esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Method
### The Adam-SAM-TCN Remaining Useful Life Prediction Model  
Figure 4 shows the overall structure of the Adam SAM TCN prediction model. The specific process of the RUL prediction method is as follows:  
1. From the 21-dimension raw data of datasets FD001 to FD004, 14 dimensions showing significant degradation trends were selected. These data were normalized and labeled with RUL, and initial values for sliding windows were set.
2. The SAM attention mechanism was introduced into the TCN to assign higher weights to input data that have a greater impact on the engine's remaining useful life, thereby obtaining new feature data. We input these new features into the TCN network for training.
3. After initializing the parameters of the Adam optimizer, the parameters of the constructed SAM-TCN network were optimized to obtain the optimal parameters.  
Figure 3: Model structure diagram of self-attention mechanism and temporal convolutional network.  
Utilizing the optimized Adam-SAM-TCN prediction model, the RUL prediction for the turbofan engine was conducted on the four datasets. The predicted results were analyzed and evaluated based on evaluation metrics.  
The parameter settings of the proposed Adam-SAM-TCN neural network prediction model are shown in Table 1. Here, \(W\) represents the sliding window size, in denotes the number of input channels, \(out\) indicates the number of channels produced by convolution, \(stride\) represents the step size, \(Softmax\) and \(ReLU\) are activation functions, \(kernel\_size\) denotes the size of the convolutional kernel, and \(padding\) refers to the padding value on both sides of the input.
## 2 Methodology
### Phase 3. Land Cover/Use Change Model
#### 2.4.1 Factors  
Two morphological variables were considered: proximity to the beach and vehicular traffic routes (Figure 2). The former was chosen given the considerable number of cities where the coastal edges stand out as places where deindustrialization has gone hand in hand with touristification and gentrification [37]. The choice of the second variable was based on studies such as [38], where the availability of transport infrastructure alongside sites has been one of the factors considered in analyzing their potential to attract investment projects.  
Three normative instruments will be studied as categorical variables. The first is the Historical Monument (HM) category. This designation is decrered by the National Monuments Council, a national-level state agency. According to Law 17.288, owners are prohibited from demolishing these buildings, and the agency's authorization is required for any intervention. In the case of Bellavista, the factory obtained the HM category in 2017. The second instrument is the designation of a Historical Conservation Area (HCA), defined in the Communal Regulatory Plan and an initiative of the Municipality. In the case of Bellavista, an HCA, which includes several housing complexes, was designated in 2008. In contrast to these two heritage preservation instruments, the third planning instrument aims to encourage high-rise real estate development. This is the Coastal Edge Section Plan (CES) which changed the historic factory land use to residential use and allowed high-rise construction (Figure 3).  
Figure 3: Categorical variables where the variables’ location is indicated in red: Historical Monument (HM), Coastal Edge Section Plan (CES) and Historical Conservation Area (HCA).  
Figure 2: Distance variables. On the (**left**) is the distance to the historical vehicle traffic route and on the (**right**) is the distance to the beach. Bluer equals closer and redder farther away.
## 1 Introduction
### Diffusion Tensor Imaging
An example is using the precentral gyrus as a seed point for the corticospinal tracts, as is contains the primary motor cortex. Way points can additionally be used, which only includes tracts that pass through them from the seed point. An example for the corticospinal tract could be the cerebral peduncles. A limitation of tractography is that when the voxel contains multiple WM tracts with different orientations, then the mean direction of the two tracts may be taken or a smaller tract can be lost, as only the overall diffusion direction is taken. This is a particular issue when two tracts cross or \"kiss\" in the same voxel and may lead to false positive and false negative tracts. There are several techniques that attempt to model multiple WM tracts in one voxel, all of which require a larger number of directions [18]. The main drawback of these methods is that image acquisition and processing will take longer, but the sequence acquisitions and the processing software are continuously improved, and with more advanced hardware and software, these advanced acquisitions and processing have become clinically feasible.
# Can Biochar Improve the Sustainability of Animal Production?
## 4 In-House Applications
No statistically significant trends were observed for the feed conversion ratio. Studies have also taken place using direct biochar application to swine manure. [PERSON] et al. [96] assessed the impact of a combination of plant material and animal waste biochar on the sorption capacity of 15 targeted VOCs when applied to fresh swine manure. Manure-based biochars proved to be poor sorbents showing zero adsorption for the primary VOCs of interest, dimethyl disulfide and dimethyl trisulfide. On the contrary, plant-based biochar had significant adsorption, specifically oak-based biochar produced at 500 \({}^{\circ}\)C. Overall, the volatile matter, fixed carbon, ash content, and pH did not affect the adsorption capacity. Similar positive results were demonstrated by [PERSON] et al. [97] using manure-based biochar for the adsorption of VOCs in fresh cattle manure. Significant reductions in sulfur- and oxygen-containing compounds, including thiols, sulfides, alcohols, and ketones, were observed. Most notably were the 98.5% removal of 2,4-dithiapentane, 90% removal of p-Cresol, and 100% removal of phenol. Figure 4 shows the GC-MS abundance of the analyzed VOCs with an order of magnitude difference between the control and biochar-treated manure. The high adsorption capacity was attributed to the high porosity and surface area; however, it is unclear whether the biochar was modified with any alkali or acid treatments to enhance its performance. Several of these studies demonstrated applications of biochar on fresh manure sources simulating the act of incorporating biochar directly in animal houses. [PERSON] et al. [94] were the first to test biochar in an active house with live animals. Further research is needed using live animal trials in house because even the short-term storage of litter or other waste samples can impact the odor properties of the material [98]. Additionally, studies evaluating biochar dosage, application form (powder vs. as-is), and application frequency or material regeneration require further investigation to become more accepted for on-farm use.
## 1 Introduction
Hence, we utilized the STRA model which is a spatiotemporal-based model that leverages these features by obtaining the relationships among objects in the input sequence of the frames, which strengthens the learned features of the normal videos' events and situations. This allows the proposed framework to strongly reconstruct normal frames and detect the poorly reconstructed frames as anomalous. Moreover, to tackle the problem of detection accuracy degradation when training the vision transformers on small and medium-sized datasets, an advanced training strategy was applied by dually combining the datasets and grouping all of them to form various synthetic heterogeneous datasets, then training the proposed model on the datasets individually and on the different combinations that improved anomaly detection accuracy for the models trained on the combined datasets. In addition, two different up-sampling approaches are introduced to enlarge the size of the smaller datasets, i.e., the UCSD-Ped2 and CHUCK Avenue. This strategy proved the robustness and effectiveness of the introduced spatiotemporal ViT-based model in identifying anomalies in larger heterogeneous video anomaly datasets captured in different environmental and lighting conditions and varied resolution and quality, which is a step towards developing a real-time video anomaly detection multi-modal surveillance system. The contributions of this work are summarized below:  
1. An unsupervised framework for video anomaly detection-based ViT transformer and spatiotemporal block using an encoder-decoder architecture to address and effectively detect diverse video abnormalities;
2. Proposing a hybrid deep learning framework which is, to the best of our knowledge, the first one to combine the ViT model with the convolutional STR attention block for video anomaly detection;
3. Adopting a different training strategy by creating different combinations of the utilized datasets to improve the performance of the proposed model for identifying anomalies in videos, despite the diverse environmental difficulties, lighting settings, and video quality of these datasets, towards developing a video anomaly detection multi-modal surveillance system;
4. Conducting a comprehensive evaluation using publicly available video anomaly detection datasets for the introduced framework shows that when compared to cutting-edge methods, the proposed approach produces outstanding results. The rest of this paper is structured as follows: Section 2 covers related VAD work, while Section 3 provides background information about the underlying used techniques. Details about the proposed model are provided in Section 4, while the experiments usingthe introduced model, their findings on the datasets, and the discussion are shown in Section 5. Finally, the conclusion is in Section 6.
# Special Issue on Ultrasonic Modeling for Non-Destructive Testing
## 2 The Feasibility of Using Innovative NDT Methods on Complex and Various Materials  
Ultrasonic NDT methods can investigate different kinds of material properties (mechanical, chemical, physical, biological, etc.) with various physical states/compositions (e.g., solid, liquid, heterogeneous [3], inhomogeneous, complex, and moving media). Ultrasonic NDT is broadly used on metals, plastics, composites and ceramics. Ultrasonic testing has even been used on wooden elements using tomography or acoustic emission [4] and fabrics [5].  
In this Special Issue, the first paper from [PERSON], [PERSON] et al. reviews the efficiency of two classical NDT techniques in detecting embedded FRP Reinforcements: Ground-Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU). GPR can detect GFRP (Glass-Fiber Reinforced Plastic) bars/strands and CFRP (Carbon-FRP) strands to some extent, with a detectability potential increasing with the emission center frequency, whereas PAU can only identify GFRP and CFRP strands.  
[PERSON] and [PERSON] proposed a non-destructive method based on the Southwell procedure that considers the temperature effect to evaluate the critical load, critical thickness, and service life of internally corroded shells under external pressure. The technique appears more practical than other methods, and its accuracy suits engineering applications.  
It is also important to efficiently evaluate the repair of industrial structures. [PERSON] and [PERSON] demonstrated the utility of using embedded PZT transducers to detect damage along the bond line and on the surface of a composite repair patch. The fabrication of the PZT sensors was validated using electro-mechanical impedance results.  
Noise reduction in experimental data acquired with an ultrasonic system is also challenging. [PERSON] et al. used the Hammerstein model identification technique based on swept sine excitation signals to model nonlinear systems. The robustness of the model parameter estimation in the presence of measured noise was evaluated, and a technical solution to moderate the noise effects and improve the model parameters estimation was proposed.
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 6 Conclusions  
This paper proposed a Dual-stream network using a Gabor filter image and gray-scale inspection image as input to detect stain defects. The Gabor filter image was selected according to the IDM score based on the GLCM and used as the input of the Dual-stream network for stain defect classification.  
As a result of the experiment, the Gabor filter method was more accurate than the method without using it, and the single-stream structure in the MT dataset and the dual-stream structure in the CCM dataset had higher accuracy. Through this, the performance of single and dual-stream structures can be different depending on the dataset's characteristics (background, degree of stain). In addition, it was confirmed that selecting N Gabor filter images based on the IDM value is more accurate than using all eight images without selecting them. Through this, it was confirmed that among the Gabor filter images, there are images with important information and other images with lower accuracy due to noise. In addition, it was confirmed that the stream in which the Gabor image was input by outputting the CAM image finds stain.  
The disadvantage of the proposed method is that the method for selecting N Gabor filter images depends on the dataset. Although it was confirmed that the method using Gabor filter images is more accurate than the method without, the value of selecting N Gabor filter images according to the dataset must be directly compared with learning and evaluation. It would be nice always to find a good method, but you have to find N chapters while comparing yourself.  
Developing a method to calculate optimized Gabor filter parameters for defect classification of datasets through deep learning without using static Gabor filter parameters is planned as a future study. In addition, a study on the structure of a CNN that effectively detects stain defects will be conducted.  
Conceptualization, M.-H.H. and T.-H.P.; methodology, M.-H.H.; software, M.-H.H.; validation, M.-H.H., Y.-G.K. and T.-H.P.; formal analysis, Y.-G.K.; investigation, M.-H.H.; resources, M.-H.H.; data curation, M.-H.H.; writing--original draft preparation, Y.-G.K.; writing--review and editing, Y.-G.K.; visualization, M.-H.H.; supervision, T.-H.P.; project administration, T.-H.P.; funding acquisition, T.-H.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2023-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation). This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00795, Development of Moire-Pattern Type 3D Camera System for AI Based Analysis).  
Not applicable.  
Compact Camera Module Dataset is the company's data. Since it is personal information, it cannot be disclosed.  
The authors declare no conflict of interest.
# Analysis of AMI Communication Methods in Various Field Environments
## 2 Power Line Communication for AMI
### Linear Regression Analysis for the Received Power  
The broadband channel model in [33] describes that, when there is a single path and the length of the line is shorter than 200 m, the attenuation factor \(A(f,d)\) at frequency \(f\) and distance \(d\) can be simplified as  
\[A(f,d)=\left(0.0176-3.2587\times 10^{-6}\cdot f^{0.7}\right)\cdot d\ \ \ (\text{dB}). \tag{7}\]  
Note that the attenuation factor of (7) depends on both frequency and distance as shown in Figure 5. Let \(P_{r}(f_{k},d)\) denote the power spectrum of the received PLC signal at distance \(d\). The power  
Figure 6: Magnitude response \(|Hc(f_{k})|\) through the power line and receiver coupler from (3).  
Figure 7: Spectra of the received and noise signals. (**a**) Signal power spectrum \(P_{z}(f_{k})\) of the received signal \(z(t)\) from (4). (**b**) Noise power spectrum \(P_{n}(f_{k})\).  
spectrum \(P_{r}(f_{k},d)\) can then be given as \(P_{r}(f_{k},d)\approx A(f_{k},d)+P_{y}(f_{k})\). In a similar way to (5), we can obtain \(P_{R}(d)\), which denotes the received power with respect to the distance. Figure 8 shows an obtained curve of \(P_{R}(d)\).  
From the result of Figure 8, we can observe that the channel attenuation with respect to the path length is very close to a straight line. This result is obtained from a simplified power line model with few branches. However, for practical low-voltage power systems, a single power line may have more branches and electrical loads than the simple model case. The received power is obtained by measuring the channel response. When measuring the channel frequency response, the responses can be corrupted due to impulsive noise. Thus, the field measurement data for received PLC signal power appears in a somewhat scattered form rather than a straight line with respect to the distance.  
To build a model for a relationship between the distance and the received power from the measurement results, we conduct a regression analysis [34]. The regression analysis is a statistical method to examine the relationship between two or more variables. In this paper, we use the linear regression analysis because the theoretical relationship between the distance and the received power is close to an affine function. Here, from a linear regression model, the received power \(P_{R}(d)\) can be expressed as follows.  
\[P_{R}(d)=\alpha\cdot d+\beta\ \ \ \text{(dBm)}, \tag{8}\]  
where \(\alpha\) (dBm/m) and \(\beta\) (dBm) are regression coefficients. Here, we call \(\alpha\) and \(\beta\) the attenuation rate (AR) and the received power at 0 m, respectively. We find the coefficients \(\alpha\) and \(\beta\) by minimizing a mean squared error between the measurements and the corresponding values on the straight line. Thus, the equation obtained by a linear regression analysis gives an optimal straight line in the sense of minimum mean squared error for a PLC model.
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
## 2 System Model
\(T_{S}(\psi)\) represents the optical filter gain of the LED, \(g(\psi)\) is the concentrator gain of the LED, and the field of view (FOV) of the PD can be represented by \(\Psi_{C}\)[1]. For the dimmable VLC systems, there are two crucial constraints. The first constraint is the peak power constraint. The LEDs utilized for the VLC system have the optimal operating range due to the characteristics of nonlinear. We should also take the eye safety of the users into account [18]. Therefore, the peak power constraint is necessary, and it can be expressed as \(0\leq x\leq P_{peak}\), where \(P_{peak}\) is the peak power. In this paper, we can normalize the peak power without loss of generality, which can be expressed as \(P_{peak}=1\). So the normalized power limitation is  
\[0\leq x\leq 1. \tag{3}\]  
The other constraint is the dimming control constraint. In essence, the dimming control function of the peak power limited VLC systems is to adjust the average power when the peak power is fixed. The dimming factor is the ratio of the average power to peak power. We adjust the average power to meet the requirements of the users by communication technologies [9]. Therefore, the dimming control constraint can be expressed as  
\[\bar{P}=E(\mathbf{x})=\gamma P_{peak}, \tag{4}\]  
where \(\gamma\) denotes the dimming factor of the dimmable VLC systems with the range \(\gamma\in(0,1)\).
## 7 Simulation
### Simulation and Analysis of Trajectory Tracking  
In order to demonstrate the control performance of the explicit-time sliding mode controller proposed in for the continuum space robot, trajectory tracking control simulations were conducted, here considering the bounded uncertainty \(\mathbf{D}\) of the system \(\mathbf{D}=20(\sin t+1\times rand)\). The parameters of the proposed controller are \(T_{c}=2.5\), \(m=0.3\), and \(e_{c}=0.05\), \(s_{c}=1  
In the comparative experiments, predefined-time [22], finite-time [27], and conventional asymptotic sliding mode controller were selected for comparison.  
Line a represents the proposed explicit-time controller, line b represents the predefined-time controller, line c represents the finite-time controller, and line d represents the conventional sliding mode controller.  
The sliding mode values are depicted in Figure 16. The proposed method facilitates rapid error convergence to zero, and the convergence time is within the explicit upper bound of time. The convergence time of the predefined time is shorter than the method proposed, but the error is higher than the proposed method. The finite-time method demonstrates quick convergence at the beginning but maintains minor oscillations in the later stages. The error of the conventional sliding mode controller is relatively stable, but due to its asymptotic convergence the error is noticeably higher than that of the proposed method. Corresponding control signals are illustrated in Figure 17, and their analysis is similar to the sliding mode analysis, showing that the proposed method yields smaller and smoother control signals.  
The stability results of the proposed trajectory tracking control are shown in Figures 18 and 19, with errors converging to zero within the explicit time.  
Both the convergence time of the explicit time and the predefined time are less than the ideal convergence time. Although both can ensure convergence time, the explicit time controller can significantly reduce control inputs, thereby decreasing the damage of the controller to the drive mechanism.  
Figure 16: The values of the sliding mode function (synthesis of 6 sliding mode functions).  
Figure 17: The absolute values of the control signals \(u_{1}\).  
In summary, the proposed trajectory tracking control method demonstrates high accuracy and smoother control inputs.