esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Multi-Channel and Multi-Spatial Attention Module
#### 2.4.2 Multi-Spatial Attention Layer  
The multi-spatial attention layer, following the channel attention layer, aims to mine the inter-spatial relationship of features so as to better identify and focus on the most distinguishable and informative areas of an input image.  
Figure 2: An overview of the MCMS attention-based CNN for whole-slide image classification. We take the preprocessed images with their labels as input to train a CNN with the MCMS module added after a selected convolutional layer to learn channel and spatial attentions. The feature map is refined with the attention information and passes through the network to enhance feature representation. After training, the model is able to quickly zoom in areas of concern and outputs an ISUP grade.  
As shown in Figure 3b, the refined feature map \(\mathbf{F}^{\prime}\in\mathbf{R}^{\mathrm{C}\times H\times W}\) is firstly applied to produce two one-channel feature maps \(\mathbf{F}^{\mathrm{S}}_{avg}\) and \(\mathbf{F}^{\mathrm{S}}_{max}\) that are in \(\mathbf{R}^{1\times H\times W}\). These two feature maps are concatenated along the channel axis as the input to multiple parallel convolutional layers with different kernel sizes. The output feature map of each convolutional layer \(\mathbf{M}_{i,k_{i}}\in\mathbf{R}^{1\times H\times W}\) can encode where to emphasize or suppress, where \(i\) is the index of convolutional layer and \(k_{i}\) is the corresponding kernel size. Intuitively, the choice of applying different kernel sizes could potentially produce richer attention information. The output feature maps of these convolutional layers \(\mathbf{M}_{i,k_{i}}\in\mathbf{R}^{1\times H\times W}\) are concatenated along the channel axis to produce a set of feature maps denoted as \(\mathbf{M}=[\mathbf{M}_{1,k_{1}};\mathbf{M}_{2,k_{2}};\dots;\mathbf{M}_{m,k_{ m}}]\in\mathbf{R}^{m\times H\times W}\), where \(m\) is the number of parallel convolutional layers. \(\mathbf{M}\) then passes through an average or max pooling layer along the channel axis to produce \(\mathbf{M}_{avg}\) or \(\mathbf{M}_{max}\) in \(\mathbf{R}^{1\times H\times W}\), which is followed by a sigmoid function for  
Figure 3: The proposed MCMS attention module consists of a multi-channel attention layer and a multi-spatial attention layer and can be easily plugged into any CNN backbone network with minimum change in the code level. (**a**) Multi-Channel Attention Layer; (**b**) Multi-Spatial Attention Layer; (**c**) MCMS attention module integrated with a convolutional layer.  
generating the spatial attention map \(\mathbf{F}_{S}\in\mathbf{R}^{1\times H\times W}\). The final output feature map of the MSMS attention module is \(\mathbf{F}^{\prime\prime}\), which is given in Equation (2).
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
## 5 Fault Tree Analysis
### Failure Rate Analysis of Negative Ion Accelerators  
Within the entire system, the Negative Ion Accelerator is the most complex component and exhibited the highest number of failures (30 incidents). The specific failure modes are as follows:  
1. The plasma electrode experienced a total of 8 failures, including: 1 incident of surface flatness not up to standard, 1 occurrence of electrode endplate blockage Fault, 1 instance of impurity deposition on the Plasma Grid (PG), 3 coolant leaks in PG water lines, and 2 scheduled electrode polishing/cleaning operations. The overall failure rate of the plasma electrode is calculated as \(\lambda_{T}\) = 8/(166 \(\times\) 24) \(\approx\) 2.00 \(\times\) 10\({}^{-3}\).
2. The common cause basic event has occurred 2 failures, both of which are short circuits between the PG and EG circuit boards.
3. The extraction electrode experienced 12 failures, specifically 5 electrode cooling water system failures, 4 instances of excessive impurity deposition, 2 cases of support cover being insufficiently flat, and 1 EG replacing the power supply chip. The overall failure rate of the extraction electrode is: \(\lambda_{T}\) = 12/(166 \(\times\) 24) \(\approx\) 3.01 \(\times\) 10\({}^{-3}\).
4. The acceleration electrode experienced 4 failures: 1 water pipe leakage, 1 case of excessive impurity deposition, and 2 cases of thermal deposition grinding and cleaning. The overall failure rate of the acceleration electrode is calculated as \(\lambda_{T}\) = 4/(166 \(\times\) 24) \(\approx\) 1 \(\times\) 10\({}^{-3}\).
5. The ground electrode experienced 4 failures: 1 water pipe leaked, 1 instance of excessive impurity deposition, and 2 scheduled polishing/cleaning operations. The overall failure rate of the ground electrode is calculated as \(\lambda_{T}\) = 4/(166 \(\times\) 24) \(\approx\) 1 \(\times\) 10\({}^{-3}\).  
The failure rates of each subcomponent within the Negative Ion Accelerator are listed in Table 1.
## 2 Materials and Methods
### Apparatus  
Stimuli for the fNIRS data collection were delivered to all participants via a laptop personal computer using two tabletop loudspeakers located 45\({}^{\circ}\) to the right and left sides of the participant. The stimuli were presented at approximately 65 dB SPL, as determined by a portable sound-level meter held by the participant's head. Stimuli for the SRTn testing were delivered for the CI group in the same manner as the fNIRS data collection. For the NH groups, stimuli were delivered through a GSI-61 audiometer to both ears via THD-50 headphones at approximately 35-40 dB SL above individual PTA (pure-tone average: mean thresholds at 500, 1000, and 2000 Hz). All the tests were conducted in a double-wall soundproof room. Bilateral CI users were tested while wearing both the CIs. Unilateral CI users were tested only with their CI (without a hearing aid on the contralateral side). One CI participant (CIS) had her left ear plugged with earplugs during the entire testing process since she had residual hearing in this ear (PTA = 48.33 dB).
## 5 Simulation
### Experimental Results and Analysis
#### 5.2.2 Training Time Analysis  
Figure 4 illustrates the local model training time across 50 rounds for both our framework and the HierFAVG method. The analysis reveals variations in training duration among IoT devices, primarily driven by hardware heterogeneity. For example, IoT 1 consistently achieves the shortest training time, averaging 4.2 s per round, while other devices exhibit slightly higher training times, reflecting their differing computational capabilities.  
The results for our framework and HierFAVG demonstrate similar patterns, confirming that the proposed framework does not introduce significant computational overhead during local training. This finding is critical, as it validates the efficiency of our hierarchical aggregation strategy and lightweight encryption mechanism in handling the diverse hardware configurations typically found in IoT environments.  
The variations in training time highlight the challenges of hardware heterogeneity in IoT deployments, with devices of lower computational power requiring longer durations, potentially affecting federated-learning efficiency. However, our framework addresses this using an asynchronous aggregation mechanism, enabling devices to contribute updates at their own pace without synchronization constraints. This approach prevents devices with lower power from becoming bottlenecks, enhancing the system's scalability and robustness.  
Moreover, the comparative analysis of training time between our framework and HierFAVG indicates that the integration of lightweight encryption and hierarchical aggregation does not adversely affect local training efficiency. The average training time per device remains consistent with that observed in HierFAVG, emphasizing the practicality of our approach in maintaining high performance while addressing security and scalability requirements.
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 2 Materials and Methods
### Methods
#### 2.2.3 Eddy Identification Algorithm  
Several eddy identification algorithms for mesoscale eddies have been proposed [35; 36; 37; 38; 39]. [PERSON] et al. [40] and [PERSON] et al. [41] compared different eddy identification methods and concluded that all methods are feasible for identifying mesoscale eddies. Since our goal is to compare the variance of eddies detected in different datasets, rather than to compare the differences of the methods themselves, it is sufficient to use the same method operating on all datasets.  
Thus, we choose the eddy identification algorithm from [PERSON] et al. [42]. The identification algorithm assumes that eddies can be represented as isolated anisotropic Gaussian anomalies, so that we can reconstruct the mesoscale eddy field by fitting optimal Gaussian anomalies to each identified eddy. Once the reconstructed SSH field is obtained, we can extract eddy kinetic energy from it to identify mesoscale eddies.
###### Abstract  
Since the sound velocity for medical ultrasound imaging is usually set at 1540 m/s, the ultrasound imaging of a patient with a thick layer of subcutaneous fat is degraded due to variations in the sound velocity. This study proposes a method of compensating for image degradation to correct beamforming. This method uses the sound velocity distribution measured in simultaneous ultrasound (US) and magnetic resonance (MR) imaging. Experiments involving simultaneous imaging of an abdominal phantom and a human neck were conducted to evaluate the feasibility of the proposed method using ultrasound imaging equipment and a 1.5 T MRI scanner. MR-visible fiducial markers were attached to an ultrasound probe that was developed for use in an MRI gantry. The sound velocity distribution was calculated based on the MRI cross section, which was estimated as a corresponding cross section of US imaging using the location of fiducial markers in MRI coordinates. The results of the abdominal phantom and neck imaging indicated that the estimated values of sound velocity distribution allowed beamform correction that yielded compensated images. The feasibility of the proposed method was then evaluated in terms of quantitative improvements in the spatial resolution and signal-to-noise ratio.  
Keywords:beamforming; MRI; MR-visible fiducial marker; subcutaneous fat layer; thyroid imaging; spatial resolution; signal-to-noise ratio (SNR); 1-3 piezocomposite material +
Footnote †: journal: Applied Sciences
## 3 Results
### Cell Proliferation: Direct Cell Count  
As observed for the BrdU incorporation assay, direct cell count demonstrated a different response in HepG2 and At-MSCs when expanded in the presence of LLP.  
Concerning HepG2 cells, although the average cell count was higher for 100 ug and 200 ug treatments, no statistically significant differences were observed between control and treated cultures (Figure 4A; Table 1). Cell-doubling time and cell-doubling number were not affected by cell treatments. Instead, LLP supplementation significantly improved the number of cultured At-MSCs after 72 h, with respect to untreated cell cultures (Figure 4B; Table 2), for each of the three different concentrations tested (\(p<0.001\)). No significant differences were observed between the treatments with 200 and 500 ug, suggesting that the treatment reached a plateau. Accordingly, At-MSCs' cell-doubling times were significantly reduced (\(p<0.001\)) and cell-doubling number significantly increased following the three treatments compared to the control cultures (Table 2), suggesting a stimulatory effect of LLP on At-MSC replication.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**HepG2**} & \multicolumn{4}{c}{**Treatment**} \\ \cline{2-5}  & **Control** & **100 ug** & **200 ug** & **500 ug** \\ \hline
**Average Cell Number \(\pm\) SD** & 450,000 \(\pm\) 82,585 & 495,000 \(\pm\) 148,951 & 517,833 \(\pm\) 53,504 & 421,185 \(\pm\) 92,766 \\
**Average Doubling Time \(\pm\) SD** & 22.51 \(\pm\) 1.87 & 21.95 \(\pm\) 3.02 & 21.03 \(\pm\) 0.91 & 23.35 \(\pm\) 2.63 \\
**Average Cell-Doubling Number \(\pm\) SD** & 3.21 \(\pm\) 0.27 & 3.31 \(\pm\) 0.45 & 3.43 \(\pm\) 0.15 & 3.10 \(\pm\) 0.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average cell number, cell-doubling time, and cell-doubling number of HepG2 cells treated with different LLP amounts (100 to 500 ug). No statistically significant differences were observed for average cell number, cell-doubling time, and cell-doubling number for the different treatments compared to untreated control cultures.  
Figure 4: Direct cell count of HepG2 and At-MSCs after LLP treatment: (**A**) HepG2: no statistically significant difference was observed for average cell number following LLP treatment ranging from 100 ug to 500 ug; (**B**) At-MSCs: following treatment with 100, 200, and 500 ug of LLP, a statistically significant increase in cell number was observed (\(p<0.001\)). Different letters indicate statistically significant differences.
## 2 Modeling of the CDPR System  
The configuration of the designed CDPR is shown in Figure 1. The cube frame dimension of the CDPR was 1 m \(\times\) 1 m \(\times\) 1 m (L \(\times\) W \(\times\) H). The pulleys were located asymmetrically to prevent intersections with each other during operation (Figure 1b). As shown in Figure 1b, four pulleys (P1, P2, P3, and P4) were upper pulleys. In contrast, four pulleys (P5, P6, P7, and P8) were lower pulleys. Additionally, the coordinates of the pulley location are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pulleys** & **X (m)** & **Y (m)** & **Z (m)** \\ \hline P1 & \(-0.455\) & \(0.540\) & \(0.884\) \\ P2 & \(0.455\) & \(0.540\) & \(0.880\) \\ P3 & \(-0.445\) & \(-0.540\) & \(0.860\) \\ P4 & \(0.455\) & \(-0.540\) & \(0.881\) \\ P5 & \(-0.540\) & \(0.465\) & \(0.075\) \\ P6 & \(0.540\) & \(0.455\) & \(0.071\) \\ P7 & \(-0.540\) & \(-0.455\) & \(0.073\) \\ P8 & \(0.540\) & \(-0.455\) & \(0.072\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coordinates of the pulley.  
A flexible cable with high-strength Dynamica rope was used in this study. These cables started from the winches and then passed through the pulleys to the end-effector and were divided into four upper cables and lower cables. The position of the end-effector was controlled by these cables. In Figure 1a, it can be seen that, to reduce the moment caused by the end-effector during movement, the upper cables were connected to the bottom anchor points of the end-effector, and in contrast, the bottom cables were connected to the upper anchor points of the end-effector as described earlier [14]. Here, we used an end-effector size of 0.26 m \(\times\) 0.26 m \(\times\) 0.26 m, with a mass of 1 kg.
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.2 Handling Outliers
Figures 5 and 6 show the model variation for each outlier removal method with contamination rates from \(c=0.05\) to \(c=0.5\), when using the prediction with RF as the imputation method, and the XBGT model, HGBR, for evaluation. The metric used for evaluation is \(R^{2}\). The number of instances remaining in the dataset for each contamination rate applied, further used in model training, is presented in Table 2. The research problem in this case is to identify the smallest contamination rate to avoid model overfitting while obtaining the best model performance. Looking at Figure 5 for the XGBT model, we notice that IF increases the performance constantly as the contamination rate increases, suggesting an overfitting trend, and a specialisation of the algorithms to select outliers to specialise the model. EE produces a similar variation, with the exception of a local maximum for \(c=0.1\), suggesting that this is a good choice of the anomaly rate. On the other hand, LOF presents a local maximum for \(c=0.2\), and then performance decreases for higher contamination rates, meaning higher contamination rates will lead to a loss of relevant information for the model. OC-SVM is similar with LOF, with the exception that the local maximum is at \(c=0.3\). Figure 6 presents very similar patterns for the HGBR model to those expressed in Figure 5.  
For each of the algorithms presented above, we used the Python Sklearn implementations: IF, LOF, EE (for MCD) and OC-SVM. Considering the experiments with different contamination rates and their impact on the model, the proposed candidates for the best results were 0.1, 0.2 and 0.3. Considering the literature recommendations for the contamination rate to avoid model overfitting and the limited data available in our case, we decided on the value \(c=0.1\) for our future experiments. For larger datasets of this type, a better choice could be \(c=0.2\), provided it was collected similarly in an uncontrolled environment without specific validation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Contamination** & **0.05** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** \\ \hline \(n\) & 934 & 885 & 787 & 689 & 590 & 492 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of instances remaining in the dataset corresponding to each contamination rate applied.
# How Human Mobility Models Can Help to Deal with COVID-19
## 2 Related Work
Recent studies have shown that not only SARS-CoV-2 viruses are transmitted in droplets, which have a reduced lifetime and range (less than 2 meters) but also in aerosols,which can remain suspended in air for hours [9] and reach longer distance. Therefore, some considered interpersonal safe distances should be revised. Several macroscopic models have been proposed to evaluate how different infectious diseases spread across cities, countries, or worldwide, considering geographical and mobility aspects (see for example the spatial models described in [33]). For COVID-19, an interesting model is the one proposed by [PERSON] et al. [34]. Their COSRE risk assessment tool obtains the exposure risk in EE.UU. 's counties (that is, the geographical risk) based on the probability of contacting infected people using the birthday paradox probability and real-time data of the potential COVID-19 cases in the counties. Another interesting macroscopical model is the one proposed in [35]. It is a macroscopical approach using a stochastic SEIR metapopulation model to predict the evolution and spreading of the COVID-19 across the different regions of Spain also using real mobility data. Finally, some studies have combined the use of mobility traces to evaluate the COVID-19 spread and the risk of transmission. For example, [PERSON] et al. used real mobility traces to evaluate the spreading of the infection in the city of Berlin [36]. A similar approach, considering the mobility trace of students on campus or schools was used in [5; 37] in order to evaluate smartphone-based contact tracing applications. To conclude, although mobility models and traces have been used to evaluate the temporal and spatial spread and risk of infectious diseases (and more recently for the COVID-19), these works are more focused on macroscopic models and areas, such as cities, countries, etc. In this paper we consider urban places and evaluate microscopically the effect of mobility patterns on the exposure risk to the virus, identifying the risky spots.
###### Abstract  
The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics describing the human presence on the planet that is based mainly on two quantitative factors: (i) the spatial distribution (density) of built-up structures and (ii) the spatial distribution (density) of resident people. Both of the factors are observed in the long-term temporal domain and per unit area, in order to support the analysis of the trends and indicators for monitoring the implementation of the 2030 Development Agenda and the related thematic agreements. The GHSL uses various input data, including global, multi-temporal archives of high-resolution satellite imagery, census data, and volunteered geographic information. In this paper, we present a global estimate for the Land Use Efficiency (LUE) indicator--SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015. In addition, we analyze the characteristics of the GHSL information to demonstrate how the original frameworks of data (gridded GHSL data) and tools (GHSL tools suite), developed from Earth Observation and integrated with census information, could support Sustainable Development Goals monitoring. In particular, we demonstrate the potential of gridded, open and free, local yet globally consistent, multi-temporal data in filling the data gap for Sustainable Development Goal 11. The results of our research demonstrate that there is potential to raise SDG 11.3.1 from a Tier II classification (manifesting unavailability of data) to a Tier I, as GHSL provides a global baseline for the essential variables called by the SDG 11.3.1 metadata.  
SDG11, Land Use Efficiency; Open Data; GHSL; Landsat; urbanization; urban expansion; population mapping 1
## 5 Results and Discussion
In this study, a photovoltaic fixed at a 30\({}^{\circ}\) angle was installed on the facade of a car park building, and a simulation was conducted by setting the exposure period to 6:00 AM to 8:00 PM every day from January to December 2023. As a result, a total of 595,593 kWh-year\({}^{-1}\) of renewable energy was generated. Ultimately, the energy generation efficiency of the kinetic photovoltaic facade developed in this study, which moves according to the optimal tilt angle every month, was proven to be high. Assuming that LED streetlights (60 Wh-h\({}^{-1}\)) installed in the city center operate for 12 h a day, the 692,386 kWh-year\({}^{-1}\) derived from this study can supply electricity to a total of 2634 streetlights.