esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 3 Experiment and Results
### Experimental Datasets
#### 3.1.2 The Denim Dataset  
The denim dataset has 6913 images and overall 9523 defects labeled in PASCAL VOC [27] format. The image size is 2446 \(\times\) 1000 pixels. There are 20 categories of defects. The number of each category defect is shown in Table 2 and Figure 6a. Most of the defects are tiny, and there areas are less than 1% of the entire image. The aspect ratios distribution of defects is shown in Figure 6b. The red bounding box corresponds to the number of defects with moderate aspect ratios. The extreme aspect ratio defects from 0 to 0.5 and 2 to 5 are accounted for 47.7% of total defects.  
Figure 7 shows some defects of the denim dataset. Defects such as knot and coarse warp in images are extremely small, the shapes of three wire and flower jump defects are extreme aspect ratios outside the red box in Figure 6b.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & **File** & **Stains** & **Difficult** & **Nep** & **Broken Warp** & **Light Warp** & **Looped** \\ \hline
**Defect Class Id** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \multirow{3}{*}{**Number of defects**} & Train & 247 & 265 & 246 & 133 & 191 & 104 & 317 \\  & Validation & 61 & 66 & 61 & 33 & 47 & 26 & 79 \\  & Test & 53 & 70 & 59 & 29 & 46 & 30 & 56 \\  & Total & 361 & 401 & 361 & 195 & 284 & 160 & 452 \\ \hline \multirow{3}{*}{**Category**} & **Star Jump** & **Coarse Pick** & **Mispick** & **Starch** & **Warp Knot** & **Broken Spandex** & **Knot** \\  & **Star Jump** & **Coarse Pick** & **Mispick** & **Lump** & **Warp Knot** & **Broken Spandex** & **Knot** \\ \hline
**Defect Class Id** & **8** & **9** & **10** & **11** & **12** & **13** & **14** \\ \hline \multirow{3}{*}{**Number of defects**} & Train & 221 & 656 & 112 & 243 & 323 & 366 & 1379 \\  & Validation & 55 & 164 & 27 & 60 & 80 & 91 & 344 \\  & Test & 26 & 95 & 22 & 31 & 67 & 89 & 273 \\  & Total & 302 & 915 & 161 & 336 & 473 & 546 & 1996 \\ \hline \multirow{3}{*}{**Category Name**} & **Flower** & **Coarse Warp** & **Loose Warp** & **Mark** & **Three Wire** & **Hole** & **Total** \\  & **Jump** & **Coarse Warp** & **Loose Warp** & **Mark** & **Three Wire** & **Hole** & **Total** \\ \hline
**Defect Class Id** & **15** & **16** & **17** & **18** & **19** & **20** & **Total** \\ \hline \multirow{3}{*}{**Number of defects**} & Train & 96 & 143 & 267 & 324 & 698 & 196 & 6527 \\  & Validation & 23 & 35 & 66 & 81 & 174 & 48 & 1621 \\ \cline{1-1}  & Test & 15 & 35 & 63 & 65 & 183 & 68 & 1375 \\ \cline{1-1}  & Total & 134 & 213 & 396 & 470 & 1055 & 312 & 9523 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of each category defects of the denim dataset.  
Figure 6: The distribution of defects. (**a**) number of defects classes (**b**) defects aspect ratios.
## 4 Discussion  
The OT camera system has been proved to be sufficient to capture the radiation layer-wise during the manufacturing process. This provides the possibility of understanding energy-relevant behavior of the LPBF process, which can link to the product quality in the end of the process chain. Unlike the existing research in [1], the proposed method focused on the radiation prediction according to the geometry of the designed part using its sliced contour. The result shows the capability of predicting the critical anomaly of radiation. Since the inputs are only contours of a designed part in slice, the strip overlap due to scan vector orientation should not be predictable. The Pix2 Pix algorithm made a guess of possible strip overlap due its generative structure by the discriminator of GAN. In this case, the guess is not plausible, according to ground truth. Considering that the wrongly predicted strip area has higher radiation than its surroundings, this can be classified as anomaly radiation. Due to this potentially wrong classification, U-Net is more stable than the Pix2 Pix algorithm. This method enables the investigation of energy-relevant behaviors without real processing or simulation, which provides the possibility of evaluating and optimizing the part orientation in data preparation. However, on the one hand, the prediction considers only geometry and is not yet fully fit to the real case due to the lack of information on the process, for example, scan vector orientation and laser parameter. On the other hand, the predictive model can only cover common cases in the manufacturing process. The stochasticity from the machine, powder bed, and environment, which will occur during the LPBF process, cannot be predicted. This will lead to potential defects in the final product.  
In the meantime, the training dataset was captured and processed by manufacturing parts with overhang angles. Radiation on the geometries with overhang angles in the test dataset was predicted due to the availability of data with similar geometries. In Figure 9, the prediction results for layer 80 and 300 of the test dataset using trained model via the full dataset and data from the first 150 layers are shown. Since the first 150 layers in the training dataset contains a strut section with an identical size for each part (see in Table 3), radiation in the overhang section at layer 300, as well as the inner section at layer 80, was predicted with offset by the prediction model using the first 150 layers. In this regard, the shape size of the designed geometry for building up training sets can influence the accuracy of the prediction model. Furthermore, since the radiation during the LPBF process is varied for different materials and different laser parameters, the prediction model is applicable under the configuration, which is identical to the training set. For new configurations, the model needs to be trained with appropriate data.  
Figure 8: Test results of layer 80 and layer 300 for different network structures.
## 1 Introduction
According to [26], the scattered field is modeled as being radiated by equivalent surface currents supported at the air/soil interface (or at some reference plane above the target), which indeed retain information about the existence and the transverse location of the buried targets. Moreover, imaging is cast as a 2D inverse problem which is computationally convenient. It is remarked that the system can be considered to operate like a passive radar. However, while the source is actually non-cooperative (in the sense that it does not share any information with the receivers), it is not opportunistic since it is deliberately deployed in the scene. The proposed model assumes that the equivalent current radiates in free-space so that a free-space propagator is considered while achieving the reconstructions. This assumption is not so critical since the targets are shallowly buried. In other words, the propagation path in free-space is much longer than the one in the half-space. Also, the proposed method can experience the drawback that targets, which are not close to the chosen reference plane, can be difficult to detect. To avoid this problem, reconstructions over plane at different depths are computed. Even under the above considered framework, the number of required sensors soon become unfeasible when the area to be investigated is even only moderately large (in terms of wavelength). This question is clearly related to the so-called number of degrees of freedom (NDF) [29] of the pertinent scattering operator. Accordingly, the first contribution we give in this paper is the estimation of the relevant NDF. As we will show, collecting the data according to the NDF is practically impossible. Consequently, reconstructions will unavoidably be aliased. Therefore, the second contribution concerns a simple multi-frequency procedure to mitigate aliasing artifacts. More in detail, since the TX and the RXs are not synchronized, multi-frequency data cannot be processed coherently (simultaneously). Hence, each frequency data is processed separately. Of course, each single-frequency reconstruction results corrupted by aliasing artifacts. However, since such artifacts are frequency dependent they corrupt single-frequency reconstructions in a different way. This feature is then exploited to mitigate such artifacts by devising a simple scheme which fuses the different single-frequency reconstructions. The paper is organized as follows. In Section 2 the scattering model introduced in [26] is briefly recalled. Section 3 is devoted to the study of the properties of the scattering operator in order to find the NDF and to highlight how the number and the positions of the receiving sensors affect the spatial resolution. In Section 4, we introduce a technique that can mitigate aliasing artifacts by properly processing single-frequency images. In addition, a criterion for choosing the working frequencies is provided. Finally, numerical examples are reported in Section 5.
## 3 Data Analysis and Processing Method
### Data Extraction using the CSP Filtering Method
#### 3.1.2 CSP Filtering Method Compared with the Raw Signal  
The difference between raw EEG and CSP-filtered signals is shown in Figures 10 and 11. Based on the data pair point of EEG for each channel, the difference between the two data can be verified before and after the application of the CSP filter. In the case of raw data, the distinction between two movements is not apparent, but CSP filters enable the distinction between two arms and, in particular, the distinction between C3 and C4 is relatively pronounced. In the case of subjects, it can be determined that the brain waves in the C3 and C4 regions of the motor domain can be used as signals to distinguish between right- and left-handed behavior. However, it is difficult to define the baseline of two signal  
Figure 8: Procedure for CSP algorithm of raw data output.  
classifications accurately, and more experimental data will be needed through improved algorithms and the diversification of measurement points.  
Figure 9: Comparison between raw data and CSP filtered data according to electrode attachment location. (**a**) Raw EEG output for right-hand (upper) and left-hand (lower) C3 and C4 channels, respectively; (**b**) CSP EEG output for right-hand (upper) and left-hand (lower) C3 and C4 channels.  
Figure 11: Comparison of the output of CSP filtering data in left- and right-handed operations.  
Figure 10: Comparison of the output of raw data in left- and right-handed operations.
# Effect of Aggregate Size on Recycled Aggregate Concrete under Equivalent Mortar Volume Mix Design
## 4 Conclusions
Calculating the required volume of NA in NAC and RAC, and RCA in RAC:  
\[\text{Volume of NA in NAC : }\text{V}_{\rm NA}^{\rm NAC}=\frac{W_{\rm OD- NA}^{\rm NAC}}{\text{SG}_{\rm b}^{\rm NA}\times 1000}=\frac{1003}{2.63 \times 1000}=0.381\]  
where \(\text{SG}_{\rm b}^{\rm NA}\) is the bulk specific gravity of NA (=2.63)  
\[\text{Volume of NA in RAC : }\text{V}_{\rm NA}^{\rm RAC}=\text{R}\times\text{V}_{ \rm NA}^{\rm NAC}=0.75\times 0.381=0.286\]  
where R is a replacement ratio of NA by RCA  
\[\text{Volume of RCA in RAC : }\text{V}_{\rm RCA}^{\rm RAC}=\text{V}_{\rm NA}^{\rm NAC }\times(1-\text{R})/\left[(1-\text{RMC})\times\frac{\text{SG}_{\rm b}^{\rm ACA }}{\text{SG}_{\rm b}^{\rm NA}})\right]=0.381\times(1-\text{R})/)/\left[(1-0.286) \times\frac{2.29}{2.52}\right)]=0.147\]where \(\text{SG}_{\text{b}}^{\text{RCA}}\) is the bulk specific gravity of RCA and \(\text{SG}_{\text{b}}^{\text{OA}}\) is the bulk specific gravity of OA  
3. Calculating volume of mortar in NAC and RAC:  
Volume of mortar in NAC : \(\text{V}_{\text{M}}^{\text{NAC}}=1-\text{V}_{\text{NA}}^{\text{NAC}}=1-0.381=0.619\)  
Volume of residual mortar in RAC : \(\text{V}_{\text{RM}}^{\text{RAC}}=\text{V}_{\text{RCA}}^{\text{RAC}}\times \left[1-(1-\text{RMC})\times\frac{\text{SG}_{\text{b}}^{\text{RCA}}}{\text{SG}_ {\text{b}}^{\text{RCA}}}\right]=0.147\times\left[1-(1-0.286)\times\frac{2.29}{2.32}\right]=0.052\)  
Volume of new mortar in RAC : \(\text{V}_{\text{NM}}^{\text{RAC}}=\text{V}_{\text{M}}^{\text{NAC}}-\text{V}_{ \text{RM}}^{\text{RAC}}=0.617-0.052=0.567\)  
4. Calculating required weight of each material in RAC:  
\[\text{Oven}-\text{dry weight of RCA}:\text{ W}_{\text{OD-RCA}}^{\text{RAC}}=\text{V}_{\text{RCA}}^{\text{RAC}} \times\text{SG}_{\text{b}}^{\text{RCA}}\times 1000=0.147\times 2.29\times 1000=336 \text{ kg}\]  
\[\text{Oven}-\text{dry weight of NA}:\text{ W}_{\text{OD-NA}}^{\text{RAC}}=\text{V}_{\text{NA}}^{\text{RAC}} \times\text{SG}_{\text{b}}^{\text{NA}}\times 1000=0.286\times 2.63\times 1000=752 \text{ kg}\]  
\[\text{Oven}-\text{dry weight of FA}:\text{ W}_{\text{OD-FA}}^{\text{RAC}}=\text{W}_{\text{OD-FA}}^{\text{NAC}} \times\frac{\text{V}_{\text{NA}}^{\text{RAC}}}{\text{V}_{\text{NA}}^{\text{ CTA}}}=720\times\frac{0.567}{0.619}=660\text{ kg}\]  
\[\text{Weight of water}:\text{ W}_{\text{Water}}^{\text{RAC}}=\text{W}_{\text{Water}}^{\text{NAC}}\times\frac{\text{V}_{\text{NM}}^{\text{RAC}}}{\text{V}_{ \text{NM}}^{\text{RAC}}}=175\times\frac{0.567}{0.619}=160\text{ kg}\]  
\[\text{Weight of cement}:\text{ W}_{\text{ Cement}}^{\text{RAC}}=\text{W}_{\text{ Cement}}^{\text{NAC}}\times\frac{\text{V}_{\text{NM}}^{\text{RAC}}}{\text{V}_{ \text{M}}^{\text{RAC}}}=389\times\frac{0.567}{0.619}=357\text{ kg}\]
## 6 Conclusions and Future Work  
Building a high-quality training corpus has become increasingly important for the application and development of the \"pre-training + fine-tuning\" model in the field of NLP. Since it is costly to build the dataset manually, it has become a trend to build it through computer modeling. In this study, to improve the efficiency of the Tibetan sentence boundary disambiguation model, we first analyze the characteristics of existing Tibetan pre-trained language models on the SBD task, then train the Tibetan BERT pre-trained model based on different subword methods and finally validate the performance of the Tibetan SBD on four different pre-trained language models, namely, BERT (BPE), BERT (SP), TiBERT, and Multi-BERT. The performance of SBD on pre-trained + four classical deep learning models with different windows is studied. The experimental results show that the method in this study has some generality in SBD tasks, and the BPE subword approach is more suitable for the Tibetan SBD task. In later work, the corpus size will be expanded further. The open corpus will be supplemented for more in-depth validation of the model, and at the same time, we will try to apply the method of this study in the data preprocessing stage of Tibetan machine translation, summary extraction, text generation, and pre-trained language models. The future improvement directions mainly include two parts. Firstly, we aim to establish a larger Tibetan corpus, including text data from different fields and styles. Secondly, a deeper understanding of the rules of Tibetan language in terms of grammar structure, language features, and the use of punctuation can provide more accurate evidence for SBD.  
Conceptualization, F.L. and Z.Z.; methodology, F.L.; software, Z.Z.; validation, Z.Z. and L.W.; formal analysis, H.D.; investigation, L.W.; resources, F.L.; data curation, Z.Z.; writing\(-\)original draft preparation, F.L.; writing\(-\)review and editing, F.L. and H.D.; visualization, L.W.; supervision, H.D.; project administration, Z.Z.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant 62162056, 62266037), Major science and technology projects of Gansu province (23 ZDGA009), Science and Technology Commissioner Special Project of Gansu province (23 CXGA0086), Gansu Provincial Department of Education: Industry Support Program Project (2022 CYZC-12), and Northwest Normal University Young Teachers Research Ability Enhancement Program Project (NWNU-LKQN2019-28).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 3 Proposed Method  
This paper proposes an end-to-end deep clustering model for insider threat detection, which learns the feature representation and cluster centers of multi-source user behavior sequences. The objective function of the model includes two main components: one for learning the feature representation of multi-source user behavior sequences and another for deep clustering.  
Firstly, an encoder-decoder model is trained. The encoder encodes the feature representation of multi-source behavior sequences, and the decoder predicts the last behavior event in the sequence. The deviation between the predicted value and the true value is used as the objective function to adjust the parameters for learning the feature representation of multi-source user behavior sequences. After training, the decoder is discarded, and the encoder is used to obtain the feature space of multi-source user behavior sequences. The cluster centers for k-means clustering are initialized using this feature space.  
Secondly, the objective function for deep clustering is defined, considering intra-cluster compactness and inter-cluster separation. The clustering centers are refined iteratively using stochastic gradient descent, and the feature representation of user behavior sequences is adjusted backward to optimize the clustering objective function. Finally, based on the clustering results, sparse clusters and outliers that deviate from the cluster centers are identified and detected as anomalies.
## 3 Underwater Routing Protocols
### Localization-Based Routing Protocols
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Routing Strategy** & **Problem Addressed** & **Merits** & **Demerits** & **Year** \\ \hline \multirow{6}{*}{FVBF} & Nodes in a pipe from the source to the water surface & & & & \\  & are selected based on closeness to the sink, closeness to the shortest & Energy consumption & High throughput, energy and delay efficiency & Nodes in the pipe overload and die rapidly & 2018 \\  & path from the source to the sink and the battery level & & & & \\ \hline \multirow{6}{*}{AVN-AHH-VBF} & Improves VBF by considering the void region, depth and the altering holding time of VBF & Void region, energy consumption & Energy efficiency & High packet loss to save energy, nodes in the pipe overloaded with data as in VBF & 2016 \\ \cline{2-6}  & Uses opportunistic routing & & & & \\  & and selects relay nodes & & & & \\  & based on packet delivery & & & & \\  & probability and packet & & & & \\  & advancement & & & & \\ \hline \multirow{6}{*}{GEDAR} & Uses opportunistic routing & Energy consumption, & Energy efficiency & Unbalanced energy consumption & 2016 \\  & and selects forwarder & & & & \\  & nodes based on packet & & & & \\  & advancement & & & & \\  & advancement & & & & \\ \hline \multirow{6}{*}{EHCAR} & Uses opportunistic routing & Energy efficiency, high & Unbalanced energy consumption & 2016 \\  & and selects forwarder & & & & \\  & nodes based on packet & & & & \\  & advancement & & & & \\  & advancement & & & & \\ \hline \multirow{6}{*}{EHCAR} & A node with energy below & & & & \\  & a certain threshold informs & & & & \\  & other nodes to replace it & & & & \\  & and avoid holes & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{6}{*}{PAM} & Autonomous underwater & & & & \\  & vehicles have predefined & & & & \\  & paths with known current & & & & \\  & distance and position from & & & & \\  & the neighbor vehicles to & & & & \\  & avoid the void zone & & & & \\ \hline \multirow{6}{*}{FLMPC} & A source node checks that a & & High throughput as & & \\  & forwarder has two or three & & nodes having no & & \\  & hops before sending & & forwarder in the next & & \\  & packets to it it to avoid & & & & \\  & packets loss due to the & & & & \\  & absence of neighbor nodes & & & \\  & of the forwarder & & & & \\ \hline \multirow{6}{*}{VBVA} & A vector shift & & & & \\  & & strategy routes & & & \\  & & the packets & & & \\  & along the void’s & & & \\  & & boundary while & & & \\  & a back-pressure & & & & \\  & mechanism & & & \\  & reverse them & & & \\  & when a concave & & & \\  & void exists & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont.
## 4 Parameter Sensitivity Analysis
### The Influence of Ground Stress on the External Water Pressure of the Structure  
Using the parameters in Section 3.1, according to Formula (19), the water head at each position of the ordinary surrounding rock section is calculated, and the curve is drawn in Figure 6.  
Figure 6 shows that the height of the water head at each position of the ordinary surrounding rock section increases with the increase of the permeability coefficient of the surrounding rock by the in situ stress variation parameter \(n\). When the variation parameters \(n\) are ignored, the water blocking capacity of the surrounding rock is improved, the head loss is increased, and the structure is in a disadvantageous state.  
Excavation disturbance, ground stress, tunnel support structure and grouting circle will all cause the permeability of surrounding rock to change to varying degrees, and then affect the water resistance of surrounding rock. The difference is that the excavation disturbance, the tunnel support structure and the grouting circle are artificially changed, and the in situ stress affects the original permeability coefficient of the surrounding rock. The influence of various factors on the permeability coefficient of surrounding rock should be considered in the design of tunnel supporting structure and waterproofing and drainage, so as to reduce the probability of prediction distortion.  
Figure 6: Water head height curve of ordinary surrounding rock section.
# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 3 Methods
### Short Description of ANN  
ANN is a computing system that is inspired by the biological neural network. It is composed of interconnecting artificial neurons. In general, a neural network function creates a linear output pattern, given a particular input layer [30]. A multilayer perceptron (MLP) is commonly used as several types of neural. The MLP is a feed-forward artificial neural-network model that maps input data onto an appropriate output. An MLP is comprised of multiple node layers. Each layer connects a network with the next layer. In many cases, the units of these networks use a sigmoid function as an activation function. For training the network, MLP utilizes back propagation [31]. The back-propagation algorithm consists of a propagation and weight update phases. In the propagation step, the calculated error, which is the difference between the target value and the output value, is propagated to each layer. By using this propagated error, the weight is modified in a weight-update phase. They are repeated until the network performance is good enough [32]. We implemented the ANN with MATLAB software. The used ANN method is a two-multilayer perceptron with sigmoid-function and four neurons in the hidden layer and one linear-function output neuron. It is also trained with the back-propagation algorithm.