esa-sceva/satcom-chunk-collection huggingface dataset:
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth-Sensitive Feature Enhancement
#### 3.2.3 Depth-Sensitive Prototype Representation Module  
This module aims to refine feature representations through depth prototype learning and enhancement. Starting from the initial depth-sensitive feature map \(F_{init}\in R^{C\times H\times W}\), generated by the Backbone network, the module predicts the depth distribution \(P_{depth}\in R^{D\times H\times W}\) for each pixel, where \(C\) is the feature dimension, \(H\) and \(W\) are spatial dimensions, and \(D\) represents the number of depth bins. We extract spatial features \(F_{init}[i]\) from the input image and match them with their corresponding depth information using a trainable attention mechanism to obtain \(Attention(i,d)\). This attention weight measures the confidence of pixel i at different depth ranges, allowing the network to adaptively emphasize relevant depth features without relying on manually designed priors.  
The predicted probability represents the confidence of each pixel belonging to a specific depth bin, which is computed using softmax normalization. The ADF module employs D-Convolution (D-Conv) and Grouped Convolution (G-Conv) to refine depth-sensitive features. D-Conv expands the receptive field without increasing parameters by using dilation rates, while G-Conv reduces computational complexity by performing convolution in separate channel groups. Using the predicted depth distribution, the module estimates depth prototype representations \(P[d]\in R^{C}\) for each depth bin d by aggregating features across all pixels:  
\[P[d]=\sum_{i=1}^{N}Attention(i,d)\times F_{init}[i],d\in\{1,\dots,D\}. \tag{6}\]  
where \(P[d]\), \(N=H\times W\) represents the total number of spatial positions, and \(Attention(i,d)\) denotes the attention weight for pixel \(i\) and depth bin \(d\), derived from the similarity between \(F_{init}\) and \(P_{depth}\). All pixels contributing strongly to depth \(d\) are combined into a single global depth prototype \(P[d]\). Intuitively, the model scans the entire image to collect spatial cues most representative of depth \(d\). If certain pixels are more likely to be at depth \(d\), they receive higher weights and thus dominate the resulting prototype.
## 4 Research on Critical Technologies
### Atmospheric Channel Modeling
#### 4.5.2 Gamma-Gamma Distribution  
Since the log-normal distribution is limited to weak turbulence, [PERSON] proposes a _Gamma-Gamma channel model_ based on the modified Rytov theory, in which light intensity fluctuations are developed as a product of two stochastic processes, with the probability density function expressed in terms of the following:  
\[f_{l,G}(I)=\int_{0}^{+\infty}p_{y}(IX)P_{X}(X)dX=\frac{2(u\beta)^{(\alpha+ \beta)/2}}{\Gamma(u)\Gamma(\beta)}I^{(\alpha+\beta)/2-I}B_{\alpha-\beta} \Bigl{[}2(\alpha\beta I)^{1/2}\Bigr{]},I\succ 0 \tag{2}\]  
where \(u\) and \(\beta\) denote the large- and small-scale scattering coefficients, respectively, and \(B(\alpha-\beta)\) (\(\cdot\)) is the second type of modified Bessel function.
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 7 Findings and Future Direction  
### Findings from Existing Works  
* The currently used BTS use the approaches based on bug reports to implement deduplication and triage, which is mainly determined by the ease of obtaining and transmitting bug reports. The biggest obstacle to using runtime information-based methods lies in the complete acquisition and format conversion of runtime information, which is also a possible research direction in the future. The similarity measurement used by BTS also generally requires more accurate text matching, which also reduces the effectiveness of automatic deduplication and requires more human resources to complete accurate deduplication and triage.
* Stack trace hash has been widely used in many works(\(\sim\)50%) due to its ready availability and general benefits. It is helpful in identifying root causes and facilitating quick scenario reconstruction and has a certain level of usability. However, its accuracy in determining bug uniqueness is not high. For example, different paths leading to the same crash point may result in splitting of what should be considered the same bug into different ones. Similarly, identical call sequences with different specific values may result in grouping of bugs that should be considered different.
* Relying solely on coverage information is also inaccurate. This is mainly because there may be new execution paths unrelated to triggering the bug, which can lead to different coverage information for the same bug.
* When using runtime information of a program for bug deduplication and triage, false positives may occur because the same bug may exhibit different crash points, error messages, etc.
* In works based on information retrieval, the main sources of information include the bug's basic attributes, crash dumps, stack traces, etc. Among them, stack trace is the most important analysis component, and almost all works(over 90%) refer to it to some extent.
* In works using machine learning methods based on bug reports, the basic approach aligns well with NLP processing approaches. Therefore, most of these works(\(\sim\)67%) utilize neural network models such as LSTM and CNN. The key input for such works is the textual description information in the bug report. In some works(\(\sim\)33%), the abilities of developers are also modeled and extracted as features to enhance the model's recognition capability.
* More than 90% of works use open-source databases as test objects. For authors belonging to certain companies, in addition to public, open-source datasets, they also use the company's datasets, such as JetBrains, Ericsson, etc.
* Generally, works based on runtime information tend to have better performance compared than those based on bug reports, but they also come with greater overhead. This is mainly because the accuracy of bug reports cannot be fully guaranteed.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
## 3 Method
### Face Model Super-Resolution  
#### 3.3.1 Registration of Radial Curves  
Registration of radial curves is to establish a point correspondence between two radial curves. Dynamic Time Warping (DTW) [15] is one method for finding an optimal match and measuring similarity between two temporal sequences in time series analysis. It has been applied to analyze any data that can be transferred into a linear sequence, such as temporal sequences of video, audio and graphics data. Here radial curve registration and radial curve match are realized by using DTW of two point sequences of two radial curves. In order to keep the central feature area of human faces for radial curve match, we eliminate the boundary area by cropping all the radial curves in the following way: keep 40 points in both directions centered on the nose tip point. After cropping, each radial curve has 81 sampling points at equal intervals. The point number 40 is set by the distribution of face features. Of course, it can vary in a wide range; for example, 38 or 42 are also good selections, since they will have little influence on the radial curve match as long as the sampling points cover the central face feature area.  
For point sequences of two radial curves, DTW is used to align them and measure their similarity. In order to eliminate the pose effect, we use the curvature difference of the two curve points to calculate their local match cost in DTW. For example, the curvature of the point \(a_{n}\) can be calculated as follows:  
\[(a_{n})_{K}=\frac{1}{\rho(a_{n-1},a_{n},a_{n+1})},1<n<N \tag{1}\]  
Figure 3: The correspondence of radial curves from (**a**) the low-resolution model and (**b**) the high-resolution model.  
where \(\rho\) is radius of curvature; i.e., \(\rho(d,e,f)\) is radius of circle determined by \(d,e,\text{and}\ f\). Assume the matching point pair of two curves \(X\) and \(Y\) is \((x_{i},y_{i}),i=1,...,N\); then the match cost of the two curves is:  
\[\text{DTW}(X,Y)=\sum_{i=1}^{N}|(x_{i})_{\kappa}-(y_{i})_{\kappa}| \tag{2}\]
# PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification
## 5 Experiment
### Algorithms  
We compare our proposed multi-modal fusion method with multiple well-known classification and embedding methods such as:  
* **ResNet50:** Residual Network [26] is widely used in the image classification field and as part of the backbone of neural networks in computer vision tasks.
* **DenseNet121:** The DenseNet model [27] alleviates the problem of gradient disappearance, strengthens feature propagation, and reduces the number of parameters.  
\begin{table}
\begin{tabular}{c l l c c c} \hline \hline
**Dataset** & **Subset** & **Class** & **Text** & **Figure** & **Multi.** \\ \hline PaperNet\_2 & & CV & 1863 & 12,774 & 25,548 \\  & & NLP & 2538 & 6609 & 13,060 \\ \hline Average & & Coarse-grained class & 2200.5 & 9691.5 & 19,304 \\ \hline PaperNet\_20 & PaperNet\_CV & CV\_attention & 201 & 1157 & 2314 \\  & & CV\_classification & 387 & 1951 & 3902 \\  & & CV\_detection & 621 & 4280 & 8560 \\  & & CV\_GAN & 228 & 1932 & 3864 \\  & & CV\_recognition & 284 & 1432 & 2864 \\  & & CV\_retrieval & 82 & 270 & 540 \\  & & CV\_segmentation & 260 & 1752 & 3504 \\ PaperNet\_NLP & NLP\_bert & 372 & 1215 & 2430 \\  & & NLP\_convensation & 262 & 698 & 1396 \\  & & NLP\_cross & 277 & 576 & 1152 \\  & & NLP\_extraction & 340 & 681 & 1362 \\  & & NLP\_Few\_shot & 119 & 309 & 614 \\  & & NLP\_knowledge\_graph & 82 & 244 & 488 \\  & & NLP\_machine\_reading & 59 & 104 & 208 \\  & & NLP\_machine\_translation & 490 & 832 & 1664 \\  & & NLP\_multilingual & 253 & 557 & 1114 \\  & & NLP\_multimodal & 145 & 829 & 1656 \\  & & NLP\_named\_entity\_recognition & 105 & 189 & 378 \\  & & NLP\_sentiment\_analysis & 172 & 179 & 358 \\  & & NLP\_text\_generation & 100 & 121 & 240 \\ \hline Average & Fine-grained class & 241.95 & 965.4 & 1930.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of PaperNet datasets (Multi. means the pre-processed multimodal data).  
* **MobileNetV3:** MobileNetV3 [28] is a light-weight model. Combined with network design and NAS technology, a new generation of MobileNets is proposed.
* **ULMFiT:** This is a general language model based on fine tuning (ULMFiT), which can be applied to a variety of tasks in NLP [29].
* **Albert:** Albert model from paper [30]. Compared to BERT, Albert achieves better results with fewer parameters. We use the Albert model as part of our proposed model for text encoding.
* **Concat:** Previous work [25] concatenates different feature vectors of different modalities as the input of the classification layer. We implement this concatenation model with our feature vectors of different modalities and apply it for classification.
## 7 Conclusions
4. the TECCs present a strong reduction of the order of magnitude of the Damage Index (DI) with respect to MFCCs and similar options, in absolute terms; this does not affect the damage detection algorithm, as the relative distance between the 'normality' model and the damaged cases remains and seems even to increase;
5. the Teager-Kaiser Energy Cepstral Coefficients outperforms all the competitors in terms of little or no type 1 errors, but it is slightly more prone to type 2 errors respect to MFCCs and derived features;
6. with respect to non-cepstral features, such as the AR coefficients, the main benefits lie in the less computational cost and greater robustness to noise and to confounding influences, such as environmental and operational effects, unrelated to damage. The outcomes of this research leave plenty of room for improvements. Indeed, apart from the TECCs and other cepstral-based alternatives, wavelet-based alternatives are of great interest. The Mel-Frequency Discrete Wavelet Coefficients may be a relevant alternative. Thanks to the Discrete Wavelet Transform, the filter spacing issue is by-passed; filter shaping is reduced to the selection of the mother wavelet. The Authors are committed to pursuing further studies in this direction. Conceptualization, M.C., M.F., R.C., C.S. and R.B. ; Data curation, M.C. and M.F. ; Formal analysis, M.C. and M.F. ; Funding acquisition, R.C. and C.S. ; Investigation, M.C. and M.F. ; Methodology, M.C., R.C., C.S. and R.B. ; Project administration, R.C., C.S. and R.B. ; Resources, R.C., C.S. and R.B. ; Software, M.C., M.F. and R.B. ; Supervision, R.C., C.S. and R.B. ; Validation, M.C. and M.F. ; Visualization, M.C. ; Writing--original draft, M.C. and M.F. ; Writing--review & editing, M.C., R.C., C.S. and R.B. This research was funded by the INTE project by Compagnia diSan Paolo (2017-18) \"System Identification, model updating and damage assessment of heritage buildings and structures\", as a joint partnership of Politecnico di Torino (IT) and Columbia University (USA). The Authors wish to thank [PERSON] and [PERSON] for their precious help and advice. The Authors also wish to acknowledge the Engineering Institute at Los Alamos National Laboratory for making available to the public domain the experimental data used in this work. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion  
In this survey, 77 studies were included in a systematic review of real-time object detection based on UAV remote sensing. These studies covered different application scenarios and were analyzed in terms of hardware selection, algorithm development, and improvement to achieve real-time detection over the past 20 years. In summary, real-time object detection is a technology that is increasingly in demand in UAV remote sensing because of its advantages related to higher detection efficiency and faster response times. In this section, the research challenges and the outlook for realizing UAV real-time object detection are discussed.
## 3 Issues in Implementing URLLC
### URLLC Packet Design  
Packet design is one of the key issues in URLLC. With an effective packet structure, the latency can be minimized in terms of packet processing time and packet transmission time [15]. Packet processing involves the time of acquiring a packet, accessing channel information, extracting scheduling (control) information, decoding the packet, and checking errors. As per URLLC requirements, the 5G NR system employs a non-square-shaped packet in the frequency domain with polar code for the control channel and low-density parity-check (LDPC) for the data channel to minimize the transmit latency. However, in LTE, a square-shaped packet is generally utilized for effective spectrum utilization [19].
# Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
## 5 Conclusions  
In this paper, the authors designed a miniaturized antenna array for MIMO applications. The proposed antenna parameters were optimized to fit the applications of sub-6 5G networks in terms of mutual coupling reduction, bandwidth enhancements, and gain increase. Therefore, the authors applied in the proposed design several techniques to realize a highly miniaturized antenna array with excellent reconfiguration mechanisms. For this, a monopole fundamental antenna element is conjugated with a meander line and interdigital capacitor to increase the antenna radiation efficiency with low surface wave retardation. In such a manner, fitting the proposed antenna element in a single array could be an excellent addition to MIMO systems by minimizing the separation distance between them using a metamaterial structure. The proposed metamaterial was found to realize a significant mutual reduction of almost \(-20\) dB over the frequency band from 3 up to 5.5 GHz with a separation distance of \(0.08\,\lambda\). This antenna array was tested with different configuration scenarios by adding four LDR switches to each antenna element through the meander line and the ground plane. We realized that such a reconfiguration process takes advantage of low complexity without including wiring or biasing systems. It was found that when exposed to a massive MIMO environment in the proposed antenna array, acceptable BER and CC can be achieved after making the antenna size theoretically 64\(\times\)64 elements. It is concluded from this study that the proposed antenna array is a very suitable candidate for 5G massive MIMO systems at the sub-6 GHz frequency band. Finally, for validation, an experimental study was applied to the proposed array to test the antenna performance in terms of S-parameters and radiation patterns.  
Conceptualization, H.H.; methodology, F.A.; software, T.A.E.; validation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Correction and Calibration
#### 2.3.4 Validation of the Attenuation Measurement  
The module of \(c\) measurement is set up so that the samples' \(c\) and VSF can be measured simultaneously. The accuracy of \(c\) needs to be validated since (1) \(c\) is an indispensable parameter to determine the calibration coefficient \(k\) during amplitude calibration; (2) it is used for the correction of the attenuation of the primary beam and the scattered light.  
A Perkin-Elmer (PE) Lambda 35 spectrophotometer was used to collect independent \(c\) measurements of the polystyrene beads suspended in water to validate the beam attenuation data from VSFLab. The \(c\) of the master solution was verified through this instrument. \(c\) was measured by placing a 1 cm sample quartz cuvette close to the output window of the sample beam and an additional custom cuvette with an aperture of 0.8 mm in front of the detector to reduce the acceptance angle of the detector to less than 0.23\({}^{\circ}\)[31, 32]. A series of samples with different concentrations (\(c\) = 0.13 m\({}^{-1}\), 0.55 m\({}^{-1}\), 0.79 m\({}^{-1}\), 1.33 m\({}^{-1}\)) of suspended 2.0 \(\upmu\)m standard polystyrene beads were prepared. More than 30 measurements were taken on each sample to compare the measurements from VSFlab with those from PE Lambda 35. Comparison results that the \(R^{2}\) value and RMSE were 96.84% and 0.047 m\({}^{-1}\), respectively indicated that the new prism yields good performance in the \(c\) measurements.  
Then, the \(c\) measurements for each sample were applied to the correction of light attenuation along the pathway inside the scattering volume following the procedure proposed by [PERSON] et al. [23]. As scattering measurements were acquired in a single scattering regime, the attenuation correction exerted a minor influence on the VSF measurements.
## 3 Testing Results Analysis
### Analysis of Experimental Results of Lower Step Specim
#### 3.2.3 Analysis of Characteristics of Two-Dimensional Spectrum  
Figure 15 shows the main frequency diagram of the whole process of the lower step specimen. Table 2 shows the proportion of the main frequency distribution of the lower step specimen. Based on the comprehensive analysis of Figure 15 and Table 2, the main frequency distribution of the experiment could be divided into four stages:  
Figure 14: Diagram of acoustic emission absolute energy and accumulated energy about lower step specimen.  
Figure 15: Diagram of acoustic emission energy and accumulated energy about lower step specimen.  
(1) I compaction stage and II stress concentration stage: The prominent feature of this process was the rapid accumulation of elastic strain energy of the specimen. The main frequencies of the two stages were mainly concentrated between 100 and 200 kHz, 250 and 350 kHz, and 450 and 600 kHz.  
(2) III impact disturbance stage: compared with the upper step specimen, the same point was that the bandwidth of the main frequency band also appeared from wide to narrow, but the overall change tended to be stable. The high-frequency (480-600 kHz) and ultra-highfrequency (600-700 kHz) bands showed a process from scratch. Different from the upper step specimen, the frequency band of high frequency (450-500 kHz) and ultra-high frequency (600-700 kHz) appeared after the experience of multi-level buried depth and at the buried depth of 1250 m, and the intermediate frequency signal (250-350 kHz) increased at the same time. The appearance of high-frequency value and ultra-high frequency value represented the initiation of small size and smaller size cracks in the specimen. The increase of medium frequency value meant the occurrence of large-scale fracture. The simultaneous appearance of the two indicated that at this stage, the rapid development of cracks of different scales caused the occurrence of zonal fracture similar to the \"logarithmic spiral\" type. Different from the zonal fracture phenomenon of the \"banded and interval distribution\" of the upper specimen, this fracture difference was mainly caused by the difference in the shape and size of the section between the two.  
(3) IV rockburst failure stage: This stage was similar to the upper step specimen, and the acoustic emission frequency band showed a bunchy feature. However, the bandwidth was basically stable. Compared with the previous two groups of mechanism experiments, there was no frequency band of 390-430 kHz and 600-700 kHz, which reflected the non-intensity of rockburst.
## 3 Results and Discussion
### Sensory Evaluation  
Sensory evaluation results for chicken patties are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Control** & **SS1** & **SS2** & **SS3** & **Significance** \\ \hline Odor & 4.08 \(\pm\) 0.17 b  & 3.75 \(\pm\) 0.14 b  & 3.04 \(\pm\) 0.11 a  & 2.75 \(\pm\) 0.15 a  & ** \\ Color & 3.71 \(\pm\) 0.22 c  & 4.00 \(\pm\) 0.15 c  & 3.17 \(\pm\) 0.17 b  & 2.58 \(\pm\) 0.20 a  & ** \\ Taste & 4.33 \(\pm\) 0.16 d  & 3.38 \(\pm\) 0.16 c  & 2.50 \(\pm\) 0.16 b  & 2.04 \(\pm\) 0.15 a  & ** \\ Texture & 3.92 \(\pm\) 0.17 b  & 3.67 \(\pm\) 0.10 b  & 3.13 \(\pm\) 0.16 a  & 2.75 \(\pm\) 0.20 a  & ** \\ General & 4.13 \(\pm\) 0.14 c  & 3.54 \(\pm\) 0.12 b  & 2.71 \(\pm\) 0.14 a  & 2.42 \(\pm\) 0.15 a  & ** \\ Acceptability & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}  
* Presented values are means \(\pm\) standard error; Control: 12% fat, SSI: 10% fat + 2% silverskin, SS2: 8% fat + 4% silverskin, SS3: 6% fat + 6% silverskin; a–d: Means marked with different letters in the same line are statistically different from each other (\(p<0.05\)); **: \(p<0.01\); **: \(p<0.05\); ns: not significant.  
\end{table}
Table 3: Texture profile parameters of chicken patties produced with coffee silverskin. 1 Substituting animal fat with CSS significantly affected all sensory parameters (\(p<0.01\)). While there were no significant differences in odor, color, and texture between the control and SS1 groups (\(p>0.05\)), the SS2 and SS3 groups had lower mean sensory scores for these parameters (\(p<0.05\)). This indicates that CSS in amounts exceeding 2% negatively affected odor, color, and texture. Taste also significantly decreased with higher proportions of CSS (\(p<0.05\)). Although the groups using CSS showed lower average values for overall acceptability compared to the control, there was no significant difference between the SS2 and SS3 groups. Similar studies replacing animal fat with dietary fiber sources in chicken meat products have reported lower average values for sensory parameters [(41; 48; 50; 34)].