esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adversarial Deep Hybrid Model for Text-Aware Recommendation with Convolutional Neural Networks
## 5 Conclusions  
In this paper, we propose an adversarial training framework to learn a deep hybrid recommendation model, which seamlessly integrates a Convolutional Neural Network (CNN) into latent factor model to exploit deep content features as well as the collaborative patterns from user-item interactions. Specifically, our main work can be concluded as follows.  
First, we propose a hybrid recommendation model that incorporates a Convolutional Neural Network (CNN) to learn the deep text features. As CNN is an order-sensitive textual feature extractor, it offers the benefit of exploiting the additional information in word orders.  
Second, with the aim of reflecting the ranking nature of top-\(N\) recommendation, we propose an adversarial learning framework to capture the pair-wise ranking distribution over user-item interactions in an effective way.  
Finally, we have conducted extensive experiments on three real-world datasets to demonstrate that our proposed model is able to deliver the state-of-the-art performance across both warm-start and cold-start recommendation settings.  
Conceptualization, X.Z., D.D.; Methodology, X.Z. and D.D.; Software, X.Z. and D.D.; Validation; X.Z., D.D.; Formal analysis, Z.Z. and D.D.; Investigation, X.Z.; Resources, X.Z.; Data Curation, X.Z.; Writing-original draft preparation, X.Z. and D.D.; Writing-Review& Editing, Z.Z. and D.D.; Visualization, D.D.; Supervision, X.Z.; Project Administration, X.Z.; Funding Acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China (No. 2018 YFB1403001), the National Natural Science Foundation of China (No. U1509221), and the Zhejiang Provincial Key R&D Program, China (No. 2017C03044).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript (in alphabetical order):  
\begin{tabular}{l l} ADHR & Adversarial Deep Hybrid Recommendation \\ BPR & Bayesian Personalized Ranking \\ CDL & Collaborative Deep Learning \\ CNN & Convolutional Neural Network \\ CTR & Collaborative Topic Regression \\ CVAE & Collaborative Variational Auto-Encoder \\ DL & Deep Learning \\ GAN & Generative Adversarial Net \\ LDA & Latent Dirichlet Allocation \\ MF & Matrix Factorization \\ \end{tabular}  
Figure 7: Saliency of each word in a review regarding an instant video in the Amazon dataset. Size and color of the words indicate the influence on final predicted score. The important chunks of words such as “great” and “footage” are learned by the model.
## 4 Simulation Result Analysis and Discussion
Regarding the NLOS signal recovery ratio, the data reveal that with 10 satellites, recovering half of  
Figure 10: GDOP with under different virtual satellite settings below 60\({}^{\circ}\) (10/20) NLOS condition: half of the satellites are set as virtual, one-quarter of the satellites are set as virtual. Figure 9: GDOP under different virtual satellite settings below 60\({}^{\circ}\) NLOS condition: half of the satellites are set as virtual, one-quarter of the satellites are set as virtual. the NLOS signal slightly outperforms recovering a quarter. However, with 20 satellites, this difference becomes less pronounced, implying that a larger number of satellites can partially mitigate the effect of the NLOS recovery ratio. The algorithm performs best when 20 satellites are used and a quarter of the NLOS signal is recovered, yielding the lowest GDOP of 0.83989 and corresponding to a root mean square error (RMSE) of 6.47 m. In contrast, the worst performance occurs with 10 satellites and a quarter of the NLOS signal, resulting in a GDOP of 2.05149 and corresponding to an RMSE of 15.80 m. Notably, GDOP values remain stable over time, showing no significant temporal trends. These findings underscore the potential of the proposed method for high-accuracy GNSS positioning in challenging environments, particularly where NLOS signals dominate, such as urban canyons. The algorithm's ability to maintain acceptable accuracy even when only partial NLOS signal recovery is achieved is crucial for practical applications. According to Equation (9), the 3D position error is influenced by both the user distance error and the geometric dilution factor (GDOP). Specifically, the user distance error reflects measurement inaccuracies, while GDOP quantifies how the satellite geometry amplifies positioning errors. While introducing inverted virtual NLOS satellite signals adds some errors, it significantly reduces the amplification effect on positioning errors by optimizing the GDOP (from over 10 to a mean of less than 1.42). Thus, the introduction of additional satellites compensates for the errors from virtual NLOS signals and greatly improves the accuracy of 3D position estimation in urban canyon environments.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 2 Materials and Methods
### Classification Methods for Detecting Moldy Maize Kernels
#### 2.4.2 Image Preprocessing  
In order to mitigate the order of magnitude difference between the dimensional data and prevent significant errors in the prediction model resulting from the substantial difference in the order of magnitude between the input and output data, grayscale images were normalized before training and the dimensions were adjusted to 224 \(\times\) 224 pixels. In order to prevent overfitting of the deep learning model caused by the limited dataset size, the existing corn kernel dataset was augmented to enhance training diversity. This was achieved through data augmentation techniques such as image rotation, flipping, and translation. The preprocessing flowchart is illustrated in Figure 3. Image rotation: the image was randomly rotated between 0 and 360 degrees. Image flip: the image was randomly flipped around the X-axis and the Y-axis. Image panning: the image was panned along the X-axis and the Y-axis, with a pixel interval of the panning interval (\(-\)20-20). Figure 3 shows the progression of an original corn kernel slice image through normalization, rotation, flipping, and panning.
# Compact Dual-Band Tapered Open-Ended Slot-Loop Antenna For Energy Harvesting Systems
## 4 Conclusions  
This study has explained a new method to design compact, high-efficient, dual-band combined antenna that can work simultaneously at lower band 0.92 GHz and higher band 2.4 GHz. The antenna is based on a simple combined OEL and OES. The structure has low-cost fabrication and A single-feed network. It is demonstrated that the overall antenna gain is 1.8 dBi and 4.2 dBi at 0.92 and 2.4 GHz, respectively. The antenna is designed and optimized to capture the energy from the ambient environment at the radio frequency range of ISM 900 MHz and Wi-Fi bands 2.45 GHz. This antenna is also highly desirable for system flexibility. Because the proposed antenna has compact architecture, it can be considered as an important candidate for low-power RF energy harvesting. The planar structure is promising for compact wireless devices and it can find applications in wireless energy harvesting.  
Conceptualization, M.M. and K.S.; methodology, M.M. and K.S.; software, M.M. and K.S.; validation, M.M. and H.K.; formal analysis, M.M.; investigation, M.M.; resources, H.K.; data curation, M.M.; writing--original draft preparation, M.M.and K.S.; writing--review and editing, H.K.; visualization, M.M.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K.; All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Cabinet Office (CAO), Cross-ministerial Strategic Innovation Promotion Program (SIP), \"An intelligent knowledge processing infrastructure, integrating physical and virtual domains\" (funding agency: NEDO), SCOPE, and KAKENHI (18K04146) JSPS.  
Authors would like to thank VLSI Design and Education Center (VTEC), the University of Tokyo in collaboration with CADENCE Corporation and Keysight Technologies who provided the simulation software and tools for simulations and results verification. Moreover, we would like to thank our colleagues at the Electronic Devices Lab. in Kyushu university for the fruitful discussion and consultations  
The authors declare no conflict of interest.
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 4 Result and Discussion
### Running Time  
In Figure 12, we test the running time of different methods on the same platform, and we find that the average running time of some classic and effective methods (algorithm of [13; 15]) is about 30-40 ms, but the average time of our proposed method is 15.53 ms. Most of the running time can be controlled within 20 ms. From the running time curve, we can infer that the stability of our algorithm performs well, and running time does not change greatly with the change of scene.  
Our algorithm not only improves the segmentation standard but also reduces the running time because of the search structure. [PERSON] directly uses the search algorithm on the range graph, which needs to search every point, consuming a lot of time. [PERSON] proposes to segment in different directions, and then use two range maps to update the category. However, in the vertical direction, the relationship between each pair of adjacent points must be judged, which takes some time.  
In order to better test the generality of the clustering algorithm, we test the following four different LiDARs: Robosense RS-LiDAR-16, Velodyne HDL-32E, Velodyne HDL-64E, Robosense RS-Ruby (128 channels). These LiDARs have 16, 32, 64 and 128 laser channels, respectively, and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distance** & **Less 15 m** & **15–20 m** & **20–25 m** & **25–30 m** & **Over 30 m** \\ \hline Bogoslavskyi & 0.954 & 0.932 & 0.904 & 0.893 & 0.864 \\ [PERSON] & 0.970 & 0.955 & **0.948** & 0.907 & **0.886** \\ Proposed & **0.976** & **0.959** & 0.946 & **0.916** & 0.874 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The scene accuracy of different methods at different distances.  
data volume increases linearly. It can be concluded from Figure 13 that the running time of our proposed method is nearly linear with the increase of data volume. In the first traversal, the time complexity is \(O(N)\), the time increases linearly with the data. The second traversal is on the set graph, the improved BFS algorithm is used, and the time complexity is \(O(N+E)\). However, in the second traversal, the calculation times are related to the number of nodes and edges. In the worst case, all single points are nodes, and the time increases linearly with the amount of data. Therefore, the method we proposed is a linear algorithm; it can be used in a variety of LiDARs.
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 5 Simulations and Results
### Nonlinear Channel Equalization
#### 5.2.2 Abruptly Changed Channel Equalization
The KLMS-RFN (\(M\) = 100) achieves the lowest MSE; however, the computational complexity of KLMS-RFN with 100 dimensions is similar to that of the QKLMS-FB. Comparing the aspect of run time, the KLMS-RFN algorithm needs less time in training than the compared algorithm, as shown in Figure 14. After 12,000 iterations, the total run times of the QKLMS, QKLMS-FB and KLMS-RFN in abrupt change channel equalization are 0.3342 s, 1.2349 s, 0.1277 s and 0.1671 s, respectively. Figure 12: The learning curves of the QKLMS, QKLMS-FB, and KLMS-RFN algorithms in channel equalization with an abrupt change in condition. Figure 13: The network size growth curves of the QKLMS and QKLMS-FB algorithms in channel equalization with an abrupt change in condition.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 2 Challenges Posed by the Current Healthcare System
### Lack of a Data-Driven Culture  
In the current healthcare paradigm, patients are assessed on population averages and data-poor practices, and it is practically not possible to conduct standard parallel group Randomized Controlled Trials (RCTs). Moreover, clinical evidence generated by non-standard RCTs has poor generalizability, therefore, having limited applicability to patients, and even is also restricted in comparing the effectiveness of drugs and medical devices [15]. In such a data-poor system, the medication doses are often over-or under-estimated, which can result in adverse drug reactions. This represents a crucial missed opportunity to embrace the development of intelligent and evidence-based healthcare solutions. These solutions can provide and apply the best evidence-based care to each patient; discover the natural outgrowth of patient care; and also ensure innovation, safety, quality and added value to the healthcare system.
## 4 Results and Discussion
### CGCW Performance Analysis
#### 4.2.3 Influence of the Cut-Off Wall Hydraulic Coefficient  
The influence of the cut-off wall hydraulic coefficient (\(k_{G}\)) on the breakthrough curve of the CGCW is shown in Figure 9. The \(H_{G}\), \(r_{G}\) and \(T_{G}\) are assumed to be 3.0 m, 0.05 m and 0.0015 m, respectively. Increasing the \(k_{G}\) can result in a worse performance of the CGCW. For example, the breakthrough time for \(k_{G}=10^{-10}\) m/s is 1.3 and 1.5 times larger than that for \(k_{G}=10^{-10}\) m/s.  
Figure 8: Influence of the hole shape on the breakthrough curve.  
Figure 7: Influence of the hole radius on the breakthrough curve.  
for \(k_{\rm G}=5\times 10^{-10}\) m/s and 1 \(\times\) 10\({}^{-9}\) m/s, respectively. This is mainly because decreasing \(k_{\rm G}\) can significantly decrease the leakage. This has also been mentioned in Section 4.1. Additionally, it can be concluded that \(k_{\rm G}\) has little effect on the CGCW performance when it is small (e.g., \(<\)10\({}^{-10}\) m/s). The breakthrough curves of \(k_{\rm G}=10^{-10}\) m/s and 10\({}^{-11}\) m/s are almost coincident. The breakthrough time for \(k_{\rm G}=10^{-11}\) m/s is only 1.06 times larger than that for \(k_{\rm G}=10^{-10}\) m/s. The dispersion can be the dominant mechanism for contaminant transport as the Peclet number in the cut-off wall is lower than 1 (e.g., \(<\)0.26) when \(k_{\rm G}<10^{-10}\) m/s. Thus, there is a threshold for \(k_{\rm G}\) in the CGCW (e.g., \(10^{-10}\) m/s) that can not only satisfy economic and safety needs. In other words, decreasing \(k_{\rm G}\) cannot help much to the CGCW performance when it is lower than the threshold. It can be very important to find the threshold in the design of the CGCW.
# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 2 Methods  
### Composite Pavement Performance Prediction  
The occurrence times and deterioration rates of different pavement diseases vary because of such influences as pavement structure, climate, and traffic. The pavement condition index (_PCI_) was proposed to quantify the comprehensive surface performance of pavements [30]. The _PCI_ can be used as an auxiliary decision-making indicator of long-term preventive maintenance. [PERSON] et al. [31] proposed the following _PCI_ predictive model for asphalt pavements:  
\[PCI = PCI_{0}[1-e^{-(\frac{\pi}{6})^{6}}] \tag{1}\] \[\alpha = \lambda[1-e^{-(\frac{\pi}{6})^{6}}]\] (2) \[\lambda = a_{1}h^{b1}ESAL^{c1}\] (3) \[\eta = a_{2}h^{b2}ESAL^{c2}\] (4) \[\zeta = a_{3}h^{b3}ESAL^{c3}\] (5) \[\beta = a_{4}h^{b4}ESAL^{c4}l_{0}^{d} \tag{6}\]  
where \(PCI_{0}\) is the initial _PCI_, which is usually 100 when construction or rehabilitation is completed; \(y\) is the pavement age; \(\alpha\) is the pavement service life factor; \(\beta\) is the deterioration curve shape factor; \(l_{0}\) is the initial pavement deflection (0.01 mm); \(h\) is the thickness of the asphalt layer (cm); \(ESAL\) is the daily equivalent single axle loading repetitions based on the B737-800 aircraft (/ day); and \(a_{n}\), \(b_{n}\), \(c_{n}\) (\(n\) is 1, 2, 3, 4) and \(d\) are the regression coefficients.  
The structures of asphalt pavement and composite pavement differ, as do their deterioration curves. This article replaces \(h\) in Equations (3)-(6) with \(h_{e}\), which can be calculated as follows [32]:  
\[h_{e}=\left(0.4C_{F}h_{F}+C_{R}h_{R}\right)/F \tag{7}\]  
where \(h_{e}\) is the equivalent thickness of composite pavement (cm); \(C_{F}\) is the thickness discount coefficient of asphalt concrete (AC) overlay; \(h_{F}\) is the thickness of AC overlay (cm); \(C_{R}\) is the thickness discount coefficient of portland cement concrete (PCC) pavement; \(h_{R}\) is the thickness of PCC pavement (cm); and \(F\) is the crack control coefficient of PCC pavement. \(F\) is calculated as follows [33]:  
\[F=\left(0.08534\frac{n_{s}}{100}-0.3594k_{0}+106.2946\right)/100 \tag{8}\]  
where \(n_{s}\) is the annual sorties of aircraft with take-off speeds faster than 140 knots (/ year), and \(k_{0}\) is the modulus of subgrade reaction (MN/ m\({}^{3}\)).
# Deep Learning within a DICOM WSI Viewer for Histopathology
## 3 Results
### Models
#### 3.2.3 Ki67 Model  
The Ki67 model was trained to classify samples into six classes. One of the classes represented patches from the background of the image, whereas the rest of them defined an interval corresponding to the percentage of Ki67-positive tumor cells. Similarly to the other datasets, all WSIs were annotated by pathologists, giving each region a Ki67 labeling index (percentage score). The training patch size was also 299 by 299 pixels and the dataset was divided into training, validation and test subsets, preserving all patches from the same WSI in the same set (see Table 6).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Set** & **[0–10\%]** & **[10–20\%]** & **[20–45\%]** & **[45–85\%]** & **[85–100\%]** & **Background** \\ \hline Train & 1933 & 3386 & 1551 & 620 & 639 & 640 \\ Val & 442 & 741 & 345 & 143 & 130 & 145 \\ Test & 451 & 734 & 325 & 130 & 141 & 163 \\ \hline Total & 2826 & 4861 & 2221 & 893 & 910 & 948 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ki67 dataset split.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **AN** & **GN** & **VGG** & **RN** & **DN** \\ \hline
0 & 0.94 & 0.95 & 0.93 & **0.95** & **0.95** \\
1 & 0.80 & 0.78 & **0.84** & 0.83 & 0.77 \\
2+ & 0.76 & 0.73 & 0.71 & 0.75 & **0.82** \\
3+ & 0.85 & 0.82 & 0.73 & **0.89** & 0.80 \\ \hline Average & 0.84 & 0.82 & 0.80 & **0.86** & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: HER2 model results (sensitivity).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Set** & **Benign** & **Malignant** & **Background** \\ \hline Train & 39,898 & 35,878 & 3550 \\ Validation & 5120 & 9279 & 700 \\ Test & 5241 & 10,153 & 700 \\ \hline Total & 50,259 & 55,310 & 4950 \\ \hline \hline \end{tabular}
\end{table}
Table 4: H&E dataset split.  
In this case, five CNN architectures were trained: ResNet18, GoogLeNet, InceptionV3, DenseNet121 and VGG11. The best results were obtained by InceptionV3, with average accuracy of 87%. Table 7 shows the results obtained. Models that use joint information from the H&E image, as we described in [37], can improve these results by up to 95%. Nevertheless, we have not integrated this method because both samples (Ki67 and H&E) are not always available.
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 2 Related Works
### Abnormal Early Warning  
Early warning is forward-looking compared to anomaly location; predicting the operational state of network or the faults of industrial equipment, which can improve the efficiency of maintenance and reduce loss. Currently, warning algorithms focus on traditional statistical-based logistic regression [27; 28; 29] as well as mathematical prediction techniques in which fuzzy theoretical models [30], and grayscale models [31] are also widely used.  
However, owing to the complexity of prediction scenarios and the explosive increase in data volume, researchers are more inclined toward data-driven intelligent prediction techniques [8; 32; 33; 34; 35]. [PERSON] et al. [8] proposed an improved intelligent early warning method based on moving window sparse principal component analysis (MWSPCA) applicable to complex chemical processes. The sparse principal component analysis algorithm was used to build the initial warning model, which was then updated using moving windows to make the warning model more suitable for the characteristics of time-varying data. [PERSON] et al. [32] proposed a coding and decoding model based on LSTM networks. This method models the time dependence of the time series through LSTM networks and achieves a better generalization capability than traditional methods. Related studies [33; 34] proposed a deep belief network fault location (DBN-FL) model based on a deep trust network, which sets a series of fault rule identification templates based on historical fault data, integrated data analysis results, and expert experience in the fault identification process. The probability of fault occurrence is obtained by matching the real-time data distribution with the fault rules. In this study [35], an implementation method for abnormal early warning technology based on the probability distribution and density estimation method was proposed, which calculates the probability value of feature distribution by extracting data features and then distinguishes fault data from normal data according to the probability value. The performance of logistic regression and machine learning methods was evaluated in this study. The results show that these methods effectively improve the prediction accuracy relative to traditional networks [36].
## 4 Experiments
### Comparison to State-of-the-Art Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **RMSE (mm)** & **MAE** & **iRMSE** & **iMAE** \\ \hline Sparse-to-Dense [6] & 954.36 & 288.64 & 3.21 & 1.35 \\ Sparse-to-Dense (SS) [9] & 814.73 & 249.95 & 2.80 & 1.21 \\ CSPN [7] & 1019.64 & 279.46 & 2.93 & 1.15 \\ Nonv-CNN [45] & 829.98 & 233.26 & 2.60 & 1.03 \\ DeepLiDAR [12] & 758.38 & 226.50 & 2.56 & 1.15 \\ NLSPN [8] & 741.68 & 199.59 & 1.99 & 0.84 \\ Ours & 742.24 & 198.26 & 2.01 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with state-of-the-art methods based on the KITTI DC test dataset. This mechanism enhances the accuracy of the teacher model's training in pseudo-labels and the generalization of the student models. Moreover, our proposed dense-pixel-wise KD module retains the advanced capabilities demonstrated in previous iterations. It preserves and progressively enhances the knowledge acquired in earlier stages, strengthening its overall robustness and effectiveness. Consequently, our few-shot learning framework enhances the performance of the original NLSPN model while substantially reducing the need for labeled data. By strategically incorporating noise and leveraging KD, the student model acquires a more robust understanding, yielding improved generalization. Therefore, the student model surpasses the teacher model, showcasing the effectiveness and scalability of our approach for efficient depth completion.