esa-sceva/satcom-chunk-collection huggingface dataset:
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 3 Materials and Method  
### YOLOv8  
YOLOv8 utilizes a similar backbone to YOLOv5, but with some modifications in the CSPLayer, now referred to as the C2f module. The C2f module, which consists of a two-convolution cross-stage partial bottleneck, combines high-level features with contextual information to enhance detection accuracy. YOLOv8 employs an anchor-free model with decoupled heads to independently handle object-detection, classification, and regression tasks. This design allows each branch to focus on its specific task, leading to improved overall model accuracy. In the output layer of YOLOv8, they use the sigmoid function as the activation function for object scores, indicating the probability of an object being present within the bounding box. They use the softmax function to represent class probabilities, signifying the probability of an object belonging to each possible class.  
For bounding box loss, YOLOv8 uses the CIoU [42] and DFL [43] loss functions, and for classification loss, it employs binary cross-entropy. These loss functions enhance object-detection performance, especially when dealing with smaller objects. For this paper, we selected YOLOv8 as the baseline model, which consists of three key components: the backbone network, the neck network, and the prediction output head. The backbone network is the core part of the YOLOv8 model and is responsible for extracting features from the input RGB color images. The neck network is positioned between the backbone network and the prediction output head. Its primary role is to aggregate and process the features extracted by the backbone network. In YOLOv8, the neck network plays a crucial role in integrating features of different scales. Typically, the neck network adopts a Feature Pyramid Network (FPN) structure, which effectively fuses features of various scales to construct a more comprehensive representation.  
The prediction output head is the topmost part of the YOLOv8 model and is responsible for identifying and locating object categories in the images. The output head usually contains multiple detectors, with each detector responsible for predicting the position and category of objects. In YOLOv8, three sets of detectors are employed, each with a different scale, aiding the model in recognizing objects of various sizes. The network architecture of YOLOv8 is illustrated in Figure 1.
## 2 Problem Statement
### System Model  
We make use of the advantage of the already existing architectural model for VANET [2] i.e., trusted authority, Server, RSUs and vehicles equipped with OBUs (On-Board Unit) and other sensing devices as shown in Figure 2. Due to high cost and commercial aspects of RSU here, we consider the limited number of RSUs. They can be installed in the main streets where many vehicles can pass and get register to the server using V-2-I communication. Further, our proposed system model can be implemented in future vehicular networks e.g., vehicular platoon.  
_Central Cloud Layer CCL:_ is a group of servers, trusted authority (TA) which have massive storage capacity and computational abilities. Central cloud can provides roadside services via V-2-I and V-2-R (vehicle to remote side unit) communication. CCL is responsible for registration of server, RSUs, and vehicles regarding brief behavioral history and profile also with digital certificate that is distributed soon after entering the coverage area.  
_Remote Cloud layer RCL:_ is a set of remote site units RSUs which gives general information and local services to vehicles on road. The main theme of our proposed system model is to enable vehicles to authenticate its neighbor without the central system to reduce extra overhead. After a complete round trip a vehicle's group leader can update the correspondent information to the server via RSU.  
_Vehicle Cloud Layer VCL:_ is treated as local vehicles cloud on the road which can be built as a set of cooperating vehicles in different groups. Vehicles can share their resources via V-2-V communication using onboard modern sensors and computers. If the target vehicle has trust value above the minimum defined threshold then no need to verify its credibility using first and second-hand recommendation. If target vehicle has lower trust value i.e., below the threshold then the requesting vehicle can ask for certificate verification and also take first and second-hand recommendations from neighbor vehicles.
# In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI
## 2 Materials and Methods
### Dynamic Imaging  
For each volunteer, three exams were performed for recording DMRI (2D hybrid contrast enhancement, FOV 200 \(\times\) 200, pixel spacing 0.68/0.68, slice thickness 5 mm, TR = 20 ms, TE = 10 ms, flip angle \(80^{\circ}\), 2.9 s per frame) of the right leg on the two optimal planes. Each exam required two flexions since the G-Scan Brio allows the acquisition on a single plane at a time. To minimize the variations between subsequent acquisitions, DMRI were taken one after the other using the same step velocity. An additional support was introduced to keep the shank fixed in the scanner during the tests while, at the same time, allowing an unconstrained motion at the knee.  
Figure 3: Schematic representation of the registration process to the reconstruction of the knee kinematics from DMRI.  
Figure 4: Reconstruction of knee kinematics from five static scans at different knee flexion angles: patient position for each scan (**top row**) and corresponding segmentation of knee bones (**bottom row**). The bones from non-weight-bearing scan are represented in green, registered on the corresponding bones, as segmented from each weight-bearing MRI, in blue.  
Figure 5: Location of the tested DMRI plane for the optimization of the kinematics reconstruction.  
The first two exams were identical, while during the third, a wooden block was positioned below the left foot to increase the starting flexion angle to the volunteer's maximum. For each repetition, 71 frames were recorded.  
Joint Kinematics Reconstruction: Repeatability of the Exam and Sensitivity to the Initial Registration  
The registration algorithm was applied to the three repetitions of DMRI performed by each volunteer. To minimize the effect of the initial registration, simulations were run from full extension to flexion, and then, the frame order was inverted to simulate extension. Only the extension cycle was considered.  
To test the repeatability of the experimental procedure, the standard deviation among the three repetitions was computed over the common flexion range for each motion component. Rotational and translational repeatability were defined as the mean standard deviation values over all volunteers.  
To test the impact of the initial registration on the final reconstructed motion, the algorithm was run by perturbing the pose of the tibia, fibula, and femur (the three considered as a single rigid complex) first by \(\pm\)5 and then by \(\pm\)10 (mm and \({}^{\circ}\)), on each component, for a total of 1456 combinations around the initial registration proposed by the operator. In this case, simulation from full extension to flexion and then back to extension was also run, and only the extension cycle was considered and compared with the motion obtained without perturbation of the initial pose. Trajectories with rotational MAE below 0.5\({}^{\circ}\) and translational MAE below 0.5 mm were considered not affected by the considered perturbation.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 4 Conclusions  
Facing the need for daily long-term cardiovascular health monitoring, many advanced flexible ECG electrodes have been developed. In this review, we introduce the recent research progress of typical graphene-based ECG electrodes. Their design ideas, structural characteristics, preparation methods, and material properties are analyzed and discussed in detail, and some possible development directions of graphene ECG electrodes are proposed through our work. With the continuous development of graphene ECG electrodes by many researchers, it is believed that graphene ECG electrodes with better electrical conductivity, higher comfort, and better skin adhesion will be proposed, and more intelligent ECG monitoring systems suitable for long-term daily health monitoring will be developed soon. Relevant research and results will play an important role in actively dealing with cardiovascular health problems and an aging society.  
Conceptualization, H.T., Y.Y. and T.-L.R.; methodology, T.-R.C.; software, X.-R.H., A.-Z.Y. and Z.-K.C.; validation, J.-D.X., Y.D., W.-C.S. and Z.-Y.T.; resources, H.T.; writing--original draft preparation, T.-R.C. and H.T.; writing--review and editing, Y.Y. and T.-L.R.; visualization, T.-R.C., D.L., Y.-Z.G. and Y.W.; supervision, T.-L.R.; project administration, H.T., Y.Y. and T.-L.R.; funding acquisition, H.T., Y.Y. and T.-L.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (grant nos. 62022047, 61874065, U20A20168, and 51861145202), the National Key R&D Program (grant no. 2021 YFC3002200 and 2020 VFA0709800), The Beijing Natural Science Foundation (grant no. M2020), the Fok Ying-Tong Education Foundation (grant no. 171051), the Beijing National Research Center for Information Science and Technology Youth Innovation Fund (grant no. BNR2021 RC01007), State Key Laboratory of New Ceramic and Fine Processing of Tsinghua University (grant no. KF202109), and the Research Fund from Beijing Innovation Center for Future Chip, Center for Flexible Electronics Technology of Tsinghua University, Tsinghua-Foshan Innovation Special Fund (TFISF) (2021 THFS0217) and the Independent Research Program of Tsinghua University (grant no. 20193080047).  
Not applicable.  
The authors declare no conflict of interest.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 5 Simulation Results and Discussion
### Comparison of Other VLC Systems with Neural Network  
To evaluate the performance of the proposed PAM-8 transceiver system with neural-network-enabled pre- and post-equalization schemes, the system architecture, training performances, and data transmission of the proposed VLC system were compared with those of previous works, as well as summarizing the characteristics of these works in Table 3. It can be noticed that our proposed VLC transceiver system could achieve the highest data rate with the longest channel length since a hybrid equalization scheme consisting of the passive equalizer, NN-based FFE, and RBF-NN-enabled post-equalization was adopted. In addition, the highest modulation bandwidth in the receiver also contributed to achieving this data rate. The AFEs of our proposed VLC transceiver system were implemented with integrated circuits, which further improved the performance of the system. Our proposed system could also be adopted to both free-space and underwater channels for communication.  
Figure 19: Simulated results of training time vs. data rate with RBFNN or DNN as equalizer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specifications of VLC System** & [12] & [15] & [16] & [17] & [35] & **This Work** \\ \hline \multirow{7}{*}{\begin{tabular}{c} System \\ Architecture \\ \end{tabular} } & Channel Type & Water & Free-Space & Water & Free-Space and & Water \\  & & 1.2 m & 3 m & 1.2 m & 1.2 m & 1.2 m & 3 m and 1.2 m \\  & LED & & & & & \\  & Bandwidth & 200 MHz & / & 200 MHz & 15 MHz & 200 MHz & 50 MHz \\  & PD Bandwidth & 250 MHz & / & 250 MHz & 200 MHz & 250 MHz & 1 GHz \\  & Pre-EQ & Passive EQ & / & Passive EQ & Passive EQ & Passive EQ & Passive EQ \\  & Post-EQ & NN-based EQ & NN-based EQ & NN-based EQ & NN-based EQ & NN-based EQ & NN-based EQ \\  & & Discrete & Discrete & Discrete & Discrete & Discrete & Integrated \\  & & Components & Components & Components & Components & Components & Circuits \\  & Implementation & Experiment & & Matlab & Experiment & Experiment & Matlab \\  & & & & & & Simulation \\ \hline \multirow{3}{*}{\begin{tabular}{c} Specifications \\ of NNs \\ \end{tabular} } & Type of NN & CV-NN & LSTM & TFDN & LSTM & GKDNN & RBF-NN \\  & Iterations & / & / & / & 400 & 2000 & 1200 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Data \\ Transmission \\ \end{tabular} } & Data Rate & 2.85 Gbps & / & 2.85 Gbps & 1.15 Gbps & 1.5 Gbps &
\begin{tabular}{c} 3.6 Gbps and \\ 4.2 Gbps \\ \end{tabular} \\  & Modulation & \multirow{2}{*}{64-QAM} & \multirow{2}{*}{DCO-OFDM} & \multirow{2}{*}{64-QAM} & PAM-7 and & \multirow{2}{*}{PAM-8} \\  & Scheme & & & & & PAM-7 and \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of neural-network-enabled VLC systems.
## 5 Conclusions  
This paper proposed a novel fault diagnosis method for wind turbines with alarms based on word embedding and a Siamese convolutional neural network. To improve diagnosis accuracy, the proposed method collaboratively used labeled alarm sequences and unlabeled alarm sequences. For the unlabeled alarm sequences, the potential relationship among alarms was mined using the Skip-gram model, and _n_-dimensional pretrained alarm vectors were obtained. For the labeled alarm sequences, the discriminative features were extracted to distinguish different alarm sequences by the proposed S-ECNN model, in which the pretrained alarm vectors were optimized and trained. The effectiveness of the proposed method was proved by using the actual alarm data of a wind farm in China. The _accuracy_ of the proposed S-ECNN model for distinguishing different alarm sequences was 86.8%, which was higher than its variants. The result indicated that the collaborative use of the labeled and the unlabeled alarm sequences could effectively improve the distinguishing ability. The _macro-average accuracy_ of the proposed method for fault diagnosing was 97.0%, which was higher than its variants and the existing three methods. The result indicated that the proposed method could effectively improve fault diagnosis accuracy. In addition, the embedding layer introduced in the proposed network provides the possibility of transfer learning, which will be further researched in the following works.  
Figure 12: The comparing indicators with existing methods for fault diagnosis.  
The method proposed in this paper utilizes word embedding to convert alarms into numerical vector representations. Furthermore, alarm sequences consisting of multiple alarms can also be represented in matrix form. In industrial settings, alarm codes continue to increase and are not presented in the form of alarm sequences. Therefore, it is of great research value to investigate how to predict the next alarm code based on historical alarm sequences and thereby forecast the type of failure that wind turbines are likely to experience. Additionally, studying the relationship between the occurrence of alarms and wind turbine power and load is another worthwhile research question. Based on the numerical representation of alarm sequences, alternative time series data mining models such as Bi-LSTM [37] can be used to establish prediction models.  
Conceptualization, methodology, software, validation and writing--original draft preparation, L.W. ([PERSON]); methodology, software, validation, J.Q.; formal analysis, investigation, resources and funding acquisition, Z.Q.; writing--review and editing, F.L., L.W. ([PERSON]), and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 61573046) and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT1203).  
Not applicable.  
The authors declare no conflict of interest.
# Preliminary Investigation of Geopolymer Foams as Coating Materials
## 4 Conclusions  
The main objective of this study was to test the feasibility of using additive spray technology to apply foamed geopolymer coatings to structures and vertical surfaces. The spray application technology eliminates the challenges of protecting surfaces with irregular geometries and, in addition, can increase the applications of geopolymers as an insulating material. Tests were conducted to investigate the possibility of using an air gun with variable nozzles to apply layers of foamed geopolymers. A visual assessment, which consisted of evaluating the adhesion of the applied material to the substrate and the degree of foaming in the applied geopolymer layer on the vertical surface, indicated that the best solution was to use a medium working pressure of the spray nozzle (0.51-0.65 MPa) and to choose a nozzle diameter of \(6\times 10^{-3}\) m. The low-pressure range of the spray nozzle, irrespective of the nozzle diameter, was unsuitable for the application of foamed geopolymer mixtures to the vertical surfaces (0.4-0.5 MPa). The use of high spray nozzle operating pressures was also unsuitable (0.66-0.8 MPa). In this study, basic mixtures for foamed geopolymers were used, which, during the spraying process itself, slightly lost their foaming properties; thus, foaming was less effective than when the foamed geopolymers were obtained via mold casting. The geopolymer foamed with H\({}_{2}\)O\({}_{2}\) at medium spray nozzle operating pressure had the best foaming effect when sprayed onto the vertical surface. The problem, however, was that the geopolymer mass took too long to set in the air, and gravitational force caused some streaking of the freshly applied material. However, the effect is very promising, and the modification of mixes with hydraulic additives or stabilizers can have a positive influence on the foaming process and the setting time of the geopolymer in air. The aluminum powder used to foam the material also showed its potential use in the spray application of foamed geopolymers. The foaming effect was slightly less than for foamed geopolymer with H\({}_{2}\)O\({}_{2}\), but the resulting surface was more regular, and there was no pooling of the material along the vertical wall.  
Conceptualization, M.L. and K.K.; methodology, M.L. and K.K.; validation, M.L. and K.K.; formal analysis, K.K.; investigation, K.K. and K.P.; resources, M.L.; writing--original draft preparation, K.K. and K.P.; writing--review and editing, M.L., B.K. and P.B.; visualization, K.K. and P.B.; supervision, M.L. and B.K.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Center for Research and Development under the Grant: Development of technology for additive production of environmentally friendly and safe insulation materials and ability to accumulate heat based on the alkaline activation of anthropogenic raw materials (LIDER/16/0061/L-11/19/NCBR/2020).  
Not applicable.  
The authors declare no conflict of interest.
# Automatic Correction of Indonesian Grammatical Errors Based on Transformer
## 1 Introduction
The proposed model is the first end-to-end neural-based Indonesian grammatical error correction (IGEC) system that outperformed the previous IGEC systems and has the ability to correct all types of grammatical errors. We summarize the contributions of this paper as follows:  
* We propose a multi-level, semi-supervised method to construct synthetic parallel training data from an out-of-domain corpus;
* We construct a large amount of synthetic data available for open access to address the challenge of a lack of training data;
* We propose an IGEC framework based on multi-head attention networks (Transformer) equipped with a copying mechanism to improve performance;
* The proposed model has the auto-correction ability for all types of errors in Indonesian GEC and outperforms the existing approaches. The code and trained models are available at GitHub ([[https://github.com/[PERSON]/Indonesian-GEC-framework](https://github.com/Almangiri/Indonesian-GEC-framework)]([https://github.com/[PERSON]/Indonesian-GEC-framework](https://github.com/[PERSON]/Indonesian-GEC-framework)) (accessed on 1 May 2022)). The rest of this paper is structured as follows. The most related work is presented in Section 2. Section 3 explains the proposed model and system components. Section 4 presents the experimental details. The conclusions are in Section 7. Figure 1: An example of an automatic grammatical error correction system.
###### Abstract
Recent animal studies have provided strong evidence of the effectiveness of ACDO devices, demonstrating improved histological and radiographic results in bone tissue samples compared to manual methods [20; 24; 25]. The utilization of continuous DF in DO protocols not only accelerates the bone healing process but also enhances the quality of osteogenesis and regenerated tissue [15; 19; 26]. Three major groups of DO devices have been developed thus far: motor-driven devices, hydraulic devices, and hybrid mechanisms. Motor-driven distractors are a category of distractors in which an electrical motor is used to rotate and generate a specific amount of torque which is transited to a linear pushing force using a mechanical mechanism. The generated pushing force (DF) is transferred to the moving BS using mechanical fixtures. It can be seen in developed motor-driven systems that the need for a power supply, as well as the force transition mechanism are the main reasons for the big size of the distractor [27; 28; 29; 30; 31; 32; 33; 34; 35]. In hydraulic distractors, a miniature hydraulic system executes a linear pushing force at the end effector fixed to the BS. In hydraulic systems, there is a need for a power supply and hydraulic components to generate a continuous DF, which makes the distractor big [18; 19; 20; 36; 37; 38]. Recently, hybrid ACDO devices have been designed and developed, in which a combination of motor-driven and hydraulic mechanisms is used to generate a continuous DF and transfer it to the BS [12]. The hybrid distractors could successfully solve the limitations of motor-driven and hydraulic distractors in terms of force generation and transition mechanism.