esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation and Experiment
### Simulation
#### 6.1.1 Zig-Zag Path Following under Constant Ocean Currents
The blue arrow shows forty times the velocity of ocean currents (\(40U_{c}\)) every 30 s. From this plot, it can clearly be seen that, when the path curvature varies greatly, _ALOS_ has a better tracking performance than _LOS_, and _APLOS_ has a better tracking performance than _PLOS_. In addition, the effectiveness of the adaptive method considering the path curvature is embodied. Furthermore, _APLOS_ can reject the constant ocean currents compared with _ALOS_, which embody the effectiveness of the proportional guidance. Figure 14b illustrates the cross-track error of _LOS_ (the yellow line) is larger than _ALOS_ (the blue line) and _PLOS_ (the pink line) is larger than _APLOS_ (the red line), which also reflects the adaptive method considering curvature factor plays an important role. Compared with _ALOS_, the cross-track error of _APLOS_ is smaller, which reflects the proportional method considering the fact that the drift angle plays a crucial role. Figure 14c presents the time evolution of look-ahead distance, including _LOS_ (the yellow line), _PLOS_ (the pink line), _ALOS_ (the blue line) and _APLOS_ (the red line). _LOS_ and _PLOS_ use a constant look-ahead 15 m. _ALOS_ and _APLOS_ use a varied look-ahead distance with respect to the curvature, and _ALOS_ is mostly smaller than _APLOS_ because the cross-track error of _ALOS_ is larger than _APLOS_, which also reflects the role of a proportional method. Figure 14d gives the time history of velocity and drift angle. In four plots, the red line and the blue line are the forward speed and sway speed of AUV, respectively. The black line is the real-time drift angle. Clearly, the drift angle is time-varying with respect to the sway speed of AUV based on four methods changed with the current heading of AUV. Compared with Figure 14a, _PLOS_ and _APLOS_ can resist the effects of time-varying drift angles and make AUV track along the reference path. Figure 14e gives the heading control effect. In four plots, the current heading is denoted by the blue line, and the desired heading is denoted by the red line. Clearly, the final tracking performance mainly depends on the design of guidance law due to the similar heading control performance of the four methods. As shown in Table 6, it is concluded that the MAE and RMSE of _APLOS_ are the lowest 1.2980 m and 2.5612 m, respectively, reflecting the position of _APLOS_ being closer to the reference path. Compared with _LOS_, _APLOS_ improves the tracking performance, reducing MAE and RMSE by 79.84% and 68.72%, respectively. Hence, _APLOS_ has a goodness of fit. According to Figure 14 and Table 6, _APLOS_ can sail along the curve path well under the ocean currents compared to _LOS_, _ALOS_ and _PLOS_.
# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 5 Basic Behaviors and Tasks in Swarm Robotics
### Pattern Formation  
Pattern formation is a technique of organizing the robots in a global structure by modifying their locations. The robots modify their locations, establishing a special form by local communication. [PERSON] et al. [113] developed a strategy to create local behavior agents in a swarm to arrange required global patterns, regardless of very restricted cognitive skills. According to the authors, this strategy was planned to perform the global pattern from the robots' stochastic bearing. The agents obey this stochastic procedure to choose an activity supported on their present understanding of the neighborhood. The authors put forward a proofing method aiming to confirm if the required pattern will constantly appear from the robots' local actions. The proofing method's major feature is that it is mainly local in nature and concentrates on the agents' local conditions and the global consequences of their local actions. It was employed as a local method to minimize computational work when examine the arising of substantial patterns. Lastly, the authors demonstrate numerous experiments on a practical robot model. [PERSON] et al. [114] discourse about problem of introducing a group of robots gradually to a shaping determined as a point cloud. The authors suggested an algorithm to convert a considering point cloud to an acyclic direct graph to accomplish this task. This graph is employed to enable a swarm of robots to arrange the object form supported just on local choices gradually. This indicates that robots are not information to obtain their position supported on the position realized of the agents previously in the formation. The authors demonstrated their method's effectiveness through experiments grounded on physics and robotic tests, demonstrating the solid confluence. [PERSON] et al. [115] developed a sophisticated formation control algorithm that allows nearly structures to be built without the requirement of connections among agents and without recognizing every agent with only one tag. This method is supported on an index-free description for a location with a shaping that demands distance and bearing measures to show the next robot's location. Furthermore, the descriptions can be adjusted to prevent collision among robots. [PERSON] and [PERSON] [116] proposed a distributed shape formation algorithm that allows a group of robots to create a structure by a specified form swiftly and with no impacts. The robots receive targets and apply the local knowledge to share the targets and program no collisions routes simultaneously. The authors performed experiments on a group of robots until 1024 simulated agents and 100 real agents. The tests' conclusion indicated that the algorithm was slower than the centralized technique; however, it can aggregate all agents arranging the intended form. Also, the significant number of experiments demonstrated the algorithm could perform with just a little divergence in the distance, nearly 25% if contrasted to an optimal centralized method.
## 3 Results and Validation
### Validation
#### 3.2.2 Mapping Accuracy from Spatial, Temporal, and Change Perspectives  
Spatial Perspectives  
The 431 spatially uniformly distributed validation samples used to evaluate the results were collected in the study area using 1986 field survey records, available Google Earth historical images, and expert prior knowledge. (Figure 15). The confusion matrices for the land cover product generated in this study and the CLUD-A dataset were calculated based on these samples (Figure 16). UA and PA for each land cover type are shown in Table 4. The overall classification accuracy for 1986 was 77.26%, with a Kappa coefficient of 0.7103, showing an improvement compared to the overall classification accuracy of 74.25% for the CLUD-A dataset, primarily because the details on the surface features of the super-resolved images are more obvious. This enhancement effectively addressed the issue of misclassification between cropland and built-up areas in CLUD-A. The misclassification likely arose because CLUD-A used coarse-resolution, directly resampled NDVI data over a long period (1980-1995), while there are many regions at the urban fringe where built-up and cropland are interspersed.  
Our average accuracies for cropland, forest, and built-up areas were 69.66%, 86.50%, and 75.79%, respectively (averaging UA and PA). Cropland had the lowest UA at 65.69%. The UAs of our product and CLUD-A for forest and water bodies were both above 90%, indicating an easier distinction for water bodies compared to other categories. However, due to the limited field survey records from 1986 and the lack of early Google Earth images with high spatial resolution in the Qilian Mountains, the distribution of land cover sample sizes is uneven, with a particularly small number of forest samples. Consequently, the  
Figure 16: Confusion matrix for our land cover map (**left**) and CLUD-A (**right**) in 1986.  
Figure 15: The spatial distribution of validation samples in 1986.  
high accuracy rate for forest areas is not representative. In this study, super-resolution methods enhance low-resolution images to resemble high-resolution ones, rather than fully replicating them. While SR methods effectively restore uniform areas, the sudden changes in edge features cause blurring, leading to misclassification between grassland, unused land, built-up, and cropland.
###### Abstract  
Background: In predictive modelling, particularly in fields such as healthcare, the importance of understanding the model's behaviour rivals, if not surpasses, that of discriminability. To this end, attention mechanisms have been included in deep learning models for years. However, when comparing different models, the one with the best discriminability is usually chosen without considering the clinical plausibility of their predictions. Objective: In this work several attention-based deep learning architectures with increasing degrees of complexity were designed and compared aiming to study the balance between discriminability and plausibility with architecture complexity when working with longitudinal data from Electronic Health Records (EHRs). Methods: We developed four deep learning-based architectures with attention mechanisms that were progressively more complex to handle longitudinal data from EHRs. We evaluated their discriminability and resulting attention maps and compared them amongst architectures and different input processing approaches. We trained them on 10 years of data from EHRs from Catalonia (Spain) and evaluated them using a 5-fold cross-validation to predict 1-year all-cause mortality in a subsample of 500,000 people over 65 years of age. Results: Generally, the simplest architectures led to the best overall discriminability, slightly decreasing with complexity by up to 8.7%. However, the attention maps resulting from the simpler architectures were less informative and less clinically plausible compared to those from more complex architectures. Moreover, the latter could give attention weights both in the time and feature domains. Conclusions: Our results suggest that discriminability and more informative and clinically plausible attention maps do not always go together. Given the preferences within the healthcare field for enhanced explainability, establishing a balance with discriminability is imperative.  
**Keywords:** attention mechanism; clinical plausibility; discriminability; electronic health record; recurrent neural network; longitudinal data
## 4 Generic Context Aware Cross-Layer Framework
### Communication Mechanism  
The communication mechanism of this framework is based on the data-centric pub/sub paradigm [15]. Under this mechanism, the AmI system is a _publisher_ that may publish inferred contexts, while sensor nodes are _subscribers_ that may subscribe to published contexts. A VB is a virtual brokerage entity formed by a cooperative group of sensor nodes that share the responsibility of providing context storage and retrieval services. Hence, any AmI context can be disseminated to subscribing sensor nodes for making informed optimization decisions based on situations of their monitored environment.  
Figure 2: Communication model.  
Figure 2 presents the communication model of this framework. It illustrates the bidirectional information flow: sensor data flow in one direction, and AmI context flow in opposite direction between the sensor nodes and AmI intelligent agents. The data structures for the bi-directional pub/sub communication are shown in Figure 3.
## 3 Approach
### Seedling Floating and Qualified State Recognition and Improvement
#### 3.2.4 Adding SimAM Attention Mechanism  
The paddy field environment is complex and variable, with factors such as exposed soil, deep water layers, water ripples, strong light reflections, and floating straw.  
Figure 9: Structure of ASPP (atrous spatial pyramid pooling).  
Especially in windy conditions, rice seedling leaves may exhibit varying degrees of lodging in the same direction, resembling the floating state. To avoid the omission or misjudgment of the model, and at the same time to further capture the feature information of normal seedlings and the key details of floating seedlings, in addition to improving the recognition performance of targets with inconspicuous features. Therefore, this study chooses to apply SimAM (Simple, Parameter-free Attention Module) [27] to the main feature extraction network. It is added after the SPPF module in the backbone network.  
The studied BAM and CBAM attention modules combine spatial attention and channel attention in parallel or serial combinations, which can only refine features in one dimension [28]. But, the SimAM attention mechanism introduces a three-dimensional modular architecture in space and channel without adding any extra parameters. It also enhances the flexibility and effectiveness of the module in terms of its ability to improve the representation of convolutional networks in space and channels. The computation of attention weights is accelerated and a lightweight attention module is obtained, as shown in Figure 10.  
This module draws inspiration from the phenomenon in visual neuroscience where active neurons can inhibit the activity of surrounding neurons. It evaluates the importance of each neuron based on priority, and its energy function is shown in Formula (2).  
\[e_{t}^{*}=\frac{4(\phi^{2}+\lambda)}{(t-u)^{2}+2\phi^{2}+2\lambda} \tag{2}\]  
In this context, \(u_{t}=\frac{1}{M-1}\sum_{i=1}^{M-1}x_{i}\), \(\sigma_{t}^{2}=\frac{1}{M-1}\sum_{i=1}^{M-1}\left(x_{i}-u_{t}\right)^{2}\), \(t\) represents the size of the neuron, \(u_{t}\) and \(\sigma_{t}^{2}\) are mean and variance calculated over all neurons except \(t\) in that channel, and the importance of the neuron can be evaluated through \(1/e_{t}^{*}\). The lower the energy, the greater the difference between the neuron and the surrounding neurons, indicating a higher level of importance.  
\[\widetilde{\mathbf{X}}=\text{sigmoid}\bigg{(}\frac{1}{\mathbf{E}}\bigg{)} \odot\mathbf{X} \tag{3}\]  
In the equation, \(\frac{1}{\mathbf{E}}\) represents the summation of \(e_{t}^{*}\) across all channels and spatial dimensions, \(\odot\) is the logical operator for XOR operation, and sigmoid is used to constrain and suppress excessively large values in \(\mathbf{E}\) without affecting the relative importance of each neuron. Through this operation, an enhanced feature matrix \(\widetilde{\mathbf{X}}\) is obtained.  
Figure 10: Simple, parameter-free attention module.
## 4 Experimental Results
### Experimental Details and Evaluation Indicators  
Several of the most advanced algorithms in blind unmixing, including the FCLSU, GLNMF, GraphL, gtvMBO, DAEU and EGU-pw, were compared with the EACNN.  
For the existing advanced algorithm models, the optimal parameter settings given in the literature were adopted. For the proposed EACNN, the power was set to 0.99 and the dropout rate was set to 0.9. The learning rate was iteratively updated by multiplying the initial rate by \((1~{}-~{}(\text{iter})/(\text{maxIter}))^{\text{power}}\). The network model ended after 300 batches.  
To evaluate the unmixing performance, the abundance and endmembers were evaluated using the root-mean-square error (RMSE) and spectral angle distance (SAD), respectively. These values are defined as follows:  
\[\text{RMSE}(\hat{y}_{\text{j}},\text{y}_{\text{j}})=\frac{1}{\text{N}}\sum_{ \text{j}=1}^{\text{N}}\sqrt{\left\lVert\hat{y}_{\text{j}}-\text{y}_{\text{j}} \right\rVert_{2}^{2}} \tag{18}\]  
where \(\hat{y}_{\text{j}}\) and \(\text{y}_{\text{j}}\) are the estimated abundance and actual abundance, respectively;  
\[\text{SAD}(\hat{\text{e}}_{\text{i}},\text{e}_{\text{i}})=\arccos\biggl{(} \frac{\hat{\text{e}}_{\text{i}}^{\text{e}}\text{e}_{\text{i}}}{\left\lVert \hat{\text{e}}_{\text{i}}\right\rVert_{2}\left\lVert\hat{\text{e}}_{\text{i}} \right\rVert_{2}}\biggr{)} \tag{19}\]  
where \(\hat{\text{e}}_{\text{i}}\) and \(\text{e}_{\text{i}}\) represent the extracted endmembers and reference endmembers, respectively.
###### Abstract  
The superior deposition rates of Wire Arc Additive Manufacturing (WAAM) allow for the production of a substantial number of parts while reducing the time and cost associated with machining, and have attracted significant attention. This study focused the microstructural and mechanical properties of a bimetallic thin-wall composed of SS308L and Inconel625, manufactured using variable-pressure rolling WAAM. A detailed analysis of the cross-sectional microstructure of the bi-metallic wall of non-pressure cases revealed a columnar solidification structure. In contrast, the pressured case was dominated by equiaxed structures. The microhardness tests conducted on the bimetallic interface revealed a hardness range of 139-188 HV within the SS308L region, and a 187-222 HV hardness spectrum within the Inconel625 region. The bimetallic interface displayed no unexpected hardness variations. Tensile tests carried out in the vertical direction across the bi-metallic interface resulted in a localized failure at the 308L region. The range of ultimate tensile strength was between 539 and 585 MPa, the yield strength varied from 344 to 377 MPa, and the elongation measurements were found to be within the range of 37 to 44%. All test specimens showed a consistent pattern of ductile rupture upon fractographic inspection, with fracture occurring in the SS308L region.  
wire arc additive manufacturing; bimetallic structure; variable-pressure rolling; Inconel625; SS308L
###### Abstract  
This study evaluates the thermal and energy performance of building envelopes incorporating phase change materials (PCM) compared with traditional resistive thermal insulation, considering future climate scenarios. Using EnergyPlus simulations, the study analyzes a medium office building with varying envelope compositions in two distinct Brazilian climates--Curitiba and Rio de Janeiro--representing bioclimatic zones 1 and 8, respectively. The PCM used, SP24E, aligns with the HVAC system setpoints, and climate projections for 2050 and 2080 are integrated using the Climate Change World Weather File Generator (CCWorld WeatherGen) based on the A2 emissions scenario. Results indicate that in mild climates like Curitiba, PCM significantly improves energy efficiency, reducing annual Energy Use Intensity (EUI) by 8.2% in 2050 and 10% in 2080 compared with resistive insulation. Conversely, in hotter climates like Rio de Janeiro, PCM increases EUI by 12.1% in 2050 and 20.7% in 2080 compared with resistive insulation. This study highlights the varying effectiveness of PCM in different climatic conditions and its implications for future building energy performance.  
phase change materials (PCM); building envelopes; energy performance; emissions; thermal storage; climate adaptation; sustainable construction +
Footnote †: journal: Applied Soft. 2025, 154-1662
## 4 Results and Analysis
### Energy Consumption  
The simulated energy consumption started at 67.92 J/s and maintained stability at around 69 J/s throughout the first 10 s of the simulation, with a slight peak at 76.34 J/s around the 9 s mark. After that, it stabilizes again at 69 J/s throughout the remaining time of the simulation. The minor fluctuations observed in the experimental data were present in the simulation as well, reflecting the arrival of the CAM at the RSU (see Figure 5b).  
Next, we changed the distance between the RSU and OBU to 50 m (see Figure 5c,d). The data recorded in the real world indicate noticeable fluctuations in energy consumption, ranging from 60 to 65 J/s with occasional peaks and dips. The energy consumption remained stable within the first 9 s at around 61 J/s. After that, it peaked at 65 J/s around the 9s mark, then started fluctuating with peaks at 67 J/s and dips at 61 J/s, before eventually decreasing toward the end of the experiment (see Figure 5c).  
The simulated energy consumption started at 67.92 J/s and maintained stability at around 69 J/s throughout the first 10 s of the experiment, with a peak at 76.34 J/s around the 9 s mark. After that, it stabilizes again at 69 J/s throughout the remaining time of the simulation. The minor fluctuations observed in the experimental data were present in the simulation as well, reflecting the arrival of the CAM at the RSU (see Figure 5d).  
We then raised the distance again to 70 m (see Figure 5e,f). In the experimental setup, the energy consumption values gradually increased over time in the first 21 s, starting from 57 J/s and maintaining a value ranging from 64 J/s to 68 J/s, eventually reaching a peak at 71 J/s after 21 s. There was a noticeable fluctuation ranging between 64 J/s and 71 J/s in the remaining time of the experiment, which decreased towards the end (see Figure 5e). On the other hand, the simulation data demonstrate a steady increase from 66.64 J/s to a peak of 77.53 J/s at the 3.1 s mark. After this, it stabilizes at 69 J/s throughout the simulation, with random fluctuations ranging from 75 J/s to 77 J/s before eventually stabilizing again at 68 J/s towards the end (see Figure 5f).  
Finally, we raised the distance to 80 m (see Figure 5g,h). In the experimental setup, similar to the previous setup with a distance of 70 m, we noticed a gradual increase in the energy consumption values, ranging from 57 J/s to 67 and fluctuating over time. Halfway through the duration, we noticed a peak at 71 J/s, then stabilizing at 66 J/s. Multiple peaks are observed towards the end at 71 J/s, eventually decreasing towards the end of the experiment (see Figure 5g).  
On the other hand, the simulation results show a more controlled energy consumption pattern, with values ranging from 66.64 J/s to 77.53 J/s, then stabilizing at 69 J/s and spiking to 77.47 J/s around the 9 s mark. After that, we see fluctuations ranging from 69 J/s to 76 J/s until it stabilizes at 68 J/s towards the end (see Figure 5h).
###### Abstract  
The spatial and temporal variations in global precipitable water (PW) derived from satellite and radiosonde observations during 2007-2016 are compared and analyzed in this study. The two observations show a good agreement in global spatiotemporal distributions and trends in PW. Generally, PW shows apparent latitudinal and seasonal variations. The averaged PW peaks in the tropical region, with the value of 40 mm or higher, and then decreases apparently to the polar region, less than 10 mm. PWs are larger in warm seasons and smaller in cold seasons for both hemispheres. It is indicated that the spatial and seasonal variabilities of PW are highly related to water vapor source and surface temperature as a direct heat source. The global PWs have changed in the past 60 years and, to some extent, strongly in the recent 10 years. It is found that PWs at Australian stations present statistically significant decreasing trends in last 60 years, while PWs at most stations in the United States have increasing trends. According to the global observations, it is found that PWs show uptrends over land and downtrends over the ocean in last 10 years, implying the widespread increase of water vapor in the troposphere over land.  
precipitable water; atmospheric water vapor; satellite; radiosonde +
Footnote †: journal: