esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Development Process Considerations in GNSS-Denied Navigation Project for Drones
## 3 Software Development Process Given for Consideration in This Paper
#### 3.2.2 Iteration Planning  
The duration of iterations in an example project was two weeks. This was enough time to see progress but not to allow the work to proceed in the wrong direction. The company work style and the need for project and software development monitoring were considered to set up iteration duration. Each task was created in a way that allowed work to be performed in these intervals. The tasks too big to be implemented in two weeks were split into smaller tasks. After preparing a product backlog that way, there was a better possibility of estimating the overall software development duration in iterations or weeks. This approach also facilitated progress monitoring with a burn-down chart [22] to correct estimates and determine the real velocity of the team in implementing issues. The velocity was determined by measuring the number of issues closed per iteration and was used to adjust the number of issues to be implemented in the following iterations. Productivity could be measured by dividing the velocity by the number of team members [22]. Productivity was out of our point of interest. More details on utilised process metrics can be found in Section 4.  
During the iteration planning, each team member responsible for source code development chose tasks to perform during the iteration. That way, the team created an iteration backlog.  
The number of tasks chosen by one person in the current iteration depended on the historical data (if some were already collected from previous iterations) and the estimated time needed to complete the task. Although tasks were prepared to last up to two weeks duration, some of them could be much simpler to complete and could take, for example, a day. Everything depended on the parent requirement's complexity and understanding of the requirement by the developer during transformation. A few initial iterations may not be optimally planned due to a lack of historical data regarding the team (or person) velocity. This would be unveiled after a few iterations and corrections.  
After selection, each selected task was given its acceptance criteria [43, 44] or definition of completion [30] to facilitate effort assessment and provide a common understanding of what was the limit of the task and what results allowed the task to be marked as completed. It facilitated transparency, inspection and adaptation [30] regarding achieved results and related thoughts/conclusions.  
The number of tasks selected by individual team members depended on the time declared for this iteration, the level of task complexity and the developer's skills. The time available for the current iteration was determined before selecting tasks. Time expressed in hours during the iteration depended on the work week length and the engagement of individuals in other projects.  
The iteration backlog was the output being entry data for software implementation and testing activity.  
A \"zoom-in\" into iteration planning activity is shown in Figure 3.
## 3 Kernel Density Estimation based on Non-Uniform Quantization
### Kernel Density Estimation Based Lloyd-Max Quantizer (KDE-LM)  
The proposed kernel density estimation based Lloyd-Max quantizer (KDE-LM) builds the quantizer in a manner similar to the k-means using sampled data. KDE-LM minimizes quantization errors by repeatedly minimizing the difference in area separated by quantization values in the probability density function by adjusting the quantization values. At this point, if the number of weights increases, the computational costs for the correct integral computation also heavily increases. Therefore, by performing a kernel density estimation once again using relatively small number of sampled data, and quantizing using it, quantization can be performed more efficiently in terms of the number of operations. Below is the pseudocode of the proposed KDE-LM methodology.  
Algorithm 2 shows the proposed KDE-LM process. Similar to KDE-KM, KDE-LM uses the KDE for original weights \(W^{0}\). However, KDE-LM has a difference in that it performs KDE once more from sampled data \(D^{s}\) to get the \(PDF^{s}\). The advantage of using sampled data \(D^{s}\) and the \(PDF^{s}\) is less computation costs when performing integral computation at line 7. The quantization is performed by assigning the quantization value to the original weights through line 9.  
```
0:\(W^{0}\), \(q_{bit}\), \(N\), \(E\)
0: original weights \(W^{0}\), target quantization bit \(q_{bit}\), the number of sampling data \(N\), the number of maximum iteration \(E\)
0:\(W^{q}\)
0: quantized weights \(W^{q}\)
0: Initialization:\(K\), \(Q\)
0: The number of quantization value \(K=2^{q_{0}a}\),
0: quantization values \(Q\)= \(\{q_{1},\ q_{2},\ \ldots,\ q_{K}\}\)
1:\(PDF^{0}\leftarrow KDE(W^{0})\) // kernel density estimation using original weights
2:\(D^{s}\leftarrow PDF^{0}(N)\) // sampling \(N\) data using probability density function
3:\(PDF^{s}\leftarrow KDE(D^{s})\) // kernel density estimation using sampled data
4:for e = 1 to \(E\)do
5:for i = 1 to \(K-1\)do
6:\(b_{i}=\frac{q_{i}+q_{i+1}}{2}\)
7:\(q_{i}=\frac{\frac{\hat{b}_{i}^{+1}}{2}\pi PDF_{s}(x)dx}{\frac{\hat{b}_{i}^{+1} }{\hat{b}_{i}^{+1}}PDF_{s}(x)dx}\)
8:for i = 1 to \(|W^{0}|\)do
9:\(w_{i}^{o}\leftarrow\underset{q_{j}}{\text{argmin}}\|w_{i}^{o}-q_{j}\|^{2}\), where \(w_{i}^{o}\in W^{0}\) // quantize the original weights \(W^{0}\)
10:\(W^{q}\gets W^{0}\) // set quantized weights \(W^{q}\)
11:return\(W^{q}\)
```  
**Algorithm 2** KDE-LM
## 2 Mechanism Introduction and Modeling Analysis
### Structure and Operating Principle
#### 2.1.4 Operation Process Analysis  
The seedling dropping process of the seedling dropping mechanism is shown in Figure 5. Figure 5a is a schematic diagram of the sequence of seedling dropping at each position during the machine advancement, symmetrical from outside to inside and left to right.  
The positions in the seedling separating cylinder are numbered from left to right as No. 1 to 10 positions, V is the forward speed of the machine, \(S_{i}\) is the dropping process of the seedlings at different positions during the forward process, and the dotted line is the dropping trajectory of the seedlings at the corresponding positions. For example, \(S_{1}\) is a schematic diagram of the dropping process of pot seedlings in two positions, namely No. 1 and No. 10. The dropping trajectory is a combination of the dropping movement of pot seedlings along the inner wall of the seedling dropping cylinder and the uniform forward movement of the machine. The dropping movement of pot seedlings along the inner wall of the seedling dropping cylinder at each position is shown in Figure 5b. Obviously, the trajectory of the potted seedlings at each position is not the same due to the principle of dropping seedlings from the drop tube, resulting in an unequal time from the potted seedlings' drop position to the planting position at each position, and the longer the drop trajectory, the longer the time required to drop seedlings.  
Figure 4: Interval seedling dropping device and its operation principle: (1) drive motor, (2) seedling separating cylinder, (3) spring, (4) seedling dropping cylinder, (5) seedling pot, (6) seedling carrying plate, (7) drive cam. (**a**) The schematic diagram of the interval seedling dropping mechanism. (**b**) The principle of the interval seedling dropping mechanism.
## 4 Results and Discussion
### Metrics Obtained from the Different Datasets Generated with Open Hardware
#### 4.1.3. DSUALM10H and DSUALM10
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Harmonics** & **NDE** & **F1-Score** & \
\begin{tabular}{c} **RMSE** \\ **(W)** \\ \end{tabular} & **MAE (W)** &
\begin{tabular}{c} **Runtime** \\ **(s)** \\ \end{tabular} \\ \hline \multirow{3}{*}{CO} & No & 1.853 & 0.463 & 376.207 & 208.329 & 3.25 \\  & Yes & 1.859 & 0.445 & 383.763 & 216.945 & 2.61 \\  & No & 0.827 & 0.499 & 295.45 & 241.034 & 1.31 \\  & Yes & 0.827 & 0.499 & 295.45 & 241.034 & 1.22 \\ Hart85 & No & 0.92 & 0.114 & 282.7 & 144.046 & 8.54 \\  & No & 1.101 & 0.419 & 405.841 & 224.72 & 66.31 \\  & Yes & 0.984 & 0.39 & 322.955 & 158.119 & 61.76 \\  & No & 0.743 & 0.529 & 242.217 & 169.454 & 310.57 \\  & Yes & 0.736 & 0.522 & 234.881 & 154.166 & 138.07 \\  & No & 0.652 & 0.593 & 191.057 & 112.596 & 5456.35 \\  & Yes & 0.619 & 0.558 & 180.981 & 111.258 & 1880.32 \\  & No & 0.633 & 0.555 & 189.776 & 111.629 & 1431.18 \\  & Yes & 0.629 & 0.528 & 203.484 & 132.55 & 754.77 \\  & No & 0.619 & 0.526 & 188.774 & 121.387 & 524.77 \\  & Yes & 0.689 & 0.495 & 225.348 & 142.535 & 8613.56 \\ WindowGRU & No & 0.714 & 0.5 & 232.156 & 150.051 & 6982.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics of DSUALM10 and DSUALM10H with nine algorithms, one-second sampling, and no filters. the accuracy and efficiency of load disaggregation, which is crucial for advancing energy efficiency and smart grid management.
# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 4 Results  
### Training and Testing on Different Object Classes  
To test the universality of different models, we trained DCP_V2_MLP, iterative PCRNet, ICP, Model-V1, Model-V2, and PTRNet on the first 20 categories of ModelNet40, for a total of 5070 models. We then selected 100 models for testing from 5 object categories that were not in the training categories and without noise in the point clouds. We used the same source and template point clouds when testing all algorithms for a fair comparison.  
The results for point cloud registration in the noise-free categories are shown in Table 1. PTRNet had the better registration results than either the depth or of traditional methods. It is worth noting that the iPCRNET model does not contain the Transformer module and the local feature extraction method adopted by us. As mentioned in Section 3, the ablation experiment Model-V2 adopts the same Transformer module as PTRNET but does not contain local features. Model-V1 contains local features but uses transformer modules that replace offset-Attention with self-Attention. For all performance indicators, the point cloud registration effect of models Model-V2 and Model-V1 with only local features or traditional Transformer is better than that of other algorithms, and this indicates that taking local features and fully considering global features in the stage of point cloud feature extraction will be more conducive to the later point cloud registration task. At the same time, PTRNet showed stronger performance with lowest mean error. On the other hand, DCP-V2 lost its registration ability when replacing SVD with MLP, which we attribute to the poor integration of the feature extraction network before and MLP after, while PTRNet was better as an end-to-end network. Figure 3 shows the point cloud registration for some representative objects, and it can be seen from the figure that point clouds after iPCRNet registration are scattered and that PTRNet has a better registration effect.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 4 Discussion
Regarding the effects of drought on vegetation, the three biomes presented different vegetation types, which directly influenced the recovery time. This was evaluated mainly by the duration of the drought on _EVI_ and _GPP_ due to a large influence of \(P\) and \(ET\). According to [PERSON] et al. [7], the concomitant development of negative \(P\), \(ET\), and vegetation index anomalies over timescales of several weeks provides valuable evidence of possible yield impacts. The Cerrado 2014 drought produced a large effect on \(ET\) and _GPP_ with a long duration of negative anomalies (11 and 10 months, respectively), which was not observed for _EVI_. For the Pampa 2012 drought, anomalies in \(ET\), _EVI_, and _GPP_ had a considerable duration considering that the event lasted less than a year. On the other hand, we found that the two drought events in the Amazon presented different behaviors regarding the effects on vegetation. The 2010 drought showed considerable duration in \(ET\) and _EVI_ anomalies, but _GPP_ (9 months) was the main variable with the longest recovery time for this event, while the 2015 drought had less effect on vegetation overall. Analyses of drought effects on vegetation are relevant considering that droughts are ecologically disruptive due to water scarcity, soil degradation, and occurrence of fires, which additionally cause economic losses in agriculture [41]. As a limitation, the study used only 10 years of data for the anomaly analysis, which was limited by _SM_ and _SSM_ product availability. Regarding the classification of 'hydroological drought', this study focused on _TWSA_, which refers to all water storage and does not focus on groundwater. This was due to the occurrence of substantial uncertainty in soil moisture data from remote sensing [83], leading to higher uncertainties in estimates of groundwater from _TWSA_. In addition, some limitations are associated with the analysis of variables with different spatial resolutions. For example, the GRACE data only had a spatial resolution of approximately 111 km (\(1^{\circ}\times 1^{\circ}\) grid), although the data demonstrated a satisfactory correlation with drought propagation and duration. Therefore, future studies should focus on addressing additional case studies, and as satellite data availability improves for global domains, it may be possible to achieve data with better spatial and temporal resolution.
###### Abstract  
The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and confer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan.  
deep learning; multiscale global graph convolutional neural network; forest type classification; remote sensing image segmentation; aerial photograph 2023  
1
# Immersive Two-Channel Recordings Based on Personalized BRIRs and Their Applications in Industry
## 6 Review of Industrial Applications
### Applications for Musicians  
The general use of binaural recordings is proven to be preferable over stereo mixes, which affirms the concept of mainly focusing on immersive applications in newer technology [56]. Research conducted by [PERSON] and [PERSON] [49] on musicians claims that in most cases the preference of playing with binaural recordings of other musicians is higher than that for a standard stereo setup. Higher engagement and arousal due to the improved auditory experience combined with stable sound source of virtual musicians are perfect conditions for remote collaborative playing, practice, and music making with the help of backing tracks. The results of this study also raise the need for the advancement of current headphone systems available to musicians. Personalization of the positions of sound sources of other instruments, so that they can practice any possible instrumental setup, is an enticing possibility. Additionally, implementing their own personalized BRIRs to the rendering could help them with precise localization of other musicians and enable them to practice their performances beforehand in their own home as if they were in the specific venue. [PERSON] et al. [57] showcased the importance of optimization of virtual loudspeaker positions for spatial sound reproduction with head-tracking headphones. The results concluded that the number of virtual loudspeakers can be reduced if the directions of direct sound and early reflections can be accurately reproduced. This leaves a lot of possibilities for creating a general database of rooms with loudspeaker setups for easier BRIR reproduction and by minimizing the real-time computation required for processing, it can also be used for training musicians in real time. [PERSON] et al. [50] lived into how binaural headphone monitoring, combined with audio-augmented and mixed reality technologies, can improve musicians' immersion during performances. This demonstrates that binaural audio enhances audio intelligibility and improves the perception of a realistic auditory image. These benefits contribute to better musical expression, creativity, and aural health for musicians. Adopting immersive audio systems, especially binaural ones with head tracking, can significantly elevate their creative process and performance experience.
# Evaluation of Anti-Skid Performance of Asphalt Mixture Based on Accelerated Loading Test
## 2 Methods
### Materials  
This study investigates the influence of internal factors, such as aggregate characteristics, gradation, and asphalt content, on the anti-skid surface layer's anti-skid performance. To explore the anti-skid durability of the anti-skid surface asphalt mixture, the research focuses on three anti-skid resistance types: AC-16, SMA-13, and SMA-10. The single-factor variable principle is adopted to examine the effect of oil-stone ratio, gradation, aggregate type, and other factors on the anti-skid durability of the asphalt mixture. The AC-16 asphalt mixture is prepared using three aggregates: andesite, diabase, lime, and SBS-modified asphalt (PG76-22). Six sets of asphalt mixture runs with different oil-stone ratios, gradations, and lithologies are subjected to accelerated loading tests to assess the influence of various factors on the anti-skid durability of the AC-16 asphalt mixture. The SMA asphalt mixture is also examined by preparing five sets of SMA-13 slab specimens for putting with different oil-stone ratios, gradations, and lithologies using two aggregates of andesite and diabase and SBS-modified asphalt (PG76-22). A set of SMA-10 slab specimens for rutting is also prepared as a comparison, and various factors' influence on SMA's anti-skid durability is studied. The specific test plan is presented in Table 1.  
The technical indices of the three types of coarse aggregates utilized in this study are presented in Table 2, while Table 3 displays the test results for the fine aggregates. The gradation design outcomes of the asphalt mixture are illustrated in Figure 3.  
Figure 2: Accelerated loading test equipment.
###### Abstract  
Most of the earlier tracking and network sorting approaches with a high sampling rate for frequency hopping (FH) signals did not adapt to the wideband system during their implementation, whereas the sub-Nyquist based algorithms cannot satisfy the real-time requirement for dealing with the rapid change of sparsity. It is important to improve the compressed sensing (CS) methods for tracking and sorting wideband FH signals. In this paper, a dynamic programming modulated wideband converters (MWC) scheme is proposed. First, considering the wide gap of FH signals, an improved power estimation method is proposed to track the support set in the time domain. Second, to sort multiple signals more effectively, a feedback control algorithm based on dynamic programming is proposed. In the proposed method, the total sampling rate is decreased significantly, and multiple FH signals are separated rapidly without recovery based on the results of tracking and comparative power. Simulations show that the proposed method can track and sort FH signals efficiently and more practically than previous methods.  
Keywords:frequency hopping signals; dynamic programming; modulated wideband converters; tracking; sorting +
Footnote †: journal: Applied Soft. 2019, 9, 2906; doi:10.3390/appr9142906
## 4 Experiments
### Datasets  
We conducted experiments on two datasets: CSL (Chinese Scientific Literature Dataset) [33] and LCSTS (Large Scale Chinese Short Text Summarization Dataset) [34].  
The CSL is the first scientific document dataset in Chinese consisting of 396,209 papers' meta-information obtained from the National Engineering Research Center for Science and Technology Resources Sharing Service (NSTR) and spanning from 2010 to 2020. In our experiments, we used the paper titles and abstracts to generate summary-article pairs for training and evaluation purposes. To facilitate evaluation and comparison, we chose the subset of CSL used in the Chinese Language Generation Evaluation (CLGE) [35] for our experiments. This sub-dataset comprised 3500 computer science papers.  
The LCSTS is a large dataset collecting 2,108,915 Chinese news articles published on Weibo, the most popular Chinese microblogging website. The data in LCSTS include news titles and contents posted by verified media accounts. Similarly to with CSL, we used the news titles and contents to create summary-article pairs for our experiments.  
Examples from these datasets can be viewed in Figures 11 and 12.  
For the unsupervised training part, our model did not have access to the matched summary-article pairs. Instead, we intentionally broke the pairs and randomly shuffled the data, ensuring that the model did not receive matched data during this part of the training.
# Optimization Method for Stiffened-Plate Layout in Box Structures Based on Load Paths
## 2 Structural Optimization Method Based on Load Path
### Establishment of Envelope Model  
Box structures usually contain features such as corners, hollow areas, and thin plates that limit the load path distribution, and it is necessary to exclude such features designed by conventional methods before optimization is carried out. Therefore, the methodology proposed in this paper first requires the construction of an envelope model of a given structure by analyzing its working conditions and functional characteristics. In this process, the following criteria are followed: firstly, the constraints and the load application region need to be kept constant, secondly, the voids in the internal discontinuity region between the two need to be filled in order to provide a basis for the load path analysis. Finally, the outer boundaries of the structure are analyzed, and the complex boundaries are filled with regular boundaries.