esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Analysis
### Grasp Detection in the Cornell Dataset  
To mitigate potential overfitting during model training in Cornell dataset, we integrated Dropout regularization and employed the Adam optimizer with a learning rate set at 0.001 while maintaining a batch size of 8.  
In Figure 7, we visualize the grasping detection results, where the first row displays the detected grasp points and grasp lines. It is evident that each grasp configuration is allocated within the graspable region. While the subsequent rows show heatmaps of grasp configurations, the heatmaps indicate the grasp configuration for each pixel in the image, with higher confidence grasp positions appearing closer to red. As a result, our model achieves a grasping accuracy of 98.9%. In addition, we conducted inference speed tests in the Cornell dataset, which only required 23 milliseconds of inference time per image, thus ensuring real-time for robot applications.  
In Table 2, we present a comparison of the test results among other algorithms in Cornell dataset. Compared with the well-known GG-CNN [4], our method exhibits a performance improvement of 25.9%. Our method also outperforms GR-CNN [6] by 1.2%. The main reason for the improvement lies in the other algorithms' relatively simple network architectures and limited capability in feature extraction. Finally, our algorithm surpasses the state-of-the-art algorithms [10; 11] by 0.9% and 0.7%, respectively. It is evident that our model is also advanced.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Authors** & **Method** & **Year** & **Accuracy (\%)** \\ \hline [PERSON] et al. [33] & Jacquard & 2018 & 74.2 \\ [PERSON] et al. [35] & FCGN & 2018 & 91.8 \\ [PERSON] et al. [4] & GG-CNN & 2020 & 84.0 \\ [PERSON] et al. [6] & GR-CNN & 2020 & 94.6 \\ [PERSON] et al. [12] & RSE-Net & 2021 & 94.8 \\ [PERSON] et al. [36] & Det. Seg. Refine & 2021 & 92.5 \\ [PERSON], [PERSON] et al. [19] & Cascaded Net & 2021 & 92.1 \\ [PERSON] et al. [10] & TF-Grasp & 2022 & 94.6 \\ [PERSON] et al. [11] & SE-Res-Unet & 2022 & 95.7 \\ [PERSON] et al. [21] & Efficient-Grasp & 2022 & 95.6 \\ [PERSON] \(Z\) et al. [14] & AAGDN & 2023 & 96.2 \\ [PERSON] et al. [22] & QQCNN & 2024 & 95.0 \\ Ours & FAGD-Net & 2024 & 96.5 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of results in the Jacquard dataset.
## 3 Data and Methodology
### Array Techniques  
Array-based techniques allow to obtain surface wave dispersion curves from ambient vibration records. In this study, three array techniques were used to extract the Rayleigh wave dispersion curves: the frequency-wavenumber (F-K) analysis [39; 40; 41; 42], the spatial autocorrelation (SPAC) technique [43; 44; 45], and the extended spatial auto-correlation (ESAC) technique [44; 46]. Some assumptions about the soil conditions are required before using these techniques. For example, it is assumed that the ambient vibration wavefield is essentially dominated by surface waves (especially the fundamental mode), and that the subsurface layers are homogenous and horizontally stratified, which means that in each layer the seismic waves propagate at a constant velocity [44].  
The array measurement campaign was carried out in January 2021. The difficulty of finding open fields far enough from human activities inside urban areas, limited the measurement sites to 11 (four in El-Attaf and El-Abadia, and three in Ain-Defla). The measurements were made during daytime with a recording time of 40 min. The configuration of the array is triangular. Concretely, the sensor deployment consists of an equilateral triangle of 30 m on each side, with a sensor placed at each vertex, a sensor placed midway on each side, and an additional sensor placed at the center (Figure 3). This configuration provides a better coverage. According to [44], the equilateral triangle is the most efficient configuration for array techniques. The equipment used was formed by 7 SARA SS10 triaxial velocity sensors (f\({}_{0}\) = 1 Hz) connected to SL06 digitizers.
## 3 Results
(2016) from a larger earthquake compilation related to M\({}_{\rm w,GCMT}\) versus M\({}_{\rm w,NIED}\) is shown in gray symbols in the background for comparison [20]. To help increase the base list of M\({}_{\text{W}}\), some statistical hypothesis testing is conducted to determine if Equations (1a) and (2a) are similar to a one-to-one line (slope = 1, intercept = 0) representing what M\({}_{\text{W}}\) estimates should be. Hypotheses testing performed on Equation (1a) found no significant difference against a slope of 1, \(t_{crit}\)(6,0.05) = 2.45, \(p\) = 0.34 and no significant difference against an intercept of 0, \(t_{crit}\)(6,0.05) = 2.45, \(p\) = 0.33. Hypotheses testing performed on Equation (2a) found no significant difference against a slope of 1, \(t_{crit}\)(4,0.05) = 2.78, \(p\) = 0.82 and no significant difference against an intercept of 1, \(t_{crit}\)(4,0.05) = 2.78, \(p\) = 0.82. This implies the relationships described by Equations (1a) and (2a) are not different from a one-to-one line. Hypotheses testing is not conducted on the TLS results as the residuals are not well-defined within the framework. As such, it is assumed the results from statistical hypothesis testing encompass the results from TLS. Due to this, the M\({}_{\text{W,NEIC}}\) and M\({}_{\text{W,JMA}}\) data are considered as M\({}_{\text{W}}\) and placed in the base list, with M\({}_{\text{W,JMA}}\) having priority over M\({}_{\text{W,NEIC}}\). In actuality, only one additional event was added to the base list, for a total of 149 earthquakes with a listed M\({}_{\text{W}}\). Given a baseline M\({}_{\text{W}}\), Table 3 presents the number of events that contain M\({}_{\text{W}}\) and any magnitude (i.e., M\({}_{\text{S}}\), m\({}_{\text{b}}\), M\({}_{\text{L}}\) etc.) according to agency. The table shows most of the data for analyses are from IDC and JMA with very limited data sets from KEA, a North Korean agency. A plot of M\({}_{\text{W}}\) and M\({}_{\text{S}}\) pairs from multiple agencies is shown in Figure 2. M\({}_{\text{S}}\) data from ISC, NEIC, BJI, MOS, and IDC is shown in Figure 2a-e, respectively. The figures show relatively little scatter and the data appear linear with R\({}^{2}\) = 0.935, 0.725, 0.940, 0.899, and 0.908 for ISC, NEIC, BJI, MOS, and IDC, respectively. Furthermore, the M\({}_{\text{S}}\) data does not show signs of saturation, a phenomenon where magnitude does not change as M\({}_{\text{W}}\) continues to increase. Mathematically, M\({}_{\text{S}}\) saturation tends to reveal itself with earthquakes approaching M\({}_{\text{S}}\)\(\sim\)8. On the lower end of the magnitude range, the figure shows NEIC, BJI, and MOS to have no data pairs M\({}_{\text{S}}\) \(<\) 4, while ISC and IDC have data pairs for M\({}_{\text{S}}\) \(<\) 4. For comparison, the ISC data generally plots closer to the right-side boundary of a global earthquake catalog data set [19].
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 9 Future Trends in ML for Smart Buildings
### Predictive Maintenance  
In smart buildings, predictive maintenance uses ML to predict equipment failures, increasing uptime and reducing downtime. To identify abnormalities and anticipate future failures, ML algorithms can evaluate historical and real-time data from building systems, enabling proactive maintenance measures [94]. Enhancing operating efficiency and prolonging the equipment's lifespan not only saves money but also minimizes energy wastage.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 5 Conclusions  
In this paper, we presented HCMUNet, a novel U-shaped hybrid architecture that integrates CNNs with Mamba for medical image segmentation. By combining the local feature extraction capabilities of CNNs with Mamba's strength in modeling long-range dependencies, HCMUNet enables efficient contextual representation learning with reduced computational complexity. To address the spatial information loss introduced during  
Figure 8: Comparison of segmentation performance (DSC) across multiple datasets using different models.  
Figure 7: ROC curves of different models on the ISIC 2018 dataset. False positive rate (FPR) denotes the rate of false positives among negatives, and true positive rate (TPR) denotes the rate of true positives among positives.  
down-sampling, we proposed the Skip Connection Dual Attention (SCDA) module, which facilitates multi-scale and cross-dimensional feature fusion. Extensive experiments on publicly available datasets demonstrate that HCMUNet consistently delivers strong segmentation performance, particularly excelling in boundary preservation and structural detail retention. The model achieved DSC scores of 90.32%, 81.52%, and 92.11% on the ISIC 2018, Synapse multi-organ, and ACDC cardiac datasets, respectively, underscoring its robustness and strong generalization ability across diverse segmentation tasks. In future work, we plan to further refine the model's internal structure to enhance its applicability to broader medical imaging domains. We also intend to evaluate its performance under data-scarce conditions using self-supervised and transfer learning techniques. Additionally, we aim to develop a lightweight variant of HCMUNet that maintains competitive accuracy while significantly reducing computational overhead, thereby facilitating deployment in resource-constrained clinical environments.  
All authors contributed to the approach conception and design. Material preparation, data collection, and analysis were performed by [PERSON] The first draft of the manuscript was written by [PERSON] under the supervision of [PERSON] and [PERSON]. All authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The code is available at [[https://doi.org/10.5281/zenodo.15606243](https://doi.org/10.5281/zenodo.15606243)]([https://doi.org/10.5281/zenodo.15606243](https://doi.org/10.5281/zenodo.15606243)).  
The authors declare no conflicts of interest.
## 4 Communication Technologies for WSANs Applied to the SG
### From Home Area Network to Wide Area Network
#### 4.2.2 Wide Area Network  
As has been explained before, WAN is the most important element of SG because it is used to centralize all the aggregated data issued from the overall electrical grids on a remote data center. Moreover, WAN could be used to diffuse control information from this data center to consumer appliances or to power plants in order to improve the efficiency of energy distribution. Therefore, WAN technologies must ensure the capacity to transport a very large amount of data with a range of several kilometers and high data rates.  
On the other hand, the sensor and actuators that compose a WSAN are not well adapted to the communication needs of a WAN in terms of data rate or energy autonomy. Nevertheless, the data measured by sensors can be transmitted to a \"central node\" which could be interfaced with a WAN such as the Internet. This central node will concentrate the intelligence of the whole WSAN, and it will not be constrained in terms of energy autonomy.  
Therefore, it is necessary to overview the main communication protocols that can be applied to WANs, because WSANs may be interfaced with them in a SG scenario. The main candidate communication technologies for the WAN level of a SG include the following.  
* Ethernet, which guarantees high data rates, up to 100 Gbps, ensures the data transport up to 10 kilometers using optical fiber without repeaters.
* Wireless Broadband Power Line is a hybrid technology between BPL and wireless techniques [78; 79]. The operating principle consists of sending the communication signal through the medium voltage electrical lines, reaching a data rate up to 200 Gbps over 1 to 3 km.
* WiMAX: Based on IEEE 802.16 standard, Worldwide Interoperability Microwave Access ensures a high data rate access, between 11 Mbps and 108 Mbps, to the Internet network with a coverage distance of several kilometers.
* Cellular technologies: Technologies currently used for mobile phone communications could also be another possibility to manage intelligent electrical grids by means of their capability of data transmission. In this case, Global System for Mobile Communication (GSM) offers the lowest data rate, around 9.6 kbps, whereas the higher data rate transmissions are reached by Universal Mobile Telecommunication System (UMTS) and High-Speed Downlink Packet Access (HSDPA), which offers 2 Mbps and 14 Mbps, respectively [80; 81]. These solutions, which are widespread over the world, also provide wide range coverage, up to few kilometers. The 4 th generation (4G), based on LTE, reaches data rates up to 100 Mbps [82; 83].
* IEEE 802.22: Due to the lack of frequency resources, this standard constitutes an alternative solution to ensure long-range wireless communication by using white spaces in the overall frequency spectrum such as television band (TVWS). This solution offers a range of 10 to 100 km and data rates of few Mbps, needs the cognitive radio (CR) [84] technology in order to detect unused frequency and to transmit data.
## 5 Comparative Evaluation of Different FOVs
### Experimental Results  
A comparative evaluation of visual interaction according to the visual context using different smart glasses was performed. A Welch's ANOVA was performed on the completion time for task execution. The analysis verified that there was a significant difference (\(p<0.05\)) in task completion according to the visual context and FOV. According to the post hoc analysis, there was no statistical difference between the stereo rendering and direct augmentation method with a small FOV and the non-stereo rendering and indirect augmentation with video background method, but there was a considerable difference in the stereo rendering and direct augmentation method with a larger FOV (\(p<0.05\)), as shown in Figure 21.  
Figure 20: The difference of FOV depending on visual contexts and different AR smart glasses (Epson Moverio BT-200 vs. HoloLens).
## 4 Discussion  
In previous studies on simulating sun drying, constant albedo values have been used, and the energy exchange within the layers under the paddy rice has been neglected. Moreover, steady state rather than transient state conditions are commonly assumed for solving equations in thermal analysis. Thus, temporary heat and mass transfer analyses were explored and presented in this paper.  
In reviewing the literature, [PERSON] et al.'s [37] approach resulted in a practical procedure to assess the measurement of albedo. We obtained an albedo value of around 0.25-0.28, corresponding to the period from 11:00 to 13:00 (Figure 6). This albedo value is higher than the values reported by other studies such as [PERSON] et al. [21] and [PERSON] [32], who proposed an albedo of 0.16 for brown rice. On the other hand, the presented range found in this study lies within the range provided by [PERSON] [17], who proposed values for wheat and rice in a range from 0.18 to 0.25. The albedo of paddy rice was shown to correlate with solar zenith angle, showing an \(R^{2}\) of 0.7880 and \(RSME\) of 0.0299. Hence, the solar zenith angle should be used for the parametrization of the albedo of paddy rice in modeling schemes in solar drying applications [10; 58].  
The results of the sun drying model show that when a variable, daytime-dependent albedo was applied, most of the RMSE and \(MAPE\) values were smaller for the estimation of the exit temperature and for the instantaneous mass than when using a constant albedo. Another important finding is that RMSE and \(MAPE\) with variable albedos are much lower for the instantaneous mass than for the exit temperature, showing that the model provides a better estimation of instantaneous mass. This was already observed from the sensitivity analysis, where albedo showed higher standardized regression values for the instantaneous mass than for the exit temperature.
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 4 The Propagation Law of Landslide-Induced Surges
### The Wave Steepness Characteristics of Landslide-Induced Surge Waves
#### 4.3.1 The Wave Steepness Characteristics of Landslide Sections  
During the test, according to the definition of a characteristic wave, the wave number, maximum wave height, maximum period, meaningful wave height, and meaningful period of the landslide-induced surge wave were measured by the wave monitor. Parameters are given in Table 3.  
During the test, Monitoring Points #1, #2, and #3 were, respectively, 1.5, 4.4, and 5.9 m away from the landslide entry point. The maximum wave height of the landslide-induced surge decreased with the increase in propagation distance, and the average wave height and significant wave height of landslide-induced surge increased with the increase in distance. The main reason for this is that Monitoring Point #1 was close to the water entry point of the landslide, and the water surface fluctuates more violently with higher wave height. However, the wave steepness at the landslide section was different from the maximum wave height and the significant wave height. When the landslide body entered the water, the steepness of the surge wave tended to increase first and then decrease, and the steepness of the surge wave was larger at the center line of the river. The curve of the wave steepness with propagation distance at the landslide section is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Point** & **Number** & **Hmax/cm** & **Tmax/s** & **1/3 H/cm** & **1/3 T/s** & **Mean Wave Height/cm** & **Mean Period/s** \\ \hline \#1 & 27 & 7.109 & 2.021 & 2.289 & 2.336 & 1.045 & 1.619 \\ \#2 & 26 & 6.437 & 2.124 & 2.589 & 1.474 & 1.289 & 1.541 \\ \#3 & 22 & 4.722 & 2.055 & 2.722 & 1.652 & 1.465 & 1.821 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The statistical table of wave height and period in the landslide profile.
## 3 Results and Discussion
### Association of Most Influential VOCs to Spoilage
The advantage of Figure 8B is that it allows associating the leading individual VOCs' contributors with samples. The VOCs with the highest contribution to variability were: butanoic acid (V62), ethyl butanoate (V18), and methyl butanoate (V13) related to butyric spoilage. This contribution agrees with that found in the ANOVA analysis and Q-Q clustering. Besides, ethanol (V10) and isopentanol (V33) were related to sulfidic spoilage, which again agrees with the high contribution of F508 to the ANOVA models of these VOCs. Besides, (Z)-3-hexen-1-ol (V47), acetoin (V38), and acetic acid (V50) seem to be present in most of the  
Figure 8: PCA (**A**), based on the log-ratios selected using the function STEP from easyCODA, and log-ratio analysis (LRA) (**B**), based the complete set of log-ratios. Symbols of samples correspond to industry A (F282 and F283, affected by butyric spoilage; F284, F285, F286, F309, normal fermentation) and industry B (F508 and F562, sulfidic and putrid spoilage, respectively; F502 and F592, normal fermentations). See Table S1 for the meaning of VOCs’ symbols. rest of the samples but absent/low in those affected by the butyric and sulfidic spoilages. Notice that several of the influential VOCs were also included in the selected log-ratios. Therefore, these analyses allowed identifying log-ratios and VOCs directly related to butyric and sulfidic spoilages but failed to detect those associated with putrid. The biplot based on the VOCs constituting these log-ratios also segregated butyric (F282 and F283) and sulfidic samples (F508) and showed similar VOC profiles of the rest of the samples (F284, F285, F286, and F309 from industry A, and F502, F562, and F592 from industry B), similarly to the grouping observed in the weighted clustering option. Therefore, the selection of high contributors' log-ratios and VOCs to the variance represents a valuable technique to visualize the relationship between them and the samples, while focusing on only the most influential ones.
# Techno-Economic Analysis of Waste Heat Recovery in Automotive Manufacturing Plants
## 3 Methodology
### Working Fluids and Boundary Conditions of the Proposed System  
This study selected toluene as the working fluid for the ORC and heat pump operating at temperatures up to 145 \({}^{\circ}\)C due to its favourable thermal properties and environmental impact. The critical temperature of toluene is 318.6 \({}^{\circ}\)C. This working fluid has high thermal stability, which ensures reliability and efficient heat transfer within the operating temperature range of the cycle. The environmental impact is considered based on the low global warming potential (GWP) and zero ozone depletion potential (ODP). Based on a selection of working fluids, toluene demonstrated good performance in various studies [22; 23].  
In addition, Ethylene glycol has been selected as the secondary fluid to transfer heat from the heat pump to the TES and from the ORC to the TES. This fluid was chosen for its favourable thermal properties and environmentally friendly effects. Another advantage is that it remains in liquid form within the operating temperature range of the proposed system at atmospheric pressure. This makes it safer to use and requires less energy to circulate compared to when it is in the vapour phase.  
In this study, several assumptions were made for simplification. All components are assumed to operate under steady-state conditions. For the heat exchanger, changes in the kinetic and potential energy of the working fluid are considered negligible, and there are no heat or pressure losses. It operates at constant pressure, with a constant overall heat transfer coefficient and specific heat. The pinch temperature difference between the heat source and the evaporator is set to 5 \({}^{\circ}\)C. Moreover, the isentropic efficiencies of the pump, compressor, and expander are assumed to be 0.75. The waste heat temperature (at s1) was assumed to be constant at 150 \({}^{\circ}\)C, and the mass flow rate was 34.25 kg/s [15], while the ambient air temperature (at a1) varied between 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C.