esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 4 Results and Discussion
### Performance of Proposed Hybrid Model (SE-ResNet-50)
#### 4.4.3 Training Stability and Computational Efficiency  
The model also demonstrates training stability, achieving convergence within 20 epochs using a batch size of 32 and an initial learning rate of \(1\times 10^{-4}\). The integration of SE blocks introduces only a modest increase in computational overhead compared with plain ResNet-50, while being significantly faster than DenseNet. As detailed in Table 8, SE-ResNet-50 provides an efficient trade-off between accuracy and training time.  
SE-ResNet-50 maintains efficient computational performance (318 s/epoch), significantly faster than DenseNet (646 s/epoch), despite similar accuracy levels. While slightly slower than basic ResNet-50, the substantial accuracy improvements justify the moderate increase in computational demand. This efficiency makes the proposed model more suitable for deployment in real-world applications, including mobile edge devices and cloud-based platforms, where computational resources may be constrained.  
To quantify its efficiency, the model's training time and GPU utilization were recorded and are shown in Table 9. The average training time per epoch was 318 s, and GPU utilization was moderate at 48 units, reflecting a good balance between performance and resource consumption. Additionally, the model maintained accuracy stability with a standard deviation of \(\pm\)0.25%, indicating robust and consistent learning behavior across training runs.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Time per Epoch (s)** & **GPU Utilization (\%)** \\ \hline ResNet-50 & 285 & 37 \\ VGGNet & 305 & 42 \\ DenseNet & 646 & 56 \\ SE-ResNet50 & 318 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Training efficiency comparison (per epoch).  
Figure 4: Confusion Matrix Comparison—(**Left**) ResNet-50, (**Right**) SE-ResNet50.  
Figure 5 illustrates the training dynamics of the SE-ResNet-50 model. The training curves show consistent improvement across epochs with minimal overfitting, though a slight divergence is observed in recall and validation loss after epoch 6, possibly due to increased sensitivity to subtle generative features. The stability in training loss and precision confirms robust convergence. This data supports the feasibility of deploying the model in both cloud-based and resource-constrained edge environments. Compared with DenseNet--which, while slightly more accurate, incurs nearly double the training cost--the SE-ResNet-50 model achieves an optimal balance of speed, accuracy, and hardware efficiency.
## 4 Discussion
### Future Directions  
The results from the real-life monitoring of wheelchair use with a method like the WCMM can be used on three distinct levels: (1) individual level, to quantify wheelchair use and define personalized goals like increasing daily distance (to increase daily physical activity) or diminishing the number of turns (e.g., to reduce observed shoulder pain when making turns); (2) clinical level, to observe wheelchair use in the daily environment of patients with shoulder problems, and to design, prescribe, or evaluate interventions tailored to the individual's capacity and requirements of daily ambulation; (3) research level, to examine relations between typical wheelchair use in terms of WCMMs and development of shoulder problems over time, which, in turn, may inform the first two levels. Future studies, including larger numbers of MWUs observed over longer periods of time, are needed. We recently started such a project, monitoring 60 MWU four times during a full week, distributed over a year. Such a dataset will deliver first insights in the longitudinal process of wheelchair use and the potential relationships in the development or progression of shoulder pain. It provides a good starting point for establishing normative data and, even further down the road, informs clinical guidelines. However, in the individual case, individual monitoring should be the preference.  
IMUs have broad potential for monitoring daily behavior. However, further advancements in algorithms for data analysis are needed, for instance, for the identification of propulsion patterns (both motion and force application) in the case of manual wheelchair use. Furthermore, even though manual wheelchair use is a dominating activity for MWUs, the quantification of other wheelchair-related activities is highly desired to obtain a full picture of shoulder loading in daily wheelchair life and its consequences for the development of shoulder problems.
In a study conducted by NTT DOCOMO [17], they explored the future development of 5G and the necessary components, applications, and innovations for 6G. They highlighted that the portable communication system undergoes significant shifts every decade, and the process of generating new market worth in the mobile communication industry takes approximately 20 years. In a statement made in [18], Samsung predicted that 6G technology would offer customers an unparalleled experience by enabling hyper-connectivity between people and all other entities. In addition, other alliances within the communication industry have also undertaken preliminary efforts in the development of 6G technology. The Next Generation Mobile Networks alliance conducted an analysis of the key elements that influence the development of 6G. These variables include social aspirations, market desires, and priorities. The alliance also explored the transition from 5G to 6G, focusing on the viewpoint of a 6G vision [19]. There are technical objectives can be obtained by using the 6G system, summarized as follows:  
1. Data transfer rates of one petabit per second or more, or one hundred times faster than 5G. We anticipate peak data rates of up to 10 Tb/s for some unique use cases, including THz wireless backhaul and fronthaul (x-haul);
2. One gigabit per second (Gb/s) as experienced by users, ten times faster than 5G. In certain cases, such indoor hotspots, it is anticipated to offer a data rate of up to 10 Gb/s as perceived by the user;
3. A delay of 10-100 us across the air and elevated mobility exceeding 1000 km/h. This will ensure a satisfactory Quality of Experience for scenarios such as hyper-high-speed rail and aviation systems;
4. Tenfold the connectivity frequency of 5G. This will accommodate up to 10 units per km\({}^{2}\) and an area traffic capacity of up to 1 Gb/s/m\({}^{2}\) for situations such as hotspots;
5. A spectrum effectiveness of 5-10 times and an energy effectiveness of 10-100 times compared to 5G. Figure 1 shows some of the capabilities of the 6G networks.
## 3 Experiment and Results Analysis
### Data Processing and Annotation
#### 3.1.2 Data Annotation Guidelines  
The sequence labeling method plays a pivotal role in entity annotation, among which BIO and BIOES [31] are commonly used approaches. The BIO method, which is simple and intuitive, uses the B (beginning of entity) and I (inside of entity) labels to indicate the entity's location but does not precisely identify the entity's end. In contrast, the BIOES method, by adding E (end of entity) and S (single-character entity) labels, offers more detailed boundary information of the entity, particularly suitable for the discontinuous entities found in marine coral reef ecosystems, such as single-character coral species or geographical locations. Consequently, this study adopted the BIOES method, as demonstrated in Table 1, aiming for more precise entity annotation in coral reef ecosystem research.  
In this study, we employed the BIOES five-tag sequence labeling approach, using the English abbreviations SPE (coral species), BSP (biological species), LOC (geographical location), and ENV (environmental factors) to represent different entity categories. Figure 7 shows the annotation format of the coral reef ecosystem named entity recognition experiment dataset (part). This method aims to refine entity recognition, especially in identifying discontinuous entities within the marine coral reef ecosystem. The study annotated a total of 4865 entities, as shown in Table 2, including 1946 coral species entities, 1459 biological  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Tag** & **Full Name** & **Description** \\ \hline B & Begin & Position at the beginning of an entity \\ \hline I & Inside & Position inside an entity \\ \hline E & End & Position at the end of an entity \\ \hline S & Single & Single-character entity \\ \hline O & Other & Any part that is not an entity (including punctuation, etc.) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Label type definition.  
species entities, 973 geographical location entities, and 487 environmental factor entities. These data reflect the complexity of the coral reef ecosystem, particularly the diversity of coral species. The larger number of coral species entities could be attributed to the richness and complexity of the marine coral reef ecosystem. This finding underscores the necessity of accurate identification and classification of coral species, which is crucial not only for understanding the biodiversity of coral reef ecosystems but also for devising effective conservation strategies. Conversely, the smaller number of geographical location and environmental factor entities might result from the research focus, data collection methods, and the challenges encountered during the annotation process.  
Future research should adopt more diverse data collection and analysis methods to ensure the comprehensive annotation and analysis of key entities like geographical locations and environmental factors, further facilitating in-depth studies of coral reef ecosystems.
## 2 Geological Setting  
The Shanxi Graben system is located east of the Ordos Block and west of the North China Block and acts as a transitional zone between the blocks [11,47] (Figure 1a). Formed during the late Pliocene, the Shanxi Graben system is the youngest of the peripheral fault basins of the Ordos Block and comprises several NE- and ENE-trending grabens and half-grabens arranged in a dextral oblique pattern. The overall trend is NNE, and the map view exhibits an 'S' shape. The system includes five large subsidence basins (from north to south): the Datong, Xinding, Taiyuan, Linfen, and Yuncheng basins [11,48] (Figure 1b). Before the Pliocene, the Linfen Basin was located in the southern part of the Graben system, with the entire Shanxi Block undergoing uplift and erosion. In the early Pliocene, the Linfen Basin began to rift and formed its initial shape. The boundary faults have remained active until the present [49].  
Figure 2: (**a**) Geologic map and (**b**) division of structural units in Linfen Basin (F1: Huoshan Fault, F2: Fushan Fault, F3: Luoyunshan Fault, F4: Hongtong–Subao Fault, F5: Guojiazhuang Fault. Light blue lines represent rivers, and black circles represent cities (LS: Lingshi, XZ: Xinzhi, HT: Hongtong, LF: Linfen, FS: Fushan, XF: Xiangfen, HM: Houma, LLS: Lvliangshan, THS: Taihangshan). Focal mechanism solutions are presented in a lower hemisphere projection [46].  
The Linfen Basin primarily comprises six secondary structures: the Xinzhi, Fushan, and Xiangfen uplifts and Hongtong, Linfen, and Houma sags (Figure 2a). The overall trend in the basin is NNE, with the Taiyuan Basin to the north and the Yuncheng Basin to the south. The eastern and western sides of the basin are flanked by uplifted regions of the Taihangshan and Lvliangshan faulted blocks [10; 49] (Figure 2b). Large active NNE-trending faults are located along the eastern and western boundaries of the basin, forming a typical 'two mountains enclosing one basin' structural pattern (Figure 2a). The western basin boundary is the Luoyunshan Fault (F3), which trends NNE but turns to NWW at the southern end of the basin and dips toward the basin. The eastern basin boundary is the Huoshan Fault (F1), which trends nearly N-S and dips toward the basin.  
The Hongtong-Subao Fault (F4) is oriented WNW in the central Linfen Basin and divides the basin into northern and southern parts. The northern part is primarily controlled by the Huoshan Fault (Figure 2a), the depocenter of which is located near Hongtong. The southern part is primarily controlled by the Luoyunshan Fault, the depocenter of which is located near Linfen [23; 50]. Since the Cenozoic, this region has undergone significant subsidence [48]. The Hongtong earthquake occurred in the Hongtong Sag in the northern part of the basin, while the Linfen earthquake occurred in the Linfen Sag in the southern part of the basin (Figure 2b).
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 4 Experiment and Analysis
### Implementation Details  
The proposed AuxDepthNet framework was evaluated on the KITTI dataset, targeting the Car category. Input images were resized to a resolution of \(1280\times 288\). The model was trained for 120 epochs with a batch size of 16, using the Adam optimizer with an initial learning rate of \(1\times 10^{-4}\) and no weight decay. A cosine annealing learning rate scheduler was employed, with the minimum learning rate set to \(5\times 10^{-6}\).  
To enhance the robustness of the model, several data augmentation techniques were applied during training. These included photometric distortions (e.g., random adjustments to brightness, contrast, saturation, and hue), cropping the top 100 pixels of the image, random horizontal flipping with a probability of 0.5, resizing to the target resolution, and normalization using RGB mean values [0.485, 0.456, 0.406] and standard deviations [0.229, 0.224, 0.225].  
For testing, augmentation was limited to cropping, resizing, and normalization without photometric distortions or flipping. The detection head employed an anchor-based design, with IoU thresholds of 0.5 for positive samples and 0.4 for negative samples. The loss function combined Focal Loss with Smooth L1 Loss to balance the classification and regression tasks. During inference, non-maximum suppression (NMS) was applied with an IoU threshold of 0.4, and predictions with a confidence score below 0.75 were discarded.  
To illustrate the convergence behavior of the model during training, we plotted the loss value across iterations. As shown in Figure 6, the training loss consistently decreases and stabilizes, demonstrating the effective optimization and convergence of the proposed framework.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Images** & **Car Instances** & **Pedestrian Instances** & **Cyclist Instances** \\ \hline Training & 3712 & 14,357 & 2207 & 734 \\ Validation & 3769 & 14,385 & 2280 & 893 \\ \hline \end{tabular}
\end{table}
Table 1: Data distribution of KITTI Training and validation splits used for monocular 3D detection.  
As illustrated in Figure 6, the horizontal axis represents the number of iterations during training, while the vertical axis denotes the loss value. The curve demonstrates that the loss decreases rapidly in the early training phase and gradually stabilizes as training progresses. This behavior indicates that the model effectively learns from the data and reaches convergence after approximately 15,000 iterations. The stable and low final loss confirms the robustness and reliability of the training procedure.
## 3 Results
### In Situ Dynamic Light Scattering
#### 3.2.2 Proof of Concept: Investigation of Silica Nanospheres  
Silica nanospheres (20 nm, NanoXactTM, nanoComposix, San Diego, CA, USA) were investigated as a proof of concept. The water-based solution with a reported concentration of 5 mg mL\({}^{-1}\) was used as received. According to the producer, these nanospheres are sensitive to lower temperatures, leading to aggregation. Their diameter is 21.4 \(\pm\) 2.7 nm, as determined by JEOL 1010 transmission electron microscope gravimetric analysis. In terms of absorption characteristics, their optical density at the 632.8 nm wavelength of the DLS-laser is lower than 0.005, meaning the light absorption by the silica nanosphere at this wavelength is negligible. The SANS data shown in Figure 9 confirms the silica nanosphere radius (R\({}_{\text{ns}}\) = 11.9 nm with polydispersity \(\sigma\) = 12%) and is not altered by the formation of any aggregates. On the other hand, the DLS investigations performed in parallel reveal that there are also larger structures in the solution of the silica nanospheres. The intensity of the first-order correlation function g\({}^{\text{I}}(\tau)\) can be attributed as follows: 18% monomer (with a signal at 0.48 ms, corresponding to an average hydrodynamic radius of 13.3 nm) and 63% to aggregates (average R\({}_{\text{H}}\) of 185 nm). Less than 15% of the intensity can be attributed to larger-sized aggregates (R\({}_{\text{H}}\) -2 \(\upmu\)m) or can be assumed to occur because of noise. Given that the scattering intensity strongly depends on the particle's volume, the percentage corresponds to 98% monomer and 2% aggregates. The reason for the presence of aggregates can be assumed as a thermal mistreatment of the sample due to temperature variation.  
Figure 9: **Left**: the electrical field first-order correlation function g\({}^{\text{I}}(\tau)\) obtained from the DLS measurement of the solution of silica nanospheres (with nominal diameter of 20 nm). The red line is a triple-exponential fit, identifying two decay times: 0.48 ms and 6.64 ms with considerable amplitudes and a third one with a comparably small amplitude which will be attributed to noise here. **Right**: the SANS cross section of the same solution (open symbols). The red line is the data fit based on the spherical form factor model (R\({}_{\text{ns}}\) = 11.9 nm) with polydispersity in size (\(\sigma\) = 12%) and instrument resolution considered [15]. Although the SANS curve does not signal the presence of aggregates in the solutions, the DLS does.
# Abstract
## 4 Use Case Scenario
### Dashboard Concept  
Figure 7 is a mock-up that proposes an integration of the network visualization in a dashboard framework. The design proposition is articulated around two main aspects: a news feed and a node-link diagram. The news feed presents users with the latest events that occurred in the network. New events appear at the top of the column, pushing the oldest ones toward the bottom in real time. Events can be filtered depending on their nature or grouped together for better visibility. The purpose of this presentation design is to highlight the time dimension of the network occurrences and enhance the perception of the dynamic aspect of the RegNet platform.  
In this dashboard concept, users are able to interact back and forth with the news feed and the node-link diagram. For instance, when a specific event in the news feed is selected, the corresponding network components are highlighted. Moreover, a dynamic visual transition shows the direction or location of the corresponding action and its relation to other organizations or smart contracts. An additional window on the right side of the dashboard also displays the previous transactions between the highlighted entities and a plot of the number of transactions per day.  
Figure 6: RegNet network visualization. The information used to populate the visualization is based on the data of the HF network deployed as a proof-of-concept in the RegNet case scenario.
## 3 Methods
### Proposed Heat Transfer Correlation  
The proposed model uses the velocity of the air mass flow calculated using instantaneous mass flows during the intake and exhaust port openings and closings, which has yet to be accounted for in any heat transfer model described previously. An average pseudo-mass flow rate inside the cylinder is expressed in Equation (13):  
\[\dot{m^{*}}=\left|\frac{\ddot{m}_{in}+\ddot{m}_{out}}{2}\right|, \tag{13}\]  
where \(\dot{m}_{in}\) and \(\dot{m}_{out}\) are the intake and exhaust mass flow rates, respectively. Furthermore, by using Equation (14), the pseudo-velocity variable is defined, which is an approximation for the averaged velocity field inside the cylinder (\(c^{*}\)), which is calculated as:  
\[c^{*}=\frac{\dot{m}^{*}}{A\,\rho}, \tag{14}\]  
In which \(A\) is the area of the piston and \(\rho\) is the density of the gases calculated using the instantaneous pressure and temperature in the cylinder. Using the velocity, the non-dimensional Reynolds number (\(Re\)) is calculated with:  
\[Re=\frac{Dc^{*}\rho}{\mu}\, \tag{15}\]  
where \(D\) is the diameter of the combustion chamber and \(\mu\) is the fluid's dynamic viscosity. Using the correlation between the Reynolds and Nusselt numbers (\(Nu\)), Nu is calculated using Equation (16):  
\[Nu=bRe^{0.7}, \tag{16}\]  
where \(b\) is the correction coefficient. It is also known that \(Nu\) is a function of the convective heat transfer coefficient (\(h_{ins}\)), a spatial dimension; in this case, the diameter of the combustion chamber (\(D\)) is used, and the thermal gases conductivity (\(k\)). The appropriate heat transfer coefficient is calculated using this correlation in Equation (17):  
\[h_{ins}=\frac{Nu\,k}{D}. \tag{17}\]  
Figure 3 depicts information on the convective heat transfer coefficient calculations adapted for this engine during scavenging in conjunction with the Woschni heat transfer coefficient calculated during combustion. The results plotted in Figure 3A depict mass flow rates in the intake and the exhaust ports. Figure 3B depicts the average mass flow rate calculated using Equation (13). Using Equations (14)-(17), the convective heat transfer coefficient results during scavenging are calculated and plotted using a solid red line, as depicted in Figure 3C. In contrast, a solid blue line depicting [PERSON]'s heat transfer coefficient calculated using Equation (7) is plotted in Figure 3C. The actual convective heat transfer coefficient actuated in the cylinder is plotted in Figure 3D. A min/max control objective is used to actuate the switch between the Woschni heat transfer coefficient during combustion and the proposed transfer model during scavenging. From Figure 3C,D, it can be observed that the blue investigation window marked as 'a' during scavenging is initiated at the moment when the exhaust port opens and lasts until the exhaust ports  
close. In contrast, the Woschni convective coefficients are actuated during the compression, combustion, and expansion strokes.
## 2 Materials
### Analyses of Field- and Satellite-Level Data  
The field canopy spectrum of the winter wheat was collected in every plot of exp1. The MODIS spectral response function was obtained in the ENVI software. Based on the spectral response function of the MODIS, the field spectral reflectance was converted to the reflectance of the MODIS blue, green, red, and NIR bands. The converted canopy reflectance of the MODIS were analyzed with the measured C\({}_{\text{ab}}\) and TN of exp1, together.  
The measured C\({}_{\text{ab}}\), DAM, and yield of the five subplots in exp2, were averaged for the ground value in the corresponding pixels of the satellite images during the experimental period.  
The satellite images with a different resolution and projection coordinates system were obtained in Hengshui City in 2017. All of the images were re-projected to the World Geodetic System in the 1984 projection. The resolution of the PAR data from the Himawari-8 satellite were resized from 5 km to 1 km, using the nearest neighbor algorithm. The resolution of the MODIS surface reflectance (MOD09A1) data were resized from 500 m to 1 km, using the nearest neighbor algorithm as well. The resolution of the MODIS FPAR (MCD15A2H) data were also resized from 500 m to 1 km, using the nearest neighbor algorithm. The resolution of the MODIS LST (MOD11A2) data was 1 km. The satellite images with the resolution of 1 km were prepared for the following inversion.