esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Visual Scene Processing of Humans and CNN
#### 2.1.2 How Does CNN Scene Processing Compare to That of Humans?
Unlike humans, CNN do not miss targets when their size is unusual [43]. However, the flip side is that CNN easily mistake objects for visually similar ones (e.g., confusing brooms and toothbrushes), not taking their implausible size within the scene context into account. Another dimension for comparison is the role of _task-irrelevant biases_. First, it seems like CNN attend to the saliency of image features in general (e.g., edges, luminance or colors), not just the class-defining object [9]. However, the mechanisms behind this impact of saliency are likely to differ between humans and CNN. For humans, saliency effects can largely be attributed to their strong correlation with meaning [27]. It is unlikely that CNN also extract such meaning and they may even rely on salient image areas that have nothing to do with the actual class. Thus, the areas attended by CNN may not make sense to humans [44; 45]. Many of these divergences can be ascribed to \"excessive invariance\" [46]: CNN learn whatever shortcut is sufficient for classification, which may or may not match the important scene characteristics according to human standards [40]. Given these similarities and differences in scene processing between humans and CNN, it can be expected that their measurable outputs, namely attention maps, might also differ. The following section will summarize the available research on comparing human and CNN attention maps, extract the factors that affect this comparison, assess the insights that previous studies provide about these factors and specify a research gap that has been ignored up to now.
## 3 Development of the Multi-Objective AutoML Model  
### Architecture of the Multi-Objective AutoML Model  
The batch size and number of epochs are the most critical hyperparameters in training the AutoML framework. The batch size defines the number of training samples processed in one forward-backward pass. Larger batch sizes often accelerate training but may compromise accuracy or lead to overfitting, whereas smaller batch sizes tend to improve generalization at the cost of increased computation and training time [19]. Here, a batch size of 100 was selected via a trial-and-error approach. An epoch corresponds to one complete pass through the training dataset, during which weights, biases, and potentially the model architecture itself are updated. Generally, increasing epochs enhances predictive performance but reduces computational efficiency [20, 21]. To minimize errors from dataset partitioning and enhance model generalization and evaluation stability [22], this study implemented 4-fold cross-validation during AutoML modeling. The average mean squared error (MSE) across the four folds served as the evaluation criterion.  
As the epoch count rose from 0 to 150, the MSE on the training dataset declined from 1.7 to 1.3, and on the validation set from 2.2 to 1.5 (Figure 4), indicating substantial improvement in model accuracy as the network weights and topology evolved. Beyond 150 epochs, MSE reductions became marginal: at 1500 epochs, the training and validation MSE stabilized around 1.25 and 1.45, respectively. Consequently, 150 epochs were adopted for final training. The validation loss remained slightly below the training loss throughout, suggesting effective control of overfitting.  
AutoML, automating model selection, hyperparameter tuning, and neural architecture search, identified the optimal structure for inverting the linear parallel bond parameters (\(E\), \(k_{s}\)/\(k_{n}\), and \(E_{d}\)). After 150 epochs, the selected architecture comprised an input layer of sixteen features (\(x_{1}\), \(y_{1}\), \(x_{2}\), \(y_{2}\), \(x_{3}\), \(y_{3}\), \(x_{4}\), \(y_{4}\), \(x_{5}\), \(y_{5}\), \(x_{6}\), \(y_{6}\), \(x_{7}\), \(y_{7}\), \(x_{8}\), and \(y_{8}\)), two fully connected hidden layers (256 and 32 neurons), and a linear output layer producing the  
Figure 4: Evolution of MSE loss for the multi-objective AutoML model over training epochs.  
three target parameters (Figure 5). Hidden neurons employed the rectified linear unit (ReLU) activation function,\(f\) (x) = max (0, \(x\)), and dropout regularization was applied to mitigate co-adaptation among neurons [23]. The output layer was a linear layer with no activation function because the prediction of the contact model parameters was a scalar regression issue, and it contained three neurons to output the predicted \(E\), \(k_{s}/k_{n}\), and \(E_{d}\) of the linear parallel bond model in PFC3D.
## 5 Smart Community
### Smart Environment Management
#### 5.1.1 Air Quality Monitoring
[46] introduced the Snap4 City platform for people to develop an IoT environmental management platform for smart and sustainable cities using heterogeneous data. In addition, citizen engagement is one of the research trends in the IoT-based external air quality monitoring system. First, citizens as sensors can play a role in data collection. [PERSON] et al. [55] proposed using crowdsourced automobiles and their built-in sensors to monitor fine-grained air quality in urban environments, which is an effective way to collect high-resolution air quality data anytime, anywhere, and at a relatively low cost. Second, the IoT system should be people-centric. [PERSON] et al. [67] also utilized data collected by sensors deployed on vehicles. They proposed a deep learning network framework based on long and short-term memory to integrate historical in situ observations (i.e., weather station) and IoT observations (i.e., sensors deployed on vehicles) to predict temperatures at high temporal and spatial resolutions. The network comprises two stacked layers of long and short-term memory, which can simultaneously process measurements from all sensor locations and generate predictions for multiple future time steps. [PERSON] et al. [52] proposed a people-centric and cognitive Internet of Things environmental monitoring system to quantify personal exposure to air pollution and evaluated this system in New York. [PERSON] et al. [56] developed MyAQI, an IoT air quality monitoring and prediction system that takes into account the context of the external environment and user attributes. This system can send air quality monitoring notifications to users with different sensitivity levels to main pollutants, changing the severity according to user health conditions. [PERSON] et al. [58] developed a mobile temperature and humidity sensor to characterize intracity variations in human thermal conditions and reduce the risk of heat stress. The sensor can connect mobile phones through Bluetooth and allow people to check the surrounding temperature and humidity on mobile applications. [PERSON] et al. [51] introduced MegaSense, a spatially distributed IoT-based monitoring system for urban air quality. This system aims to provide surrounding air information to people.
## 2 Data and Methods
### Simulation Experiment  
The eddy covariance method measures fluxes integrated over an upwind area of a specific size, known as a footprint [40; 41]. The size of this area depends significantly on wind speed and direction. The NEE measurements from stations have a half-hour temporal resolution, which means that the station is sensitive to the area around at a distance that air mass has passed upstream during a specific half-hour. The linear scale of such an area may range from hundreds of meters to several kilometers.  
Thus, in the case of 30 m resolution remote sensing data, we cannot directly associate the surface reflectance value in a band's pixel (or calculated index) with the station inside and NEE estimation on this station for the specific date. In order to do so, we need to calculate the relevant weighted footprint [42; 43] of the impact area around the station for the corresponding time scale (daily in our case, see Figure 2). To calculate this footprint, we used a meteorological model which can reproduce transport processes on a local scale depending on weather conditions on a given day. For this purpose, we used the Lagrangian particle dispersion model (LPDM) FLEXPART. We weighted six Landsat-derived spectral bands and indices and integrated them by a footprint probability density function estimated using the FLEXPART model in backward mode. There is no need to do such a calculation for meteorological features, as their spatial resolution is coarser (linear size of gridcell about 9-10 km or 100 km\({}^{2}\)).
## 4 Experiments
### Road Tests  
The road test was executed on the Industrial Technology Research Institute (ITRI) campus. The total driving distance was about 5.4 km, during which the road slope varied and the maximum speed was 30 km/h. The driving curve is shown in Figure 10. The initial SOCs of the front, rear left, and rear right batteries were 72.8%, 70%, and 71.2%, respectively. Driving modes were shifted at \(SOC_{g}=-\kappa\%=-1\%\) and \(SOC_{r}=\rho=1.02\), according to the flowchart of the TBD strategy in Figure 4. Figure 11 illustrates the time history of mode, state, \(SOC_{r}\), \(SOC_{g}\), and the corresponding torque distributions.  
Figure 11: Histories of the mode (solid line), state (dashed line), state of charge (SOC) ratio, SOC gap and the torque distributions of the front, rear left, and rear right motors during torque and battery distribution process for the electric vehicle in the road test.  
Figure 10: Driving speed curve for the torque and battery distribution strategy in the road test on the campus of Industrial Technology Research Institute.  
Similar to the result of HIL test, the front battery pack took the first priority to supply power to drive the EV at Mode 1 (\(P_{r}=1\)) during the first 180 s. Because the EV moved upslope approximately at 150 s, two rear motors delivered extra torque. Between 180 and 480 s, Mode 2 with the PSO strategy worked when the SOC of the front battery was lower than that of the rear battery packs. The front motor still provided the major torque until the SOC gap was less than \(-1\%\) (\(SOC_{g}<-1\%\)) while Mode 3 (\(P_{r}=0\)) was executed. Then, Modes 2 and 3 shifted alternatively. The SOC difference of the three battery packs was finally kept within 1%, as shown in Figure 12.  
The two strategies with a PTR, \(P_{r}=0\) and \(P_{r}=1\), were also executed in the road test. The total energy consumption and the consumption of each battery pack are given in Table 5. It shows that the energy consumption rate of the TBD strategy was lower than those of \(P_{r}=0\) and \(P_{r}=1\). It means that with the same battery energy, the EV has a longer driving range by the proposed TBD strategy than that by other PTR strategies. For example, the TBD strategy extended \(11\%\) more driving range than the PTR strategy when \(P_{r}=0\), and the TBD strategy extended 23.5% more driving range than the PTR strategy when \(P_{r}=1\).
## 1 Introduction
A large part of available studies deals with the relationship between H/V frequency and depth to the basement derived from other techniques, such as boreholes or other seismic data [26; 27; 28; 29; 30; 31; 32]. Some other authors incorporate gravity information in their studies, to estimate the sediment thickness, e.g., [21; 23; 24], but these studies are not orientated to determining a correlation between both techniques, but rather they use the information to reach other objectives. Additionally, they do not explore how any potential correlation depends on geology. Therefore, the motivation of this work is to define in what geological conditions both methodologies are consistent and if they can be used indiscriminately with respect to each other. In other words, in what conditions it would be possible to use gravity to estimate where the impedance contrast occurs (predominant frequency) and vice versa. Understanding this relation can be very important in future studies. Not only in cases where is not possible to apply one of the methodologies, but also to constrain and validate the results of each other. Additionally, another objective is to make a distinction between the gravimetric and the seismic basement, as was described before. To achieve these objectives, a group of Chilean cities was selected as a case study. These cities (Arica, Iquique, Mejillones, Vira del Mar, Santiago and Concepcion) cover a great portion of the country and collect different geological conditions, which is crucial to understand possible correlations (see Figure 1). Together, these cities account for approximately 50% of the country's population [33]. These sedimentary basins include practically all the different types of soils present in the fore-arc of the Chilean Andes. The dataset was collected from different projects, which are described in Section 2 and whose main objective was to assess the seismic hazard in the country. Even though we are not including data from other main cities in the Andes of Peru and Ecuador, our observations are representative of almost all the tectonic and depositional settings of the fore-arc Andean domains of the whole region, dominated by intercalation of fluvial, volcanic and mountain erosional deposits [34; 35].
## 3 Application in Interventional Surgical Robot Training
### Sensing Performance  
Piezoresistive tactile sensors are mainly assigned to mimic the sense of touch in human. Thus, they can enhance control and feedback studies. In this evaluation study, the developed sensor was used for tracking the several hand motions during robot-assisted procedures, i.e., pushing, pulling, clockwise rotation, and anticlockwise rotation, which were analyzed using machine learning in the previous section. Usually, surgeons are aware of their motions (push, pull, and rotate) made at any time, while they only anticipate the sensory touch (stimuli) via experience using haptic feedback. These are transmitted to their brain for translation purposes to decide the next hand motion needed during an intervention. Thus, it has become vital to design systems that can predict the hand movements for robot intelligence and surgery security purposes.  
To mimic the surgeon's capability of determining hand motion executed each time, an open-setting experiment was carried out using two units of the piezoresistive tactile sensors mounted on a volunteer's thumb and index fingers to assess sensor feedback. The sensors were fixed on the volunteer's fingers while manipulating the robotic control interface. The electrical resistance shows the sensor's ability to respond swiftly as the robot's control interface was swiftly moved along the different axes. In this scenario, the control knob of the robot hand was arbitrarily glided to different positions for several seconds. During all procedures, the sensors were in a fixed position on a wearable glove; thus, the pressure applied could be correctly recorded from the sensor's surface. One key function of the piezoresistive tactile sensor is the viscoelastic behavior of the elastomer structure and the conductivity network fillers. During the process, sensors were in contact with the robot arm for a period of 0.45 s and then released for 0.15 s. The results were analyzed (Figure 6b,c), showing that the sensors exhibited a stable status. The apparent resistance signals showed a fast response to the hand's touch, while stability was inferred along the testing time of the axial and radial processes. Furthermore, we noticed that the outputs from both sensors matched one another closely. This result highlights the potential of the piezoresistive tactile sensor for force sensing in surgical robots, smart gloves, and e-skin.
## 4 Research Methodology
### Simulation Scenarios  
Three simulated scenarios were created in this research study to evaluate the efficacy, strengths, and shortcomings of regular OLSR, D-OLSR, ML-OLSR, and P-OLSR in a SAR environment using UAV ad hoc networks. The scenarios are as follows:  
1. _Increasing Mobility:_ The first SAR scenario examines the impact of mobility by varying UAV velocities (10, 20, 30, 40, and 50) m/s over the GM mobility model. Meanwhile, the number of UAVs is set at 50. With this flying speed, either low or high, drones cover more ground in a shorter period of time than search teams on foot [36];
2. _Increasing Scalability:_ The second scenario examines the impact of network density by varying the number of UAVs (50, 60, 70, 80, and 90) over the GM mobility model. Meanwhile, the UAV's speed is regulated at 20 m/s. In some situations, a significant number of UAVs are required to offer emergency assistance [37]. In addition, dense networks must be considered for evaluating routing protocols;
3. _Increasing functional/Alloved space:_ The third scenario examines the impact of the allowed space by varying the size of the serving area (1.5 \(\times\) 3, 1 \(\times\) 2, 1.5 \(\times\) 3, 2 \(\times\) 4, 2.5 \(\times\) 5, 3 \(\times\) 6) km \(\times\) km over the GM mobility model. The number of UAVs is set to 50, and the UAV speed is set to 20 m/s. The altitude of UAVs is set at 100 m. For instance, in the USA and Europe, civil drones can only fly without exceeding the regulated 100 m altitude in non-segregated air spaces and must be under the direct visual line of sight of the operator [38; 39].
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Ablation Study
#### 3.3.2 Reduction Ratio  
Reduction ratio \(r\) is a hyperparameter introduced in Equation (4). By changing \(r\), we can change the vector size between the two fully-connected layers of excitation operator in SSE module. In order to discuss the influence of \(r\) on the experimental result, we ensure that the input feature maps of the SSE module are the same (select the feature map Con_2 obtained from the second convolutional layer and Con_3 obtained from the third convolutional layer). It can be concluded from Table 3 that the detection accuracy decreases with the increase of \(r\), and the highest detection accuracy is obtained when \(r=0.5\). [32] proves that the larger \(r\) is, the less the parameters of the model are. When \(r=0.5\), the model has the most parameters and the strongest ability, thereby achieving the highest detection accuracy.
## 2 Description  
### Benchmark Building  
The investigated building, BM0, is illustrated in Figure 1, was constructed in Zagliveri, Thessaloniki in 1979 and no longer meets the requirements of current anti-seismic regulations. It is part of a group of four tobacco warehouses. Therefore, for many years, it has been used as a tobacco warehouse, and recently, the re-use of this building was proposed but with a new unknown function. The dimensions of its sides are 28.40 m \(\times\) 39.60 m. In the x-x direction, it consists of four frames. The external ones have an opening of 7.20 m, and the rest are 7.00 m. In the y-y direction, BM0 consists of seven frames. Similarly, the external ones have an opening of 5.80 m, and the rest are 5.60 m. The height of the first floor is 5.00 m and the second floor is 4.20m. The concrete that was used is class B225 (\(\approx\)C16/20, with characteristic cylinder compressive strength \(f_{ck}=16\) MPa and elastic modulus \(E_{cm}=28,608\) MPa), the steel of the longitudinal steel bars is STIII (\(\approx\)S400, with min. yielding strength \(f_{yk}=400\) MPa, min. ultimate strength \(f_{su}=500\) MPa and min. fracture strain \(\varepsilon_{su}=12\%\)) and the steel of the transverse steel bars is STI (\(\approx\)S220, with \(f_{yk}=220\) MPa, \(f_{su}=340\) MPa and \(\varepsilon_{su}=18\%\)).  
The dimensions of the columns of the ground floor are 0.40 \(\times\) 0.40 m, and those of the first floor are 0.30 \(\times\) 0.30 m. The beams of the ground floor are 0.25 \(\times\) 0.70 m, and those of the first floor are 0.25 \(\times\) 0.60 m. The thickness of the floor slab is equal to 0.15 m. All this information is provided in Table 1.  
Beams and columns of the building have been modeled as frame elements with rectangular cross sections (see Table 1), and it was assumed a rigid floor diaphragm for the model, as the buildings are regular in both plan and elevation (EN 1998-1:2004, pages 42-43, section 4.2.3.3) [24]. The building is subjected to gravity and lateral loads. Under this study, it is assumed that the additional permanent load of the floors (excluding the self-weight) is equal to \(g=2.40\) kN/m\({}^{2}\) and that the live load is equal to \(q=4.00\) kN/m\({}^{2}\), as the new function of the building is unknown. The area of Zagiliveri belongs to the second Hazard Zone of Greece (ZII). Therefore, for the model's design, it is used a ground acceleration equal to \(a_{g}=0.24\) g and a soil category of B.
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 6 Conclusions  
The approach based on the FEM/CFD coupling iterative algorithm is developed for analyzing the performance of 2 SFS, and the porous medium model for simulating the side leakage flow of the finger seal is also further improved. On this basis, the performance advantages of the 2 SFS compared with the 1 SFS are investigated, then the operating parameters and side leakage flow on the leakage and inter-stage pressure drop characteristics are discussed, and finally the way to improve the inter-stage imbalance of pressure drop of 2 SFS is studied. The main conclusions are as follows.  
1. The difference in leakage between 2 SFS and 1 SFS increases with increasing axial pressure difference, and when the axial pressure difference increases from 0.2 MPa to 1 MPa, the contact pressure ratio of the 1 st finger seal in 2 SFS to the 1 SFS A decreases from 0.49 to 0.25, the contact pressure ratio of the 2 nd finger seal in 2 SFS to the 1 SFS B decreases from 0.84 to 0.54. So the 2 SFS has better performance than the 1 SFS, and the advantage of the 2 SFS compared with the 1 SFS is more significant under high axial pressure difference.
2. Compared with not considering side leakage flow, the leakage of 2 SFS increases when the side leakage flow is considered. When the RMS roughness of finger elements is 1.5 um, the relative increment of leakage of 2 SFS is more than 15%.
3. There exists a phenomenon of inter-stage imbalance of pressure drop for 2 SFS, and increasing axial pressure difference and the RMS roughness of finger elements will aggravate the imbalance of pressure drop between stages of 2 SFS. The radial displacement excitation of rotor has little effect on the inter-stage pressure drop characteristics of 2 SFS.
4. Increasing the number of finger elements of the 1 st finger seal and decreasing the number of finger elements of the 2 nd finger seal can improve the inter-stage imbalance of pressure drop of 2 SFS with little change in leakage.  
Figure 20: Effect of number of finger elements of each 1 SFS on the inter-stage pressure drop characteristics.  
**Author Contributions:** Conceptualization, H.Z. and H.S.; methodology, H.Z. and H.S.; software, H.Z.; validation, Y.Z. and G.C.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, Y.Z.; writing--original draft preparation, H.Z.; writing--review and editing, G.C.; visualization, H.Z.; supervision, H.S.; project administration, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant numbers 51575445, 51305343.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** 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.