esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Early Attention Layer Analysis  
The first two rows in Figure 6 illustrate the early convolutional layers, which capture basic spectral and harmonic structures within the input spectrogram. At this stage, the feature maps display a high level of activity across the spectrogram, revealing that the network is primarily learning to detect general patterns such as fundamental frequencies and overtone series. These early Conv layers act as foundational filters, identifying broad regions of frequency and amplitude that are essential for distinguishing basic sound structures. The activity patterns suggest that the network is still focused on general sound characteristics, laying the groundwork for more detailed differentiation in later layers.  
The third and fourth rows depict the height and width attention maps. In the height attention maps, the model emphasizes certain frequency bands, suggesting that it is beginning to recognize which harmonic regions contribute most to the trumpet and bass timbres. This selective focus highlights early attempts at isolating key harmonic frequencies. In the width attention maps, the network shows attention to temporal segments, indicating an emerging sensitivity to rhythmic or temporal consistency across the input sound. These attention maps reveal how the model begins to prioritize significant areas of the spectrogram, helping it capture the foundational time-frequency patterns.
## 2 Related Work
### Methods Based on Generative Adversarial Networks
#### 2.2.3 Generative Adversarial Network Method Based on Attention Mechanism  
In the field of computer vision, attention mechanisms can be divided into two categories: soft attention and hard attention. Soft attention is the proportion of a certain position in all positions, and its weight value is in the range 0-1. [PERSON] et al. [23] proposed the self-attention mechanism, and achieved good results in a machine translation model. [PERSON] et al. [24] took the lead in applying the self-attention mechanism to computer vision tasks, so as to establish the correlation between remote pixels. [PERSON] et al. [25] introduced a self-attention mechanism in a GAN to establish the connection between pixels, and their experimental results achieved good results on the ImageNet dataset. Hard attention is based on the response of a single position, so as to enhance or weaken the position, and its weight value can only be 0 or 1. [PERSON] et al. [26] expressed the importance of channel features through the attention mechanism, and enhanced or suppressed useful or non-useful channel features. [PERSON] et al. [27] restored the channel-level feature map to the original size by an up-sampling operation.  
[PERSON] et al. designed a generative adversarial model with enhanced channel and spatial attention, and trained the model using the optical sonar pairing data of the physical model in a zero-shot scene. This can generate rich sonar images and significantly improve the quality of sonar images and the subsequent classification effect [28]. [PERSON] et al. utilized a physically guided diffusion model, integrated the semantic vividness knowledge extracted by the Swin Transformer and the diffusion-generated features, and combined this with edge perception loss to improve the clarity of the target in the generated sonar data; this was conducive to the improvement of the accuracy of target detection and recognition [29]. The dual-branch attention model proposed in this paper focuses on the correlation between pixels and channels, and is applied to the generation of sonar images under specific categories.  
Overall, traditional methods overly rely on physical modeling and perform poorly in complex marine environments. The existing methods based on deep networks are vulnerable to environmental noise and have poor robustness. In response to the above problems, we propose a Conditional Dual-Branch Attention Generative Adversarial Network, which can generate high-fidelity sonar images of different environments and has strong robustness against environmental interference.
## 3 Radiometric and Geometric Calibration and Corrections  
Even within the same general or flock, each PS sensor has a unique relative spectral response and calibration. Atmospheric and environmental conditions also vary at the time of image capture, sometimes contributing radiometric and geometric errors that are difficult to correct across a constellation with no on-board calibration [10]. While the PS images undergo varying levels of processing by Planet before being released (Table 2), additional post processing by the user is often still needed, particularly when images from different sensors are to be combined or mosaicked for analysis. The following sections review the geometric and radiometric characteristics of different PS products and summarize the calibrations and corrections performed by Planet alongside a discussion of the advances being developed and implemented by the user community. Users should be sure to first check their Planet licensing agreements to determine the products/processing levels to which they have access.  
\begin{table}
\begin{tabular}{c c c c c} \hline
\begin{tabular}{c} X \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 2: Product levels with descriptions of radiometric, geometric, and atmospheric corrections for three PlanetScope Products. \(\surd\) = most complete, \(-\) = less complete, X = no corrections.
# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
## 3 Methodology
### Discussion on the Anonymization Techniques  
In this subsection, we illustrate how the proposed Grouping method can protect the user _POI_ by satisfying the _l-diversity_[19] privacy requirements. From the bike sharing transaction table (Table 1), we see that a user with the Birth Year of 1970 has four records in total. Therefore, this user is selected as a representative to find his _POI_, and we would like to verify whether our proposed method can protect his privacy or not.  
We consider that a bike sharing user with the Birth Year 1970 and an adversary wish to infer the user's locations and timing information (i.e., _POI_) from the published anonymized Table 3. In order to determine the user's _POI_, the adversary has to examine its matching bucket. By checking the Birth Year column, it is found that the user exists in both buckets. From the nature of a bike sharing transaction table, we know that a person's record will appear multiple times in the published dataset. From the published microdata table, the adversary finds that, in the first bucket, there are two records for the Birth Year 1970. The adversary is not sure that the user is a male or a female because the Gender column carries generalized values.  
By examining the third column (Start station, End Station) first bucket, the adversary finds that there are six records and among them two records (record number two and five) are similar. The adversary might assume that these records represent the locations where the particular user visited. The adversary needs to link the locations with the timing information to breach the user's privacy.  
In the first bucket of the fourth column (Start Time, End Time), the adversary finds that there are a total of six different time stamps. In order to determine the _POI_, the adversary needs to associate the location information with the timing information. However, the location information associated with six different time stamps make the adversary uncertain about the user's actual visiting time in that place. Therefore, we can say that the proposed Grouping method might preserve user privacy by breaking the linking amongst the columns.  
By carefully observing the first bucket, we discover that the Birth Year 1970 is linked with two persons (the Gender column is generalized, and, in generalization, we have to consider all possible values), one visiting place, and six different time stamps. It makes a total of 12 possible combinations or records, which satisfy the _l-diversity_ privacy requirements as in Slicing [19].
# Differences in Performance of Models for Heterogeneous Cores during Pulse Decay Tests
## 6 Summary and Conclusions  
Comparative analysis of pressure and pressure derivative curves for pulse decay tests was carried out. The performance differences between dual-permeability and dual-porosity models and between sphere and slab matrix transient dual-porosity cores were studied under different vessel volumes, storativity ratios, matrix-fracture permeability ratios, and interporosity flow coefficients. The results are summarized as follows:  
* The pressure and pressure derivative curves of sphere and slab matrix transient dual-porosity cores are coincident in early and equilibrium stages and are slightly different in the interporosity flow stage.
* Since the values and shapes of the pressure and pressure derivative curves are very similar, it is difficult to distinguish between the two matrix shapes, and another observation is necessary to estimate the matrix shape.
* The pressure and pressure derivative curves of dual-permeability and dual-porosity models are coincident in the early and equilibrium stages and are slightly different in the interporosity flow stage.
* Compared with the dual-porosity model, the horizontal section of the pressure derivative in the interporosity flow stage of the dual-permeability model is shorter.
* When the interporosity flow coefficient or vessel volume is large, the dual-permeability model is close to the pseudo-steady-state model.
* When the matrix-fracture permeability ratio is less than \(10^{-2}\), the difference between the dual-permeability model and the dual-permeability model can be ignored.
* The results of fitting the three models against the experimental data verify the results of this study.  
When the core contains only a few fractures to the core scale, although it often exhibits similar characteristics of the dual-continuum medium, the dual-continuum medium model is not very accurate, and the discrete fracture model is more suitable. This study conducted a comprehensive comparative analysis of all parameters of various dual-continuum medium models for pulse decay tests. Not only their pressure curves were compared but also their pressure derivative curves. However, only one test  
Figure 11: Comparison between the numerical modeling results and experimental data of [PERSON] (2014) for (**a**) pressure histories and (**b**) pressure derivative histories.  
case was used to verify the results of this study. It is hoped that there will be more experimental data to verify the conclusions in the future.  
Conceptualization, G.H. and X.L.; methodology, G.H. and M.L.; validation, Y.C. and X.L.; formal analysis, Y.C. and X.L.; investigation, G.H. and M.L.; writing--original draft preparation, G.H. and Y.C.; writing--review and editing, M.L. and X.L.; funding acquisition, G.H.  
This research was funded by the National Natural Science Foundation of China, grant number 11602276.  
The first author acknowledges the support from the Youth Foundation of Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Chinese Academy of Sciences.  
The authors declare no conflict of interest.
# Multivariate Algorithmics for Finding Cohesive Subnetworks
## 4 Density-Based Clique Relaxations
### Absolute Relaxation of the Average Degree: Average \(s\)-Plexes  
Density conditions may also be expressed in terms of the _average_ degree of a graph.  
**Definition 10**.: The _average degree_ of a graph \(G=(V,E)\) is \(2m/n\). A set \(S\) is an _average s-plex_ if \(G[S]\) has average degree at least \(|S|-s\).  
This clique relaxation was studied in the context of graph clustering problems [53]. If we consider only sets of a fixed size \(k\), then there is a direct correspondence between average \(s\)-plexes and \(\gamma\)-quasi-cliques.  
**Observation 5**.: Let \(S\) be a vertex set of size \(k\) in a graph \(G=(V,E)\), and let \(\gamma\in(0,1]\). Then, for \(s=k-\gamma\cdot k+\gamma\), \(S\) is a \(\gamma\)-quasi-clique if and only if \(S\) is an average \(s\)-plex.  
Proof.: Let \(m_{S}\) denote the number of edges in \(G[S]\). A size-\(k\) vertex set \(S\) is a \(\gamma\)-quasi-clique if and only if \(2\cdot m_{S}/(k(k-1))\geq\gamma\) or, equivalently, if \(2m_{S}/k\geq\gamma(k-1)\). In other words, a set \(S\) is a \(\gamma\)-quasi-clique if the average degree is at least \(\gamma\cdot(k-1)\). Setting \(s=k-\gamma\cdot k+\gamma\) yields \(k-s=\gamma(k-1)\), and thus, the two average degree conditions are equivalent.  
Moreover, as for \(\gamma\)-quasi-cliques, we can observe the following useful feature of average \(s\)-plexes.  
**Proposition 7**.: _The average s-plex property is quasi-hereditary._  
Proof.: Let \(S\) be an average \(s\)-plex of size \(k\) in a graph \(G\). Then, there is some \(\gamma\leq 1\) such that \(G[S]\) is a \(\gamma\)-quasi-clique and \(\gamma\cdot(k-1)\geq k-s\). Now, since the \(\gamma\)-quasi-clique property is quasi-hereditary [8; 49], there is a vertex \(v\in S\) such that \(S\setminus\{v\}\) a \(\gamma\)-quasi-clique. That is, \(G[S\setminus\{v\}]\) has average degree at least \(\gamma\cdot(k-2)\). This means that \(G[S\setminus\{v\}]\) is an average \(s\)-plex: since \(\gamma\leq 1\) and \(\gamma\cdot(k-1)\geq k-s\), we have \(\gamma\cdot(k-2)\geq k-s-1\).  
Once again, we are concerned with the problem of finding large average \(s\)-plexes.
# Cosine-Based Embedding for Completing Lightweight Schematic Knowledge in DL-Lite\({}_{core}\)+
## 2 Related Work
### Factual Knowledge Embedding  
Factual knowledge embedding mainly consists of two mainstream models, which are translational distance models and semantic matching models [9]. The former utilized the distance-based scoring function to measure the plausibility of one triple, and the latter used the similarity-based function to match the latent semantics of entities and relations in the vector space.  
TransE [13] was one of the most representative translational distance models. It tried to encode both entities and relations of triples as vectors into the same semantic space. For each triple (h, r, t), the head entity and the tail entity in the semantic space were denoted by \(\mathbf{h}\) and \(\mathbf{t}\) that could be connected by their relations \(\mathbf{t}\) with low error, such that \(\mathbf{h}+\mathbf{r}\approx\mathbf{t}\). Afterward, several methods have been proposed to improve this idea. TransH [14] projected all the entities into one relation-specific hyper-plane, which made different roles of one entity in different relations. TransR [15] and TransD [28] still followed the strategy of TransH. They projected entities into relation-specific spaces using a projection matrix so that more complex relations (i.e., 1-to-N, N-to-1 and N-to-N) could be encoded. To seek the most reliable relation among two entities, TransA [29] introduced an adaptive Mahalanobis distance to define the score function so that it could handle complex relations more flexibly. Nevertheless, the translation-based embedding strategy only considers the local information of triples, which cannot make full use of the global information in KGs.  
Another type of KG embedding models based on semantic matching adopted the architectures of neural network, which obtained encouraging results of KG completion, including MLP [30], NAM [31], and R-GCN [32]. In addition, ProjE [33] and ConvE [34] introduced the features of complex space and further optimized the score functions of underlying models. Therefore, both of them could obtain better performances compared to several models without the features of complex space.  
The methods for factual knowledge embedding mainly consider encoding the triples of KGs to obtain vector representations of entities and relations for factual knowledge completion. However, few of them pay attention to model logical properties (e.g., transitivity, symmetry) of relations. Hence, these methods can hardly be applied to the related tasks of schematic knowledge or ontologies (e.g., completion, reasoning, and repairing).
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 3 Factors Responsible for the Spontaneous Heating of Haystacks
### Storage Conditions  
The respiration process occurring in both plant cells and micro-organisms is responsible for the initial increase in internal bale temperature immediately after baling [32]. This results in spontaneous heat generation which often lasts approximately five days. Later,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Temperature during Storage (\({}^{\circ}\)C)**} \\ \hline
**Moisture (\%)** & **Maximum** & **Minimum** & **Average (30 Day)** & **Average (60 Day)** \\ \hline
32.50 & 61.78 & 31.39 & 45.50 & 39.78 \\
28.70 & 59.50 & 31.11 & 45.28 & 39.72 \\
24.80 & 54.22 & 30.28 & 42.28 & 37.89 \\
20.80 & 43.50 & 30.11 & 37.72 & 35.39 \\
17.80 & 40.22 & 30.00 & 35.89 & 34.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of moisture contents of hay on storage temperature, adapted from [PERSON] et al. [22].  
Figure 2: Relationship between heating degree days \(>\) 30 \({}^{\circ}\)C and moisture content at baling, adapted from [PERSON] et al. [10].  
the internal bale temperature may decrease for a short period of time followed by the initiation of an extended period of heating lasting for several weeks [10]. This prolonged period of heating occurs due to increased microbial respiration in their rapid growth and development stage during bale storage.  
In addition, hay baled at a high moisture content (30%) maintains a higher internal temperature compared to drier hay due to increased microbial respiration [33]. Dry hay does not heat excessively due to the absence of adequate moisture to support microbial growth. Figure 3 illustrates the typical pattern of heating and cooling that occurs over storage time for conventional rectangular bales of lucerne hay made at 20 and 30% moisture.
###### Abstract  
China is actively promoting ocean territory construction, and how to design low-energy buildings to fit the unique climate of tropical island regions has received much attention. The heat transfer coefficient of a building external surface plays a vital role in calculating air-conditioning load accurately. To obtain reasonable heat transfer coefficients in the tropical island region, this study introduced a naphthalene sublimation experiment to conduct full-scale measurements on convective heat transfer coefficients (CHTCs) in the tropical island region, and proposed a simplified calculation model of evaporative heat transfer coefficients (EHTCs). Results indicated that the function expression between CHTC and wind speed was CHTC = 5.56 + 4.48u (R\({}^{2}\) = 0.94), and it was validated to be reliable. Furthermore, compared with CHTCs and radiation heat transfer coefficients (RHTCs), the EHTC had a wider changing range, owing to rainfall effects. Moreover, whether evaporation was considered or not, the difference of total heat transfer coefficients (THTCs) on building external surfaces was 5.2 W/(m\({}^{2}\cdot\)K) for the whole year, so evaporation cannot be ignored directly. Additionally, THTCs with consideration of evaporation in winter and summer were 33.4 W/(m\({}^{2}\cdot\)K) and 38.9 W/(m\({}^{2}\cdot\)K) severally, which are much larger than the recommended values in the Chinese standard. This study would make up for the lack of surface heat transfer coefficients in energy conservation design of tropical island buildings.  
tropical island region; heat transfer coefficient; convection; radiation; evaporation; energy conservation design
## 3 Results
### Experimental Results  
Each configuration was evaluated using two different tasks, executed by two different non-expert operators. In total, 20 trials were carried out. The obtained results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task and** & **Average** & **Average Reaching** & **Average Tip Path** \\
**Configuration Id** & **Success Rate** & **Time [s]** & **Length [m]** \\ \hline Task 1, conf. A & 0\% & — & — \\ Task 1, conf. B & 25\% & 28.66 & 0.947 \\ Task 1, conf. C & 100\% & 20.07 & 1.005 \\ Task 1, conf. D & 100\% & 10.70 & 0.633 \\ Task 2, conf. A & 25\% & 47.16 & 1.920 \\ Task 2, conf. B & 50\% & 37.80 & 1.077 \\ Task 2, conf. C & 80\% & 34.45 & 1.250 \\ Task 2, conf. D & 100\% & 24.71 & 1.080 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table with metrics of performance of different tasks with different configurations.  
Figure 18: A pre-defined experimental test (Task 1): The arm tip is located out of the grill. The avoiding environment (the point cloud in boxes) consists of a single rock over the grill. The green circle corresponds to the target goal.  
A task is considered successful if the operator was able move the end effector to the target position with a precision of at least 0.015 [m], without colliding with the grizzly or rocks. If the operator cannot reach the target position, the task is considered a failure.  
Table 2 shows the average success rate, representing the percentage of successful tasks. The average reaching time shows how many seconds it takes for the operator to reach the target. The average tip path length represents the distance in meters that the tip of the end-effector has to move to reach the desired target position. It can be observed that the success rate increases as more features are integrated into the system. In Task 1 conf. A, the operators were not able to achieve the target point without colliding with the rocks. The integration of more features allows the operator to complete the task and reduce collisions. Configuration D allows complete completion of the task without collisions because the haptic system does not allow commands that generate collisions to be executed.  
In order to illustrate how the system operates, Figure 19 shows the trajectories of the tip for a different configuration when executing task 2. It can be seen that the trajectory becomes simpler as more features of the system are added.  
Figure 19: Different hammer tip trajectory paths of task 2 (same operator). The path is on a cyan line. (**a**) Conf. A (user controls each joint of the arm). (**b**) Conf. B (user controls the pose of the end-effector). (**c**) Conf. C (user controls the pose of the end-effector and 3D reconstruction of the environment) (**d**) Conf. D (user controls the pose of the end-effector, 3D reconstruction of the environment, and haptic feedback).