ln\gamma\right)^{1/2}l_{v}^{(3-D)}\left[\cos\left(\frac {2\pi x}{l_{v}}\right)\right](0<x<l_{v}). \tag{3}\]  
The above Equations (2) and (3) can obtain the height \(\delta\), curvature radius \(R\) and volume \(v\) of the asperity and pore, and are used to calculate the contact load and leak rate.  
According to the fractal theory [17], the distribution function of the asperity and pore are given by:  
\[n_{l}(l)=(D-1)l_{max}^{D-1}l^{-D}, \tag{4}\]  
\[n_{l,v}=(D-1)\left[\frac{4(3-D)A_{l}}{\pi(D-1)}-l_{max,v}^{2}\right]^{\frac{D -1}{2}}l_{p}^{-D}, \tag{5}\]  
Figure 1: Schematic of the ring gasket leak rate prediction model.  
where \(l_{max}\) and \(l_{max,v}\) are the maximum characteristic length of asperities and pores, respectively.
## 4 Methods
### Preprocessing
#### 4.1.3 Incidence Angle Normalization  
As the images within the time series were recorded with multiple incidence angles, they must be normalized to reduce the effect of incidence angle on backscattering signal. In this regard, the SAR images are normalized to a reference incidence angle of 40\({}^{\circ}\), using scene-based linear regression [71]:  
\[\sigma_{0}^{norm}=\sigma_{0}^{i}-\beta\Big{(}\theta_{i}-\theta_{ref}\Big{)} \tag{1}\]  
with \(\sigma_{0}^{norm/i}\) being the normalized and original backscattering signal, \(\theta_{ref/i}\) being the reference and original incidence angle and \(\beta\) as the slope derived from linear regression between \(\sigma_{0}^{i}\) and \(\theta_{i}\). An additional correction of azimuthal angle was not considered, as azimuthal anisotropy is mainly caused by the orientation of steep slopes, as present in higher and lower mountain ranges. Focusing on agricultural areas, predominantly located on flat surfaces, the influence of the azimuth angle is substantially lower [71]. Via the investigation of the influence of azimuthal angle over winter wheat fields, it was found negligible within a dense time series [72].
## 3 Results and Discussion  
### Screening for Lactococcus lactis  
After the screening procedure, 105 typical LAB colonies were isolated from the fermented milk samples. Only 26 strains that were catalase-negative and Gram-positive cocci which acidified the cow milk the fastest were selected for further research. All selected strains grew in M17 liquid medium with 1% glucose at 30 and 37 \({}^{\circ}\)C, and with addition of 2, 4 and 6.5% NaCl (Table 1). Only two isolates did not grow at 10 \({}^{\circ}\)C (7.7%). The best growth of all the strains was observed at 30 and 37 \({}^{\circ}\)C and in media supplemented with 2 and 4% of NaCl. Two isolates, A13 and A14, showed the best growth ability during the experiment even in the presence of 6.5% NaCl, which is a prerequisite for their potential use as starter cultures. A similar screening procedure was described by [PERSON] et al. [5], [PERSON] [28], [PERSON] [29], and [PERSON] [30], where the researchers obtained 29 and 14 isolates confirmed as _Lactococcus lactis_ from milk, 40 isolates from dairy products, and 10 isolates from Algerian cheese \"J\"ben of Naama\", respectively. In contrast to strains A13 and A14, raw milk isolate IBB109 and bryndza cheese isolate IBB417 showed no growth and a 5-fold reduction in growth in the presence of 5% NaCl, respectively [11].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{4}{c}{**Temperature**} & \multicolumn{4}{c}{**Salt**} \\ \hline
**Strain** & **10 \({}^{\circ}\)C** & **30 \({}^{\circ}\)C** & **37 \({}^{\circ}\)C** & **45 \({}^{\circ}\)C** & **2\%NaCl** & **4\%NaCl** & **6.5\%NaCl** \\ \hline A1 & + & ++ & ++ & - & ++ & ++ & + \\ A2 & + & ++ & ++ & - & ++ & ++ & + \\ A3 & + & ++ & + & - & ++ & ++ & + \\ A4 & + & ++ & ++ & - & ++ & ++ & + \\ A5 & + & ++ & ++ & - & ++ & ++ & + \\ A6 & + & ++ & ++ & - & ++ & ++ & + \\ A7 & - & + & ++ & - & ++ & ++ & + \\ A8 & + & ++ & + & - & ++ & ++ & + \\ \hline \end{tabular}
\end{table}
Table 1: Growth of 26 selected strains at various temperatures and salt concentrations after 48 h of incubation.
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 3 PM2M Overlay Network over PRACH
### Superframe Structure of PM2M  
The superframe structure of the proposed Priority-based Machine-to-Machine (PM2M) overlay network is shown in Figure 5. Time is organized in beacon-delineated superframes that start immediately after the reception of beacon; UEs send their messages in the CSMA-CA manner after completing the backoff procedure, as explained below. eNodeB has to acknowledge the message,otherwise the message will be retransmitted until the retransmission limit is reached. While H2H traffic will continue to follow the four-way handshake to access the network, the PM2M overlay network reduces four-way handshake to a simpler two-way one.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Spatial Pyramid Pooling  
In semantic segmentation, traditional CNNs typically use the FCN structure, which adds upsampling layers at the end of the network to upsample low-resolution feature maps to the original input image size for pixel-level classification. However, this approach requires a high computational cost and large amounts of memory, making it unsuitable for real-time semantic segmentation tasks.  
Spatial pyramid pooling (SPP) [55] was proposed to address this problem. The core idea of the SPP network is to introduce pyramid pooling layers in CNNs that map different sizes of feature maps to a fixed-size feature vector through pooling operations, thereby avoiding the need for upsampling operations and greatly reducing the computational cost and memory overhead without sacrificing accuracy. Moreover, an SPP network supports inputs of arbitrary sizes and has translation and scale invariance, making it suitable for object detection and semantic segmentation tasks involving various scales and orientations.  
With the introduction of the SPP network, semantic segmentation has made considerable progress. Future research directions include optimizing the SPP network structure, exploring multiscale feature fusion methods, and introducing attention mechanisms and context information to further improve the performance and speed of semantic segmentation.
# Study on the Prediction Method of the Ultra-Low-Cycle Fatigue Damage of Steel
## 3 ULCF Life Prediction for the Q345 qC Base Material and Weld Joints
### Strain Cyclic Loading Test for Base Material and Welded Joints
#### 3.1.2 Test Results  
The crack initiation life \(N_{f}\) and fracture life \(N_{r}\) of the base metal and weld specimens and the ratio of \(N_{f}\)/\(N_{r}\) are shown in Table 3. The average fatigue life \(N_{f}^{{}^{\prime}}\) decreases with the increase in strain range. The average fatigue life \(N_{f}^{{}^{\prime}}\) of the base metal specimen is greater than that of the weld specimen in the same strain range. In addition, the \(N_{f}\)/\(N_{r}\) ratio is from 84.0% to 97.0%, which indicates that the crack initiation stage occupies most of the cyclic loading stage. Fatigue cracking occurs at the edge of the cross section of the base metal specimen. The post-fracture path is perpendicular to the axis, as shown in Figure 5a. For the weld specimen, the fatigue crack originates in HAZ near the weld metal area. The post-fracture path shows that the cracks that originated at HAZ pass through the weld metal and extend to HAZ on the opposite side, and the cracks that formed on the weld specimen are diagonal, as shown in Figure 5b.  
Figure 4: Size of specimens for ultra-low-cycle fatigue (ULCF) (unit: mm). (a) Base metal specimen and (b) weld specimen.
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 3 Defect Inspection Based on Generative Models
### Defect Inspection Based on GANs
Using these generative models for constructing defect inspection methods can not only solve the issue of limited samples but also guide the learning mode of GANs due to the constraints on input samples. The selected methods that are based on image-to-image translation GAN architecture are presented in Table 4. For instance, [PERSON] et al. [94] introduced a weakly-supervised defect segmentation method that adopted the CycleGAN to generate defect-free templates through image-level annotation training and designed an attention module to enhance the ability to eliminate defective regions. [PERSON] et al. [95] presented an unsupervised learning method suitable for defect detection with small samples, which expands the dataset by generating defective images and utilizes the attention mechanism to highlight key features in the images as a way to improve the accuracy of defect classification. [PERSON] et al. [96] combined the SinGAN with the channel attention mechanism to generate high-quality and diverse defect images and improved classification accuracy by an image cutting and fusion method. [PERSON] et al. [97] proposed an improved StyleGAN model that actively places different emphases on images of different resolutions to enhance the generation quality and employed different CNNs for classification. [PERSON] et al. [98] proposed a structural defect detection algorithm based on the pix2 pix network and migration learning using CGAN to reconstruct the defective region, calculate the difference between the reconstructed image and the defective image, and perform segmentation tasks on fabric surfaces. [PERSON] et al. [99] introduced the foreground perception cycle-consistent adversarial network (FCGAN), which combines an attention network with a generator and a discriminator to recognize discriminative foregrounds and synthesizes coordinated and realistic pseudo-defect images. [PERSON] et al. [100] designed a data augmentation module and a self-attention mechanism into the progressive GAN (Pro-GAN) to generate high-quality and high-diversity surface defect images. [PERSON] et al. [101] combined dual U-Net networks with the pix2 pix network to generate defect localization maps, and then employed the YOLOv3 for fabric defect detection. [PERSON] et al. [102] proposed a new GAN for image-to-image translation to achieve high-quality synthesis from masked images to defect images of steel strips and validated the effectiveness of the synthesized data for data augmentation in YOLOv8. [PERSON] et al. [103] proposed the production-and-elimination GAN (PE-GAN) for high-quality defect sample generation, which guides the learning direction of generative models by simulating the formation of defects on the surface of steel plates.
## 1 Introduction
In addition to receiving the surrounding influence, the emotion of user D at timestamp 3 is affected by [PERSON]'s emotion at timestamp 1 (due to the absence of the emotion of user D's emotion at timestamp 2). These relationships are defined as time-evolving influence. In this study, the second challenge is extracting the time-evolving influence in irregular emotion time series. To address the aforementioned challenges in individual emotion prediction, a surrounding-aware individual emotion prediction model (SAEP) is proposed, which is based on a deep encoder-decoder architecture, adopts an attention mechanism to combine the surrounding influence and time-evolving influence into the emotion prediction task. Specifically, SAEP  
Figure 1: Illustration of the surrounding-aware individual emotion prediction problem. constructs two attention networks: A time-evolving attention network and a surrounding attention network. The time-evolving attention network extracts the time-evolving context from the user's emotion time series. The surrounding attention network obtains the surrounding context from the emotion time series of neighbors. Next, the time-evolving influence and surrounding influence are integrated to predict the user's future emotion. Following are the main contributions of this study:  
* Based on a deep encoder-decoder architecture, a surrounding-aware model is proposed to predict individual emotions in social networks, which takes into account both the influences of time-evolving and social network surrounding. First, the time-evolving and surrounding attention networks are constructed. Then, two attention context vectors are incorporated, time-evolving information and surrounding information, into the emotion prediction task. * Aiming at the problem of irregular time series data, a variant long short-term memory (LSTM) network is introduced as the encoder of the proposed model, to extract the underlying structure in irregular time series data, allowing it to capture individual emotional changes more precisely. To the best of the authors knowledge, there is a lack of research on emotion prediction for irregular time series data based on surrounding influence and deep learning techniques. * Extensive experiments were conducted to validate the proposed model over several baseline methods. The experimental results show that the micro F1 of SAEP can reach 60.20% and 63.33% on Twitter and Microblog datasets, respectively.
## 4 Elaboration of the Optimization Method
### Design Constraints  
In order to perform effective optimization, it is necessary to distinguish between sandwich constructions that are aligned with a specific purpose and those that are not. In other words, design constraints are the limitations that any proposed design must comply with.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variables** & **Value** & **Remark** \\ \hline Number of face sheet layers & \(1\leq N_{l}\leq 15\) & \begin{tabular}{c} Discrete variable, \\ Integer values \\ \end{tabular} \\ Face sheet materials & \begin{tabular}{c} CFRP layer: identified by No. 1 \\ GFRP layer: identified by No. 2 \\ Aluminum layer: identified by No. 3 \\ \end{tabular} & \begin{tabular}{c} Discrete variable, \\ Integer values \\ \end{tabular} \\ possible FRP composite & \(\theta_{FRP}=0^{\circ},90^{\circ},+45^{\circ},-45^{\circ}\) & \
\begin{tabular}{c} Discrete variable \\ \end{tabular} \\ layup orientation & \(\rho_{c}\left[\text{kg}/\text{m}^{3}\right]\) &
\begin{tabular}{c} Discrete as specified in \\ the Table 2 \\ \end{tabular} \\ Core thickness & \(5\leq t_{c}\leq 20\left[\text{mm}\right]\) & Continuous value \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design variables of the optimization.  
In this study, design constraints such as maximal deflection of the structure, strength limitations, and failure criteria for the sandwich structure were used to establish the necessary limits that any proposed design must satisfy.
## 2 Project overview and In-Situ Monitoring
### Tunnel Design and Construction Scheme  
As a large-section loess tunnel, the New Badaling tunnel is a horseshoe-shaped tunnel with a width of 14.78 m, height of 12.49 m and cross-sectional area of 144 m\({}^{2}\). The design parameters of the tunnel support are shown in Figure 4. Three-bench seven-step excavation method (TSEM) was used for excavation. The advance length for each step excavation was about 1.6 m, and the distance between two adjacent I-shaped steel sets along the tunnel longitudinal axis was about 0.8 m. A total of nine stages are required to complete the construction of the whole cross-section. In order to avoid the interference of critical disturbance zones on each other, a series of trailing distances between different faces remained. Drift 1, also called upper bench (UB), was excavated first. Middle bench (MB) was composed of drift 2 and drift 3, with a distance of about 2 m. Lower bench (LB) was composed of drift 4 and drift 5, with a distance of about 2 m. Lengths of UB, MB and LB were 3-5 m, 8-13 m, and 8-13 m, respectively. In the excavation of MB and LB, Drift 2 and Drift 4 were excavated with a distance of 2-3 m ahead of Drift 3 and Drift 5, respectively. The secondary support of the whole cross-section was installed in stage 8 and stage 9. Approximately 23 to 28 days were spent closing the primary support to a complete ring, at a distance of about 35 m behind the leading advance face, i.e., the face of drift 1. The distance between the secondary support and the leading advance face was about 60 meters. Figure 5 demonstrates the specific construction procedure for the project.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Specific Weight** & **Elastic Modulus** & **Poisson’s Ratio** & **Cohesion** & **Friction** \\  & **(kN/m\({}^{3}\))** & **(GPa)** & **Poisson’s Ratio** & **(kPa)** & **Angle(\({}^{\prime}\))** \\ \hline loess & 17.6 & 0.1 & 0.37 & 25 & 27.3 \\ fully weathered tuff & 26.7 & 3.5 & - & 165 & 31.3 \\ strongly weathered tuff & 27.0 & 11.3 & 0.3 & 750 & 40.2 \\ weakly weathered tuff & 27.1 & 33.1 & 0.23 & 1850 & 50.6 \\ \hline \end{tabular}
\end{table}
Table 1: The typical physical and mechanical properties of ground.  
Figure 4: Support parameters of New Badaling tunnel.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Grade I** & **Grade II** & **Grade III** & **Grade IV** & **Grade V** \\
**Value** & **(Very Good)** & **(Good)** & **(Fair)** & **(Poor)** & **(Very Poor)** \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{BQ} & \(>\)550 & 451–550 & 351–450 & 251–350 & \(<\)250 \\ Q & \(>\)40 & 10–40 & 4–10 & 1–4 & \(<\)1 \\ \hline \end{tabular}
\end{table}
Table 2: Relationship between the basic quality (BQ) system and quality (Q) System.
## 4 Results and Discussion
### Data Analysis  
After performing data distribution analysis, the monthly average, minimum, and maximum values were computed. To assess data trends over time, these values were graphically depicted on graphs. The blue color in the graphs represents the average values, while orange represents the minimum values, and the gray color is used for the maximum values.