esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Analysis of Heavily Doped n+
## 4 Conclusions  
In this paper, the properties of an n\({}^{+}\) pocket AJ DG MOSFET were analyzed for low-power improved subthreshold characteristics. The proposed n\({}^{+}\) pocket AJ DG MOSFET has shown a very high I\({}_{\text{ON}}\)/I\({}_{\text{OFF}}\) current ratio of \(\sim\)10\({}^{13}\) with SS of 59 mv/dec and DIBL of 13.9 mV/VA, which shows the device's suitability for low power applications. A cavity region is introduced in the gate oxide region, and its sensitivity is analyzed concerning the change in dielectric constant and charge concentration. Biomolecules like DNA, enzymes, etc., have different dielectric constants and charge concentrations. AJ DGMOSFET with an n\({}^{+}\) pocket region is highly sensitive to changes in dielectric constants as well as charge concentrations and can be used as a biosensor effectively. The device with HfO\({}_{2}\) as the dielectric material performs better.  
Figure 7: **(a)** Graph representing drain current versus gate voltage of the proposed n\({}^{+}\) pocket AJ DG MOSFET for p+ charge concentration in the cavity region; **(b)** Graph representing drain current versus gate voltage of the proposed n\({}^{+}\) pocket AJ DG MOSFET for n\({}^{+}\) charge concentration in the cavity region.  
The structure of the device is simple, and the cost of manufacturing is low. Finally, it can be concluded that this device has a low OFF-state current and high I\({}_{\text{ON}}\)/I\({}_{\text{OFF}}\) ratio providing better performance. It is also less prone to short channel effects. Therefore, this device can be used as a biosensor for detecting different types of diseases at early stages.  
N.M., S.L.T., S.P., devised the project, the main conceptual ideas, design experimentation and writing--first draft preparation; S.P., S.L.T., supervision, data validation and review and editing; S.P., E.H., project administration, software; S.P., technical input for testing and validation of results; S.P., S.L.T., E.H., review and suggestion for improvement. All authors have read and agreed to the published version of the manuscript.  
No source of funding for this research activity.  
Authors would like to thank the Department of Energy Technology, Aalborg University, Esbjerg, Denmark for technical inputs, expertise, transfer knowledge and support.  
The authors declare no conflict of interest.
# Numerical Study on the Bending Performance of Steel-Ribbed Composite Slabs for Substations
The studies mentioned above primarily focus on the unidirectional performance of floor slabs, with relatively few studies addressing composite slabs subjected to bidirectional loading. [PERSON] et al. [18] conducted static load tests on four two-way concrete composite slabs and investigated the stress state at the joints. The results revealed that increasing the lap reinforcement length and cross-sectional height at the joints significantly enhanced the overall integrity and flexural capacity of the composite slabs. [PERSON] et al. [19] carried out bidirectional loading tests on laminated two-way slabs with L-shaped joints, conventional dense-splicing joints with additional reinforcement, and cast-in-place bidirectional slabs. The deflection, reinforcement strain, and crack development of the test slabs, as well as the force transmission mechanism at the joint, were analyzed. [PERSON] et al. [20] conducted stacking tests on dense-splicing composite slabs with steel-bar trusses measuring 3.6 m \(\times\) 3.6 m and supported at four edges to study the ultimate capacity, failure modes, load transfer, and stiffness. The results indicate that the dense-splicing composite slabs with additional overlapped reinforcements crossing the joints can ensure reliable force transmission under bidirectional loading. However, the crack distribution as well as the stiffness is slightly different from the cast-in-place slabs. [PERSON] et al. [21] proposed a new dense-splicing technique for two-way composite slabs using grooves and closed-ring reinforcement. The structural performance of the new type of joint was studied by full-scale tests and found to be basically the same as that of the cast-in-place slab. This study introduces a new type of prestressed concrete composite slab with steel ribs and explores its potential in applications of large-scale prefabricated substation construction. The paper begins by outlining the project background and design scheme of the floor slabs, followed by the finite element modeling of the composite slabs. Subsequently, the unidirectional bending performance of the composite slab along the direction of the prestressed tendons is analyzed and the ultimate capacity is compared with theoretical calculations for validation. After that, double-spliced and triple-spliced composite slabs are analyzed to access their bidirectional performances followed by a parametric analysis to identify the influence of key factors.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 2 Related Work
### Comparison of the German Smart Metering Approach with Implementations in Other Countries  
Implementations of smart metering approaches differ largely between countries. For member states of the European Union, a comparison can be found in [26]. Among other things, implementations differ regarding functionality and interfaces, expected deployment strategies (partial rollout vs. full rollout), the market model (market-driven vs. regulated), the targeted diffusion rates, rollout time frames, or expected costs. The German approach with a dedicated and highly regulated communication gateway (SMGW) in addition to the meters can be seen as rather unique, compared to other countries. In [27], background information on the privacy debate between 2010 and 2016 is given, pointing out critical aspects resulting in Germany's current smart metering architecture with SMGWs. Due to requirements that are defined and verified by the BSI as German authority and due to national regulations, it is adapted to the German market. However, the overall concept, mechanisms and implementation of the SMGW as a gateway securing the communication between LMN, HAN and WAN could be applied to different countries, when countries adapt their national regulations accordingly. For instance, in [28], the Swiss Federal Office of Energy evaluated the appropriateness of different smart metering approaches by defining minimum national requirements and comparing them with implementations in other countries, resulting in a large overlap between the Swiss requirements and the German approach. A broader comparison of national smart metering implementations can be found in the smart metering landscape report by the Austrian Energy Agency in [29]. Among other things, the report categorizes national smart metering implementations regarding their overall strategy and regulatory basics.
## 4 Discussion  
Since the late 1960s, SDF has been utilized for many years worldwide, with concentrations ranging from 10% to 38% [53, 98, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112].  
The Food and Drug Administration approved it in 2014 as a desensitizing agent, and since then, its use and research interest have increased both in the US and, more recently, in Europe [113, 114, 115, 116].  
While its utilization has expanded globally, a comprehensive examination of the existing body of literature is imperative to distill the evidence surrounding its efficacy, safety, non-invasive nature, child-friendly quality in dental care practices, and the nuances of its application in the specific context of molar caries prevention and arrest in children.  
This systematic literature review aims to comprehensively assess and synthesize existing research on the utilization of SDF as an intervention for both preventing and arresting caries in the molars of pediatric patients [117].  
The synthesis of evidence from a series of studies over the last ten years contributed significantly to our understanding of the effectiveness and implications of SDF in preventing and arresting pediatric dental caries. This discussion will be structured around three major themes, each delving into various facets of the research, encompassing its comparative efficacy, clinical performance, application protocols, and broader population-level impact. All studies showed low bias in both the placement and possible systematic errors in evaluating the effect of SDF on caries prevention [118].
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Applications of GAN-Synthesized Images for Brain MRI (RQ 1)
#### 3.1.1 Image Translation
Skip connections of U-Net and GAN loss restore edge information in images [21]. The dosimetric and image-guided radiation therapy (IGRT) method of SCT generation is discussed in [22]. GAN with a ResNet generator and a CNN discriminator creates SCT images from T1-weighted postgadolinium MRI. In [23], a spatial attention-guided generative adversarial network (Attention-GAN) minimizes the spatial difference in SCTs that can deal with the atypical anatomies and outliers in a better way. The framework projects the regions of interest (ROI), and the transformation network performs the domain change. The term attention is the addition of the absolute values in different layers of the discriminator across the channel dimension. CycleGAN is the second most commonly used GAN model in image translation applications. The cycleGAN can work on unpaired data but may introduce inconsistent anatomical features in the generated images. The unsupervised attention guided GAN (UAGGAN) model can work with both paired and unpaired images and be used for bidirectional MR-CT image synthesis. First, the supervised pre-training fine-tunes the network parameters; then, the unsupervised training improves the medical image translation. The combination of WGAN adversarial loss with content loss and L1 assures global consistency in the output image. The UAGGAN achieves satisfactory performance by producing attention masks [24]. In [25], cycleGAN with dense blocks performs two transformation mappings (MRI to CT and CT to MRI) simultaneously. A multi-scale patch-based GAN performs unpaired domain translation and generates 3D medical images of a high resolution. The approach has a low memory requirement where a low-resolution version is generated and later converted into a high-resolution version by patches of constant sizes [26]. Three-dimensional cycleGAN uses inverse transformation and inverse supervision to learn the mapping between the MRI and CT image pairs for proton treatment planning of base-of-skull (BoS) tumors. The dense blocks-based generator explores image patches for textural and structural features [27]. Attenuation correction (AC) needed for a PET image is accomplished by a 3D cycle-GAN, where a 3D U-net generator produces continuous AC maps from Dixon MR Images without MR and CT image registration. The downsampling and upsampling layers in 3D U-net reduce the memory requirements [28]. StarGAN performs image translation among more than one pair of classes. In [29], counterfactual activation generator (CAG) implement image transformation for seven classes. This setting extracts task-sensitive features from brain activations by equating ground truth and real and synthetic images. In [30], high dimensional input maps are translated to high dimensional output maps with the help of Pix2 Pix-cGANs to colorize the tumor region in Intracranial tumor MRI images.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 1 Introduction
Furthermore, we couple it with a method to utilize the per-class queuing system to reserve bandwidth for the transmitted video to optimize QoS/QoE and enhance the overall resource usage. * We apply our methodology by streaming videos of different resolutions and evaluating their quality performance under a network topology experiencing packet loss and congestion. The rest of this paper is organized as follows. Section 2 includes the related work. The proposed video VQoSRR framework is described in Section 3. The experiments and results are presented in Section 4. Finally, the conclusion and future work are shown in Section 5.
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
## 2 Historical Background
These systems had a low efficiency of information transmission. In 1954, in fact, masers, microwave amplification by stimulated emission of radiation, was developed [32]. Four years later, the same team behind masers introduced lasers (Light Amplification by Stimulated Emission of Radiation) as an efficient source of light [33], and these inventions radically increased the efficiency of data transmission [34]. In the mid-1960s, NASA began experimenting with the use of a laser as a communication tool between Earth and space [35]. The first articles on FSO were published in Berlin in 1968 by [PERSON], considered to be the father of FSO [36]. The first commercial systems with a capacity of 100 kb/s to 34 Mb/s were published in 1985. In 1999, lines with a capacity of 100-155 Mb/s were developed, and a little later 622 Mb/s data rates were obtained. Currently, capacities of up to 100 Gb/s are achieved [37; 38]. In the 90s, the wireless revolution began [39].
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 3 Analytical Calculations of Sheath Currents under Fault Conditions
### Constructing Feature Vectors  
The sheath current of an HV cable grounding system contains both amplitude and phase information, and different types of faults can produce different sheath currents. Therefore, this paper detects and diagnoses different fault types by the magnitude and phase of the sheath currents flowing into the first and last ends of the grounding box. In order to make the constructed feature vectors can reflect the sheath current characteristics of the same loop, but also can weaken the influence of load current, minor section length, phase distance, and other factors on the sheath current, therefore, the sheath current amplitude ratio and phase difference of the same loop and the same grounding box is constructed as feature vectors, which can reflect the different operating conditions of the HV cable sheath loop. The corresponding magnitude and phase of the sheath currents flowing into the first and last grounding boxes are shown in Equation (24).  
\[\left\{\begin{array}{l}\dot{I}_{\text{sa1}}=I_{1}\angle\varphi_{1},\ \dot{I}_{\text{sa2}}=I_{2}\angle\varphi_{2},\ \dot{I}_{\text{sa3}}=I_{3}\angle\varphi_{3}\\ \dot{I}_{\text{sa4}}=I_{4}\angle\varphi_{4},\ \dot{I}_{\text{sa5}}=I_{5}\angle\varphi_{5},\ \dot{I}_{\text{sa6}}=I_{6}\angle\varphi_{6}\end{array}\right. \tag{24}\]  
Figure 8: Equivalent circuit diagram of grounding fault of cable joint JA1.  
where \(I_{n}\) and \(\varphi_{n}\) (\(n=1,2,3,4,5,6\)) are the amplitude and phase angle of the sheath current \(i\)san.  
The amplitude ratio and phase difference of the same grounding box sheath current is shown in Equation (25).  
\[\begin{cases}a_{1}\angle b_{1}=\frac{\dot{i}_{\text{s=2}}}{I_{\text{s=1}}}=\frac {I_{2}\angle p_{2}}{I_{1}\angle p_{1}}\\ a_{2}\angle b_{2}=\frac{I_{\text{s=2}}}{I_{\text{s=1}}}=\frac{I_{2}\angle p_{ 2}}{I_{1}\angle p_{1}}\\ a_{3}\angle b_{3}=\frac{I_{\text{s=1}}}{I_{\text{s=4}}}=\frac{I_{5}\angle p_{ 5}}{I_{4}\angle p_{4}}\\ a_{4}\angle b_{4}=\frac{I_{\text{s=4}}}{I_{\text{s=4}}}=\frac{I_{6}\angle p_{ 6}}{I_{4}\angle p_{4}}\end{cases} \tag{25}\]  
The amplitude ratio and phase difference of the sheath currents at the first and last ends of the same circuit are shown in Equation (26).  
\[\begin{cases}a_{5}\angle b_{5}=\frac{\dot{i}_{\text{s=6}}}{I_{\text{s=1}}}=\frac {I_{6}\angle p_{6}}{I_{1}\angle p_{1}}\\ a_{6}\angle b_{6}=\frac{I_{\text{s=1}}}{I_{\text{s=2}}}=\frac{I_{6}\angle p_{ 4}}{I_{2}\angle p_{2}}\\ a_{7}\angle b_{7}=\frac{I_{\text{s=5}}}{I_{\text{s=3}}}=\frac{I_{5}\angle p_{ 5}}{I_{3}\angle p_{3}}\end{cases} \tag{26}\]  
In view of the above, the 14 feature vectors [\(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), \(a_{5}\), \(a_{6}\), \(a_{7}\), \(b_{1}\), \(b_{2}\), \(b_{3}\), \(b_{4}\), \(b_{5}\), \(b_{6}\), \(b_{7}\)] are constructed to comprehensively reflect the operation states of the cable grounding system.
## 5 Conclusions  
The detection and recognition of forest fire risk images is of great significance for the detection and prevention of forest fires. Combined with migration learning, the work in this paper trained a DCNN network, used multiple pre-trained DCNN models to extract the features of forest fire images, used PCA reconstruction technology to convert features into a shared feature subspace, and established the forest fire risk recognition model. According to the experimental results, the main research conclusions of this paper were as follows:  
1. A DCNN network combined with migration learning was constructed and trained using multiple pre-trained DCNN models to extract features from forest fire images and used PCA reconstruction technology to convert features into a shared feature subspace to establish a forest fire risk recognition model. We used 550 flame images to test the fire risk location performance and the segmentation algorithm. The true positive rate was 7.41% and the false positive rate was 4.8%. When verifying the impact of different batch sizes and loss rates on verification accuracy, the loss rate of the DCN_Fire model of 0.5 and the batch size of 50 provided the optimal value for verification accuracy (0.983).
2. Comparing the test results showed that the performance of the improved DCNN model was comparable with that of other models. In terms of processing time and verification accuracy, the DCN_Fire model was considered a better DCNN architecture in fire risk identification. When calculating the training and verification losses of several models, those of the DCN_Fire model were minimized.  
Although the improved DCNN model achieved good results in the detection of forest fire risk, the detection accuracy still warrants improvement. In future research, we will further optimize the network model structure and improve the network performance of the forest fire risk identification model.  
Conceptualization, S.Z. and W.W.; methodology, S.Z., P.G., W.W. and X.Z.; software, S.Z. and X.Z.; validation, S.Z., W.W. and X.Z.; formal analysis, S.Z., P.G. and X.Z.; investigation, S.Z., W.W. and X.Z.; resources, S.Z., W.W. and X.Z.; data curation, X.Z. and P.G.; writing--original draft preparation, S.Z.; writing--review and editing, W.W. and X.Z.; visualization, S.Z. and X.Z.; supervision, W.W., X.Z. and S.Z.; project administration, W.W. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Field R&D Program Project of Guangdong Province, China (Grant No. 2019B020223003), Guangzhou Science and Technology Plan Project Innovation Platform Construction and Sharing (Grant No. 201605030013), Guangdong Laboratory of Lingnan Modern Agriculture Project under Grant NT2021009 and the No. 03 Special Project and the 5G Project of Jiangxi Province under Grant 20212 ABC03A27.  
We would like to thank our anonymous reviewers for their critical comments and suggestions for how to improve our manuscript.  
The authors declare that there are no conflict of interest.
## 2 General Concepts  
As mentioned in the previous section, unpolarized light can be partial or fully polarized by means of reflection. In Figure 1a, unpolarized light is incident on a transparent surface. The reflected beam will be partially polarized (mostly horizontally polarized) for most angles of incidence when the electric field vector is perpendicular to the incidence plane (represented by \"dots\" in the figure)--that is, parallel to the surface.  
For a specific incidence angle, called the polarization angle \(\theta_{P}\) (the reflected and transmitted rays are perpendicular to each other), the reflected light will be fully polarized in a perpendicular direction with respect to the plane of incidence, as shown in Figure 1b. The transmitted or refracted light is partially polarized in a direction parallel to the plane of incidence.  
Using the law of reflection and [PERSON]'s law, that is  
\[\theta_{i}=\theta_{r}, \tag{1}\]  
\[n_{1}\sin\theta_{i}=n_{2}\sin\theta_{r}, \tag{2}\]  
and considering an angle where the reflected angle is completely polarized with its electric field parallel to the surface (the partially polarized refracted beam)--for which \(\theta_{i}\) and \(\theta_{r}\) will be renamed as \(\theta_{P}\), forming \(90^{\circ}\) between the reflected and transmitted beam--the following expressions are obtained:  
\[\theta_{P}+\theta_{R}=90;\qquad\theta_{R}=90-\theta_{P}, \tag{3}\]  
\[n_{1}\sin\theta_{P}=n_{2}\sin(90-\theta_{P}). \tag{4}\]  
By using the identity \(\cos\theta=\sin(90-\theta)\), then  
\[n_{1}\sin\theta_{P}=n_{2}\cos\theta_{P}. \tag{5}\]  
From Equation (5), the expression for \(\theta_{P}\)[12] is  
\[\theta_{P}=\text{atan}\frac{n_{2}}{n_{1}}. \tag{6}\]  
It is known and it can be observed that, by using Equation (5), the refractive index of a material \(n_{2}\) can be calculated if Brewster's angle and \(n_{1}\) are known. At this angle, the intensity of the refracted light is minimum since only s-polarized light (perpendicular to the plane of incidence) is reflected, generally monitored by eye or a light power meter in a laboratory experiment. Figure 2 depicts the general schematic of an experimental setup commonly used to determine \(\theta_{P}\).  
Figure 1: (**a**) Partially and (**b**) fully polarized light by reflection.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 3 Results
### Impact of Loss Function on Defect Detection Accuracy  
Existing GAN networks have limited capability in handling image details, resulting in incorrect reconstruction of non-defective areas. This issue leads to increased dissimilarity errors in images and a decrease in defect segmentation accuracy, potentially causing missed and false defect detection, as illustrated in Figure 8b. In CGAN training, we introduced both the L1 loss function and an enhanced SSIM loss function as the target loss functions. As depicted in Figure 8d, when using only the L1 loss function, the edge regions of defect images are not entirely detected. This might be due to the L1 loss function's capability to perform pixel-wise comparisons, adapting to the complex requirements of reconstructing fabric patterns in images, but it requires an enhancement in image detail reconstruction ability. When using only the improved SSIM loss function, the results, as shown in Figure 8e, indicate that some defect regions are missed. Combining both loss functions can address the aforementioned limitations, as illustrated in Figure 8f. The weight parameter \(\lambda=100\) balances the CGAN loss function with the L1 and SSIM loss functions effectively.  
Figure 7: Comparison chart with and without self-attention mechanism. (**a**) Original defect image; (**b**) Reconstructed image without self-attention mechanism; (**c**) Reconstructed image with self-attention mechanism; (**d**) Detection result without self-attention mechanism; (**e**) Detection result with self-attention mechanism.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 6 Simulation Results  
Four different SE simulation methods were tested, but only two methods will be discussed in their own subsections. The explanations for the conventional WLS state estimator and the modified LSTM-based state estimator are discussed in Sections 6.1 and 6.2, respectively. All the state estimators estimated the voltage magnitude value of the buses in the DC microgrid for one day (or 24 h) in the presence of the voltage fluctuation caused by the load variance.