esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 4 Conclusions  
To ensure the inspected fruits are free from defects, images including the whole surface of fruit are necessary for defect detection. Although many efforts have been made by investigators, current solutions provide advantages as well as disadvantages, and a thoroughgoing solution is still not available. Furthermore, most of the whole surface detection methods were developed for area scan imaging systems. The studies about how to inspect the whole surface of the fruits by using line scan imaging system such as a push broom hyperspectral camera are limited. In this study, without using mirrors or multiple cameras, a simple and reliable hyperspectral imaging system for scanning rotating globe-like object was developed. The rotating speed of object could be precisely controlled by using a rotation stage thus the bounding or wobbling of the fruit during rotation on roller could be eliminated. Stable rotation of object is important for line scan imaging systems to obtain high quality images of rotating object. By using this system, about 95% of the whole jujube surface area could be observed in one scan pass. A robotic arm with suction pads could be easily integrated into this imaging system and the second image of the jujube under inspection could be captured after rotating the rotation axis of the jujube about horizontal line 90 degree. The area of whole surface can be covered by these two images and used for defect classification. The comparison of classification accuracy of SVM and ANN models trained by using HSI data obtained by linear and rotation scan respectively showed that the models trained with the use of rotation scan data had higher accuracy than those trained with the use of linear scan data. This line scan HSI system for rotating objects and the defect classification models can be integrated into current automatic jujube grading systems which grades jujubes by weight, and has the potential to be used for the surface defect detection of other fruits, such as peach, pear, and apple. In order to promote the use of hyperspectral imaging for the defect detection of fruits with a glossy surface, further illumination optimization is recommended to reduce the glare.  
Q.T.P., experimental setup, software implementation, data collection and manuscript preparation; N.-S.L., total planning, experimental setup design, software design, manuscript revise and correspondence. All authors have read and agreed to the published version of the manuscript. This work was supported by the Council of Agriculture (Taiwan) (Grant Number: 109 AS-11.3.2-ST-aA). The authors declare no conflict of interest.
## 1 Introduction
We showed that the induction of phage lytic development and the Stx toxin production was also inhibited by the action of ITC [19; 20]. Moreover, the toxicity of bacterial lysates treated with ITC against human and simian cell cultures was significantly reduced [19; 20]. The molecular mechanism underlying the antibacterial effect of phytochemicals involves the global stress response, the stringent response. Its alarmones, ppGpp and ppPGpp, accumulate during various stress conditions and nutrient limitation, leading to remodeling of the gene expression pattern by direct and indirect action on the transcription machinery [21]. The stringent response is widespread among bacteria and both _E. coli_ and _S. dysenteriae_, belonging to the Enterobacteriaceae family, can synthesize (p)ppGpp via two synthetases, RelA and SpoT. However, previously we presented evidence that the antibacterial effect of ITC is not always related to the stringent response induction, e.g., in _Bacillus subtilis_, and could be based on the disruption of the integrity of the cellular membrane integrity [22]. Nevertheless, we showed that ITC could inhibit the growth of several bacterial pathogens, including _Klebsiella pneumoniae_ and _Staphylococcus aureus_. Recently, we also presented evidence for the antibacterial action of ITCs against another toxin-producing bacterium, _Vibrio cholerae_[23]. Developing a new possible line of infection treatment is important, especially nowadays, when multidrug-resistant bacterial strains emerge and when designing new antibiotics is a long process. In this work, we ask whether the growth of a human pathogen bearing the Stx encoding genes, _S. dysenteriae_, is affected by two representative ITC, sulforaphane (SFN) and phenethyl ITC (PEITC). We show that these two compounds can effectively inhibit bacterial growth, toxin gene expression and toxicity against eukaryotic cells. Furthermore, we presented the SFN efficient antihigellosis effect in the surrogate _Galeria mellonella_ larvae model based on the enhanced phagocytosis of bacterial cells.
## 4 Results: The Usage of CARIMO
### Usage Data Logging for CARIMO
#### 4.2.5 Implementation of the Data Logging Procedures  
Integrating Piwik for usage data logging into CARIMO enabled the logging of: (i) the starting and stopping of the CARIMO activity, (ii) changes in the content (e.g., for Android displaying and removing fragments), and (iii) every interaction with user interface components, such as the use of buttons, input fields, or check boxes.  
In alignment with our research interests, usage measurement for CARIMO had to provide information on the characteristics of the users and the frequency of their use of the CARIMO app and its features. Thus, we implemented the logging of: (i) a unique user identification number (UUID) (who); (ii) type of interaction, such as click, touch, or scroll (what); (iii) UI component that was interacted with (where); and (iv) date and time of the interaction (when). On this basis, further information, such as how often the user accessed the feature, could be derived. The interaction was identified using a unique page/view identifier (e.g., \"/main\" for the home screen) combined with a unique user interface component identifier (e.g., \"/main/btnAdvice\" for the tip of the week button in the home screen). Thus, clicking somewhere on the screen resulted into no further action and was not recorded.
## 3 Method
### Model Structure  
The structure of our AGD detection method is shown in Figure 2. Our method is divided into 3 parts: data preprocessing, feature extraction, and feature fusion and classification.  
First, during data preprocessing, the TLD is removed from the domain name, and the remaining sections are mainly used as the basic unit for classification. We use Word-ninja [46] for meaningful word segmentation and use n-gram algorithms to construct n-gram sequences for the domain name without TLD. Here, we construct 1-gram and 3-gram sequences, as discussed in Section 3.4.  
The next step is feature extraction. For the extraction of meaningful word segmentation features, we first calculate the context-sensitive word embedding features using  
Figure 1: Overall concept of the detection method.  
ELMo [39] and summarize the word features to obtain 128-dimensional word distribution features, as discussed in Section 3.3. Next, based on the domain name and the segmented words, we extract 11-dimensional statistical features and transfer them into 8-dimensional statistical features, as discussed in Section 3.5. For n-gram sequence feature extraction, based on the results of 1-gram and 3-gram sequences, we use the LSTM model to calculate the 128-dimensional 1-gram and 3-gram sequence features, respectively, as discussed in Section 3.4. Finally, the four parts of features are concatenated, and 392-dimensional features (128 + 8 + 128 * 2) are obtained.  
In the feature fusion and classification step, we use two fully-connected layers to fuse the 4 features and use the sigmoid activation function to calculate the classification result, which is a value in the interval [0, 1]. When the value is greater than 0.5, the domain name is classified as an AGD, and when the value is less than 0.5, the domain name is classified as a benign domain name.
# Investigating the Reliability and Dynamic Response of Fully 3D-Printed Thermistors
###### Abstract
Among various applications, printed thermistors are particularly promising due to their simple structure, fast and accurate thermal response, and wide sensing range [21; 22; 23]. A typical printed thermistor consists of three main layers: a substrate, a pair of electrodes, and a thermosensitive (active) layer [24; 25]. The substrate serves as the mechanical support for the device and is expected to have high thermal stability and compatibility with printing processes [26]. The electrodes, commonly made from conductive materials such as silver or carbon-based inks, are patterned onto the substrate to create conductive paths, forming an electrical interface that enables current to pass through the thermosensitive layer [27]. The active layer, also known as the thermosensitive layer, is the key functional component, whose electrical resistance varies with temperature thereby allowing the device to detect and quantify thermal changes based on resistance measurements across the electrode pair [28]. Among various thermosensitive materials, poly(3,4-ethylenedioxythiophene):poly(4 styrenesulfonate) (PEDOT:PSS) is commonly preferred due to its advantages such as low-cost processing, high conductivity, improved wettability, and compatibility with PE technology [29; 30; 31]. In the current literature, PEDOT:PSS is demonstrated to be printed generally on traditional substrates such as polyethylene terephthalate [32; 33], and polyimide [34; 35]. However, only a limited number of studies have reported the utilization of 3D-printed substrates, thereby enabling a fully printed thermistor structure encompassing the substrate, electrode pair, and thermosensitive layer. For example, a fully 3D-printed multi-material thermistor was recently developed by micro-dispensing silver (Ag) and PEDOT:PSS onto an FDM-manufactured polycarbonate (PC) substrate [8] due to the increasing use of PC material in the FDM process [36]. Despite these advancements, there remains a lack of comprehensive understanding regarding the measurement capability, dynamic performance, and reliability of such fully 3D-printed multi-material smart structures, especially given the use of unconventional substrates, i.e., 3D printed substrates. Addressing these knowledge gaps is pivotal for unlocking new frontiers in functional 3D-printed technologies, revealing their full potential in the multi-material 3D printing domain. This study, therefore, investigates the measurement capability, dynamic behavior, and mechanical reliability of 3D-printed multi-material thermistors fabricated via FDM and micro-dispensing technologies. By examining the performance and reliability of these devices, this research contributes to the expanding knowledge base on multi-material 3D-printed smart structures and provides valuable insights into their practical applications in the areas of 3D printing, such as smart textiles, health monitoring, and aerospace engineering.
# Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains
## 2 Algorithms
### Algorithms for Computational Fluid Dynamics (CFD) and Validation  
As one goal of the whole optimization process, the drag of the whole train has to be evaluated, which is performed by CFD analysis. CFD accuracy directly affects the construction of the surrogate model and efficiency of optimization algorithm. In this paper, the speed of high-speed train is 300 km\(\cdot\)h\({}^{-1}\). Under this condition, air compressibility has to be considered. Therefore, the steady compressible Reynolds-averaged Navier-Stokes equations [22] based on the finite volume method are used to predict the aerodynamic drag. The \(k\)-\(\omega\) SST model [23] is selected as the turbulence model. The standard wall functions are used near the wall so that the accuracy of the CFD results could be ensured with a limited amount of mesh.  
To validate the CFD algorithms, and to obtain a reasonable mesh configuration for aerodynamic simulation of high-speed trains, the authors performed a comparison study with wind tunnel experimental results. The experiments were performed in China Aerodynamics R&D Center. The wind tunnel model is a 1:8 scaled train model, with the bogies and inter-coach spacing included, as shown in Figure 2a. The velocity of the air flow is 60 m/s. The computational domain is the same as the tunnel walls. Due to the complex geometry, local densification of the mesh is performed, and the total number of grids is about 21.7 million. The mesh configuration is shown in Figure 2b.  
Table 3 shows the comparison of aerodynamic coefficients between experimental results and numerical results. It can be seen that the aerodynamic coefficient error for each car are all within 5%, which is acceptable for engineering application, indicating that the CFD algorithms and mesh configuration for the simulation are both feasible for aerodynamic drag evaluation of high-speed trains, and will be utilized in the present study.
## 6 Conclusions  
In this paper, we introduce SliceSamp, an effective downsampling method that enhances model performance while minimizing computational latency and memory requirements. To reconstruct and align high-resolution feature maps, we also developed SliceUpsamp for upsampling, employing inverted slicing and depthwise separable convolution. We validated the effectiveness of SliceSamp across multiple datasets, including ImageNet-1K, COCO, VOC, and ADE20K. Extensive experiments demonstrate SliceSamp's superior performance across various computer vision tasks compared to other classical downsampling methods. Compared with the baseline model, SliceSamp improves Top-1 by 1.3% and Top-5 by 0.87% on the image classification task, maintains a similar accuracy while reducing the number of parameters by 17.17% and FLOPs by 14.53% on the object detection task, and improves mIoU by 2.58% and mAcc by 3.64% on the semantic segmentation task. In addition, ablation studies and a Grad-CAM analysis show that integrating Slicing and  
Figure 6: Validation accuracy and loss on the VOC dataset during model training.  
DSConv can provide a qualitative leap in model performance compared to the use of a single element. SliceSamp seamlessly integrates into various models like ResNet, YOLO, and Transformer. We firmly believe that SliceSamp represents a superior and promising alternative to widely used downsampling methods.  
Conceptualization, M.W.; Methodology, M.W.; Validation, L.H. and M.W.; Investigation, L.H.; Resources, L.H.; Data curation, L.H.; Writing--original draft, M.W.; Writing--review & editing, L.H. and M.W.; Visualization, M.W.; Project administration, L.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
In this study, we used four open-source datasets. They are available at: [[https://image-net.org/challenges/LSVRC/index.php](https://image-net.org/challenges/LSVRC/index.php)]([https://image-net.org/challenges/LSVRC/index.php](https://image-net.org/challenges/LSVRC/index.php)) (accessed on 20 October 2023), [[https://cocodataset.org/#download](https://cocodataset.org/#download)]([https://cocodataset.org/#download](https://cocodataset.org/#download)) (accessed on 20 October 2023), [[https://pireddie.com/projects/pascal-voc-dataset-mirror](https://pireddie.com/projects/pascal-voc-dataset-mirror)]([https://pireddie.com/projects/pascal-voc-dataset-mirror](https://pireddie.com/projects/pascal-voc-dataset-mirror)) (accessed on 20 October 2023), [[https://groups.csail.mit.edu/vision/datasets/ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K)]([https://groups.csail.mit.edu/vision/datasets/ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K)) (accessed on 20 October 2023).  
We are very grateful for the support of benchmark datasets, including ImageNet-1K, COCO, VOC, and ADE20K, and open-source codes, including ResNet, YOLOv5, and Swin Transformer.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Optical Characteristics of the Color Camera  
Color cameras are divided into single-chip color cameras and three-chip color cameras. A color camera has red, green, and blue color channels. The single-chip color camera uses a Bayer filter to show one of the three colors, and the camera' processing unit performs spatial color interpolation to obtain the other two colors. The three-chip color camera has three semiconductor devices, each of which corresponds to a primary color. A prism can separate the primary colors of received light. The first type is significantly cheaper, and the second one provides better-quality captured color images [15]. It is common that in color cameras, the spectra of red, green, and blue channels are always made to overlap so that there are no color-blind regions in the spectrum [16]. But the overlaps between different color channels are not alike. Generally speaking, the overlaps between the green channel and the other two channels are serious. Red and blue channels have very little color crosstalk. We used a single-chip color camera; its spectral response curve is shown in Figure 1. In our projector, the wavelength of blue LEDs was 459 nm, and that of the red LEDs was 618 nm. Figure 1 shows that the quantum efficiency of the red channel, at 459 nm, and that of the blue channel, at 618 nm, were very low. This means that the red and blue superimposed fringe patterns can be separated well.
# Pneumonia Recognition by Deep Learning:
## 2 Methods
### Step 2: Deep Learning Model Construction for Pneumonia Recognition
#### 2.3.2 Gpu Loading  
The PyTorch network framework for deep learning can specify the computational device. The CPU is used for computation by default, or we can enter the relevant command to load the model into the GPU for computation, thus greatly speeding up the computation. The process of loading the GPU is divided into three parts. First, the device is set as \(device=torch.device(cuda:0^{\prime}iftorch.cuda.is_{a}available()else^{ \prime}cpu^{\prime})\). The meaning of the above code is that, if there is a GPU, the calculation is performed on the GPU, and the calculation graphics card is cuda:0, which means that the calculation is performed on the first graphics card; otherwise, the calculation is performed on the CPU. Then, the model is loaded into the GPU: \(model.to(device)\), and finally, both the input and output of the training module are loaded into the GPU at computation time. In this paper, the code is represented as a training module: \(inputs,target=inputs.to(device)\), \(target.to(device)\), or test module: \(images,labels=images.to(device)\), \(labels.to(device)\). When loading the GPU, it is necessary to ensure that the model, training module, and test module are all loaded on the same card.  
We compared the execution time of the deep learning models in the form of \"big O\" notations, as illustrated in Table 2. \"Big O\" notation is generally used to describe the complexity of an algorithm, which is the worst-case speedup of the algorithm and is generally represented by \(O(n)\), where \(n\) denotes the number of operations [34]. The representation of \"big O\" notation includes \(O(1)\), \(O(n)\), \(O(n^{2})\), \(O(2n)\), \(O(logn)\), etc., which describes the upper session of asymptotic time complexity when the program runs, as well as the size and performance of the algorithm in the worst-case scenario when the elements in the data structure are increased [35,36].  
In this paper, the complexity of the algorithm is calculated in the form of \"big O\" notation mainly by the analysis of the cyclic nested structure, in which LeNet5, AlexNet, ResNet18, MobileNet are all two-layer cyclic nested structures, and the other parts of the model are computed in parallel, the time complexity of which is \(O(n^{2})\). Vision Transformer includes three nestings, and its time complexity is \(O(n^{3})\).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **LeNet5** & **AlexNet** & **ResNet18** & **MobileNet** & **Vision Transformer** \\ \hline
**Time complexity** & \(O(n^{2})\) & \(O(n^{2})\) & \(O(n^{2})\) & \(O(n^{2})\) & \(O(n^{3})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The time complexity of each model.
## 2 Literature Review
### Manufacturing Execution Systems
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Commercial Solutions**} & \multirow{2}{*}{**Main Functionalities**} & \multicolumn{2}{c}{**Does It Include**} & \multicolumn{2}{c}{**Does It Consider**} \\  & & **Human Scheduling?** & **Human Well-Being in** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Plex Execution \\ System [39] \\ \end{tabular} } & Production finite scheduling & & \\  & Closed-loop quality management & Yes & No, it considers humans as a resource. \\  & Inventory and production management & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Siemens Opcenter \\ Execution Process [40] \\ \end{tabular} } & Resources allocation and control & & \\  & Inventory and production management & Yes & Yes, it includes performance analysis. \\  & Data collection and acquisition & & \\  & Maintenance operations management & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Proficy Smart Factory MES \\ by GE Digital [41] \\ \end{tabular} } & OEE and quality with analytics & Not in MES software, but GE & \\  & Production scheduling & Digital have a separate software & N/A \\ \cline{2-4}  & Manufacturing data management & to perform it. & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} SAP Manufacturing \\ Execution [42] \\ \end{tabular} } & Simplified integration with SAP ERP & & \\  & Inventory and production management & & \\ \cline{1-1} \cline{2-4}  & Performance analysis & Yes, but as external module. & No, it considers humans as a resource. \\ \cline{1-1} \cline{2-4}  & Data collection and acquisition & & \\ \cline{1-1} \cline{2-4}  & Production scheduling & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Commercial solutions in MES providers. These tools are specific for human scheduling and shift planning. All applications offer a different way to perform the human scheduling, considering different attributes but only one of the solutions includes the well-being of the worker. Even the commercial solutions shown in Tables 1 and 2 were considered; however, they do not provide any technical insight of the methodologies used since the information available is what is shown in the marketing material of these applications.