esa-sceva/satcom-chunk-collection huggingface dataset:
# Diverse Decoding for Abstractive Document Summarization
## 3 Neural Abstractive Summarization with Diverse Decoding
### Diversity-Promoting Beam Search Method
#### 3.2.3 Diversity-Promoting Beam Search  
To overcome this shortcoming and inspired by the work of [41] in machine translation, we propose to introduce the ranking information in the calculation of candidates' score \(S\left(Y_{t-1}^{k},y_{t}^{k,k^{\prime}}\,|\,x\right)\) to discourage sequences from sharing common roots and thus promoting diversity. For each of the K candidates \(Y_{t-1}^{k}\), we rank the newly-generated top \(K\) nodes \(Y_{t-1}^{k,k}\), based on their conditional probability \(P(y_{t}^{k,k^{\prime}}|\,x,Y_{t-1}^{k})\) in descending order. Therefore, now, \(k^{\prime}\) denotes the ranking of the current node among its siblings. Then, we add an additional part \(\gamma k^{\prime}\) to rewrite the score function for \(\left[Y_{t-1}^{k},y_{t}^{k,k^{\prime}}\right]\):  
\[\hat{S}\left(Y_{t-1}^{k},y_{t}^{k,k^{\prime}}\,|\,x\right)=S\left(Y_{t-1}^{k}, y_{t}^{k,k^{\prime}}\,|\,x\right)-\gamma k^{\prime} \tag{10}\]  
where scalar \(\gamma\) can be tuned based on the validation set. The top K candidates are then selected based on \(\hat{S}\left(Y_{t-1}^{k},y_{t}^{k,k^{\prime}}\,|\,x\right)\) as the time step moves on. This additional term \(\gamma k^{\prime}\) works by punishing bottom ranked nodes among siblings (directly descended from the same parental node), thus making the whole decoding process tend to select candidates descended from different root nodes. The diversity-promoting process is shown in Figure 3.  
With the length of decoding sequences growing, the \"accumulated parental advantage\" of the first beam would gradually undermine the effect of \(\gamma k^{\prime}\) and make the selection still concentrate on the first beam in the last several time steps of decoding. To avoid this, we use the new score function for beam search at the beginning of decoding. Next, we divide the K unfinished candidates into groups G at  
Figure 3: Illustration of standard beam search and the proposed diversity-promoting beam search. Note that this is only the initial part of the diversity-promoting process. After the candidates have diverged sufficiently, we turn to using the grouping strategy.  
time step \(t^{\prime}\) when the candidates have diverged sufficiently. Then, we limit the selection of candidates into each of the groups, i.e., we use the standard beam search with beam size K/G inside each of these groups until the end of decoding. As the sequences have already diverged, the subsequent predicted nodes from different groups are unlikely to overlap, and thus, the grouping strategy can help to preserve the diverged beam.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 5 Conclusions  
The identification of lithology from Pol-SAR data is challenging due to weathering and surface occlusion. In this paper, we have proposed a novel classification method based on dual-frequency Pol-SAR data and a deep learning algorithm. This method includes pre-processing, dual-frequency polarimetric features extraction, and classification with an SSAE. In this method, superpixels are produced to integrate contextual information of the neighborhood. Then, new features are proposed to increase the class separability of lithology by combining dual-frequency Pol-SAR data. These features and the coherency matrices of dual-frequency Pol-SAR data are incorporated into an SSAE for lithology classification. The performance of the classifier is demonstrated with measured SIR-C data. The overall accuracy is about 98.90% for the dataset in the experiment. New features are proved to be necessary by comparing with M1, M2, M3, and [PERSON]'s method. Meanwhile, it has been demonstrated that the SSAE outperforms SVM for lithology classification. Experimental results confirm the validation and applicability of the proposed method for lithology classification.  
Methodology, W.W.; Validation, W.W. and X.R.; Formal analysis, W.W. and X.R.; Data curation, Y.Z.; Writing--original draft preparation, W.W. and X.R.; Writing--review & editing, M.L.; Project administration, W.W.; Funding acquisition, W.W.  
This research was funded by the National Natural Science Foundation of China [61771028].  
The authors would like to thank the Chinese Academy of Sciences and the NASA/JPL SIR-C team for the data used in this paper.  
The authors declare no conflict of interest.
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 4 Experiment
### Comparison between Pattern Recognition Methods and Our Method  
To evaluate the performance of the classification module of our method, we compared the accuracy of the defect classification between our method and traditional methods whose codes are publicly available. (1) GLCM (Grey-Level Co-Occurrence Matrices) [37]: The GLCM feature refers to a common method of describing texture features by studying the spatial correlation properties of grayscale, and the texture features are a combination of energy, contrast, entropy, and correlation. (2) HOG (Histogram of Oriented Gradients) [38]: The HOG feature is a feature descriptor used for object detection in image processing. The algorithm first divided the image into small connected regions, which we call cell units. Then we collected the gradient or edge direction of each pixel in the cell unit to get a histogram. Finally, these histograms were combined to form a feature descriptor.  
After obtaining the features described above, we used the SVM (Support Vector Machine) and the MLP (Multi-layer Perceptron) to classify the features. The MLP consisted of a two-layer neural network, a hidden layer, and an output layer. The hidden layer had 15 hidden units and the output  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Defect Detection Network**} \\ \hline More & \(\surd\) & & & \(\surd\) & & \(\surd\) & \(\surd\) \\ augmentation & \(\surd\) & & & \(\surd\) & & \(\surd\) \\ High resolution & & \(\surd\) & & \(\surd\) & & \(\surd\) \\ Pre-trained & & & \(\surd\) & & \(\surd\) & \(\surd\) \\ network & & & \(\surd\) & & \(\surd\) & \(\surd\) \\ mAP(\%) & 67.18 & 69.45 & 65.84 & 76.17 & 74.08 & 73.69 & 84.49 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of influencing factors.  
Figure 11: Detection results under different base networks: (**a**) Resnet-50; (**b**) VGG-19; (**c**) MobileNet; and (**d**) our CNN model.  
layer had 4 output units. We evaluated the performance of the defect classifier quantitatively using the micro F1 score. The micro F1 score was introduced in Section 4.1.2.  
The results are shown in the Table 11. It can be seen from the Table that the traditional method could only achieve a micro F1 score of about 70, whereas our method achieved a score of over 90 in the classification task. That was because we used deep convolutional neural networks to learn the internal features of the defects, which had a positive impact on the classification task.
# Quantification of the Effective Detectable Period for Concrete Voids of CLP by Lock-In Thermography
## 3 FEM Modeling and LIT Simulation  
### CLP Mockup Specim  
As shown in Figure 2, the shape of the mockup specimen considering the concrete void on the back of the CLP included four concrete voids of two areas (75 \(\times\) 75 mm\({}^{2}\) and 105 \(\times\) 105 mm\({}^{2}\)) and two depths (15 mm and 30 mm). All voids were designed to be located within an area of 250 \(\times\) 250 mm\({}^{2}\) in consideration of the inspection range of an IR camera (FLIR SC645). A 6 mm thick SS 275 plate similar to CLP was attached to the front of the concrete block, and black paint with an emissivity of 0.95 was applied to the surface of the plate.  
For the LIT simulation through numerical analysis and the analysis of the experimental results, identification numbers (#1 to #4) were assigned to each void part, and'sound parts' without voids were distinguished by '#N'. In addition, 'S' for simulation and 'E' for mockup experiment are added to distinguish them.
# Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants
## 2 Materials and Methods
### Methods  
The proposed inspection process aims to make full use of the advantages of aerial IR thermography for speed, and ground-level IR thermography for its resolution and precision. This strategy is based on a mixed thermographic inspection done in two stages. Aerial IR thermographic inspection is done in the first stage, while ground-level IR thermographic inspection is done in the second stage, only for significant incidents detected by the aerial IR thermography. This second stage with greater detail will improve the previous diagnosis conducted in the first aerial inspection. This two-stage thermographic inspection strategy may be suitable for cases in which inadequate maintenance is done, either due to technical reasons or due to excessive time delays in doing it.  
For proper IR thermographic inspections of photovoltaic plants, specific configuration and positioning issues for the thermography equipment must be kept in mind [29]. The surface emissivity of the modules and the reflected apparent temperature must be determined experimentally. Both parameters enable the configuration of the thermography equipment. The modules' surface emissivity can be determined with the help of a contact  
Figure 1: Images of the drone used for the aerial IR thermographic inspection.  
sensor. Typical emissivity values are 0.85 for glass and 0.95 for the polymer on the back cover [13]. The reflected apparent temperature is required to offset the radiation reflected on the object from the sky. The reflected apparent temperature can be determined by a Lambertian radiator in a horizontal position in the shade so that it is directly exposed to the sky [14; 30].  
The positioning of the equipment for the inspection must also be borne in mind [29]. The ideal location so that the emissivity value can be considered constant is to inspect with an angle slightly greater than 0\({}^{\circ}\) from the perpendicular with the PV module surfaces, to prevent reflection, but without exceeding 40\({}^{\circ}\) as the upper limit for doing the inspection. Moreover, the ideal thermographic inspection angle will be that which minimises solar reflection and does not increase reflectivity. A suitable position may be when the thermographic camera is situated with the sun behind it at an inspection angle of less than 45\({}^{\circ}\). Further, to minimise reflections from the sky, the inspection must be done at a specific height, while maintaining the required optical resolution. To obtain high-quality images, taking IR images with high solar radiation is recommended (>600 W/m\({}^{2}\)), as well as constant environmental conditions (no clouds, low wind speed and stable ambient temperature). Figure 2 shows the flow chart to be considered for the proposed two-step approach.
# IoT Privacy and Security Challenges for Smart Home Environments
## 4 Security Threats in the Smart Home  
### Threats  
Although the Smart Home is a very different environment, the overall nature of security threats is similar to other domains.  
Confidentiality threats are those that result in the unwanted release of sensitive information. For example, confidentiality breaches in home monitoring systems can lead to the inadvertent release of sensitive medical data. Even seemingly innocuous data, such as the internal home temperature, along with knowledge of the air conditioning system operation parameters, could be used to determine whether a house is occupied or not, as a precursor to burglary. Loss of confidentiality in things such as keys and passwords will lead to unauthorized system access threats.  
Authentication threats can lead to either sensing or control information being tampered with. For example, unauthenticated system status alerts might confuse a house controller into thinking that there is an emergency situation and opening doors and windows to allow an emergency exit, when in fact allowing illicit entry. One issue that will be raised later is automated software updates--if these are not appropriately authenticated problems can arise.  
Access threats are probably the greatest threats. Unauthorized access to a system controller, particularly at the administrator level, makes the entire system insecure. This can be through inappropriate password and key management, or it could be by unauthorized devices connecting to the network. Even if control cannot be gained, an unauthorized connection to a network can steal network bandwidth, or result in a denial of service to legitimate users. Since many Smart Home devices may be battery operated and wirelessly networked with a low operational duty cycle, flooding a network with requests can lead to an energy depletion attack--a form of denial of service.
## Appendix B Description of VPM Model  
In the vegetation production model (VPM), LUE\({}_{\text{max}}\) is impacted by temperature, land surface moisture, and leaf phenology. Here is a concise overview of the VPM model:  
\[GPP=PAR\times FPAR\times LUE_{max}\times T_{s}\times W_{SLSWII}\times P_{s} \tag{1}\]  
The current version of the VPM model assumes that FPAR is directly proportional to EVI, with a fixed coefficient value of 1.0 [6]. \(T_{s}\), \(W_{SLSWII}\), and \(P_{s}\) are scalar values that represent the impact of temperature, water, and leaf phenology on the LUE of vegetation, respectively. At each time step, \(T_{s}\) is calculated using the equation developed for the terrestrial ecosystem model [60] as follows:  
\[T_{s}=\frac{(T-T_{min})\times(T-T_{max})}{\left((T-T_{min})\times(T-T_{max})- \left(T-T_{opt}\right)^{2}\right)} \tag{2}\]  
where \(T\) represents the air temperature and \(T_{min}\), \(T_{max}\), and \(T_{opt}\) represent the minimum, maximum, and optimum air temperatures (\({}^{\circ}\)C) for photosynthetic activity, respectively. When the air temperature falls below \(T_{min}\) or exceeds \(T_{max}\), \(T_{s}\) is assigned a value of 0. In this study, \(T_{min}\), \(T_{max}\) and \(T_{opt}\) were set to \(-2.5\), \(44.6\), and \(23.4\)\({}^{\circ}\)C, respectively, for all PFTs except C4 crops, while for C4 crops, \(T_{min}\), \(T_{max}\) and \(T_{opt}\) were set to 7.5, 43.7, and \(31.3\)\({}^{\circ}\)C, respectively [34].  
The VPM also uses the LSWI (land surface water index) [6] to account for the impacts of water stress and phenology on plant photosynthesis:  
\[LSWI=\frac{\rho_{NIR}-\rho_{SWIR}}{\rho_{NIR}+\rho_{SWIR}} \tag{3}\]where \(NIR\) denotes the 841-876 nm band and \(SWIR\) denotes 1628-1652 nm band. The water index was defined as follows:  
\[W_{SLSWI}=\frac{1+LSWI}{1+LSWI_{max}}\] (A4)  
where \(LSWI_{max}\) is the highest LSWI value during the plant growing season for each pixel, \(P_{s}\) is included to represent the impact of leaf phenology (leaf age) on photosynthesis at the canopy level, and \(P_{s}\) is computed as a linear function of LSWI from bud burst to full leaf expansion:  
\[P_{s}=\frac{1+LSWI}{2}\] (A5)  
Once the leaves reach full expansion, \(P_{s}\) is assigned a value of 1.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 3 The Proposed Algorithm
### Patch-Based Representation  
First, we divide the bounding box into \(n*n\) patches, and different gridding sizes from \(7*7\) to \(10*10\) are tried in the proposed method. We decided to divide the bounding box into 64 patches as a trade-off between accuracy and efficiency, i.e., the parameter \(n\) is 8. Since the results will become worse if adopting other gridding sizes bigger or smaller than \(8*8\), a patch should have the proper size to make the tracker more robust. Each patch is characterized with a d-dimensional feature vector through a combination of the RGB color histogram and HOG feature. Then, the feature descriptor [10] for a bounding box in the \(t\)-th frame is given by:  
\[\psi(\mathbf{z}_{t},\mathbf{r})=[\mathbf{f}_{1}^{T},\mathbf{f}_{2}^{T}..., \mathbf{f}_{64}^{T}]^{T} \tag{1}\]  
where \(\mathbf{r}\) represents a bounding box in the \(t\)-th frame \(\mathbf{z}_{t}\) and \(\mathbf{f}_{i}^{T}\) is the feature vector of each patch. \(\psi(\mathbf{z}_{t},\mathbf{r})\) is a simple feature descriptor for \(\mathbf{r}\). To assign a weight for each patch, we adopt the MBD to calculate it. By considering these patches as nodes \(V\) and the 4-neighbor links between patches as edges \(E\), the bounding box in the current frame can be represented by a graph \(G(V,E)\). The MBD from a patch to seed set \(S\) reflects the difference between the patch with background. Therefore, our next step is to calculate the MBD between each patch and the boundary patches, while the MBD of each boundary patch is set to 0. Then, the distance is regarded as the weight of each patch, which describes how likely it belongs to the target object.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 5 Results
### Comparative Model Evaluation  
To assess the effectiveness of the proposed model, it was compared with widely used algorithms such as Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNNs), Naive Bayes, Logistic Regression, Decision Tree, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Long Short-Term Memory networks. These models were chosen for their relevance in stock market prediction literature as noted by [PERSON] et al. [46] and [PERSON] et al. [47]. Furthermore, each algorithm was fine-tuned to optimize its parameters, ensuring a fair comparison across different approaches.  
Table 1 presents the accuracy, precision, recall, and F1-score for each model, providing a comparative analysis of their performance metrics. Meanwhile, Table 2 focuses on the ROC curves, highlighting the area under the curve (AUC) as a measure of each model's ability to discriminate between market states.  
The proposed model, LSTM, achieved the highest accuracy (97.20%) and consistently outperformed all baselines across other metrics, such as precision, recall, and F1-score, indicating its robustness in predicting market states.
# Characterization of the Evolution of Energy Loss Rate in Cyclic Load Testing of Marine Soft Soil
## 5 Discussion  
The study presented provides valuable insights into the energy evolution laws of marine soft soil, particularly regarding energy dissipation and failure mechanisms under cyclic loading conditions. However, as with any research, several limitations should be acknowledged:  
(1) Single source of material  
The study primarily relies on data obtained from a single source of marine soft soil. This limits the generalizability of the results across different types of marine soft soil that may exhibit varying characteristics, such as different mineral compositions, particle sizes, and organic content. The findings may not fully represent the diverse range of marine soft soil encountered in various geographical locations or environmental conditions, potentially affecting the accuracy of predictions for projects in different areas.  
(2) Controlled laboratory conditions  
The dynamic triaxial tests were conducted under controlled laboratory conditions, which may not perfectly replicate the full complexity of natural field conditions, such as the influence of groundwater fluctuations, seasonal variations, or long-term loading effects. Real-world conditions, such as changes in soil structure over time, could result in different energy dissipation behaviors, which may not be fully captured in the laboratory-based experiments.  
(3) Simplification of the soil structure  
The study may have simplified the microstructure of marine soft soil for the sake of modeling and analysis. Natural marine soft soil often has heterogeneous and anisotropic structures, which can exhibit more complex behaviors under dynamic loading. The assumptions made in the study may not account for all the intricacies of real-world marine soft soil behavior, which could affect the reliability of the proposed energy loss rate criteria and the stability assessment methods in complex, field-based scenarios.  
(4) Limited focus on long-term behavior  
The research primarily focuses on the immediate response of marine soft soil under cyclic loading conditions, without addressing long-term soil behavior under sustained or repeated loading over extended periods. This is important for assessing the durability and performance of infrastructure such as tunnels and foundations over their operational lifespans. The study's results may not fully capture the long-term deformation and failure mechanisms that can influence the stability of infrastructure in the long run, particularly under heavy, continuous traffic loads or extreme weather events.
## 2 Blood Pressure Estimation Methods
### LSTM Neural Networks  
After the meaningful features are extracted, the LSTM is adopted to model the characteristics of the time series of features. The structure of the LSTM unit is shown in Figure 3. The inputs of the first LSTM layer and the other LSTM layers are the output of attention layer \(SA_{i}\) and the outputs of previous LSTM units, respectively. In time slot \(t\), the input and the output of the LSTM unit are \(y_{t}\) and \(h_{t}\), respectively. For an LSTM unit, it composes an input gate \(g^{i}\), a forget gate \(g^{f}\), an output gate \(g^{o}\), a mapping function \(m_{t}\), and a memory cell \(c_{t}\).  
Figure 3: Structure of the LSTM unit.  
Figure 2: Block diagram of the self-attention mechanism.  
Mathematically, each vector in these components of the LSTM unit can be computed as follows.  
\[Y=y_{t}\oplus h_{t-1} \tag{6}\]  
\[g_{t}^{i}=\sigma\Big{(}W^{i}\cdot Y+b^{i}\Big{)} \tag{7}\]  
\[g_{t}^{f}=\sigma\Big{(}W^{f}\cdot Y+b^{f}\Big{)} \tag{8}\]  
\[g_{t}^{o}=\sigma(W^{o}\cdot Y+b^{o}) \tag{9}\]  
\[m_{t}=tanh(W^{m}\cdot Y+b^{m}) \tag{10}\]  
\[c_{t}=g_{t}^{f}\odot c_{t-1}+g_{t}^{i}\odot m_{t} \tag{11}\]  
\[h_{t}=g_{t}^{o}\odot tanh(c_{t}) \tag{12}\]  
\(\sigma(\bullet)\) and \(\odot\) are the sigmoid function and elementwise multiplication. \(W^{i}\), \(W^{f}\), \(W^{o}\), and \(W^{m}\) are the weighted matrices, and \(b^{i}\), \(b^{f}\), \(b^{o}\), and \(b^{m}\) are bias vectors of LSTM. These parameters are learned during training. Three gates have their weights and then each LSTM unit works like a state machine. Therefore, the LSTM neural network can deal with sequence problems.
## 4 Experiments
### Experimental Settings
#### 4.1.1 Experimental Dataset  
Our experiments revolve around two distinct industrial applications: wood defect examination, utilizing the wood category within the MVTec AD dataset [15], and freight train monitoring, employing the TFDS-RP dataset.  
The MVTec anomaly detection dataset is a widely recognized and publicly available industrial defect inspection dataset. Within the MVTec AD dataset, the wood category stands out, comprising 247 training images and 79 test images, all with a resolution of 1024 \(\times\) 1024 pixels. Abnormal images in the testing dataset cover five types of defects: color, hole, liquid, scratch, and combined. These defects manifest as conspicuous alterations in texture, presenting varying sizes.  
In contrast, the TFDS-RP dataset introduces a more complex application environment. This dataset consists of authentic images captured during the operation of railway freight trains. These images have been cropped to 128 \(\times\) 128 pixels, focusing on a specific circular component, namely a brake pad locking ring. The absence or deformation of this component could potentially compromise the safety of the running vehicle, making it a relatively high-frequency fault. The TFDS-RP dataset comprises 387 training images and 100 test images, encompassing two fault types: locking ring deformation and locking ring loss.  
Notably, images within the TFDS-RP dataset originate from outdoor settings by track-side cameras. Consequently, these images exhibit a wider range of lighting conditions and positional variations, rendering fine-grained detection significantly more challenging.