esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Video Querying Latency  
Image and video processing require expensive data extraction and storage resources [4; 54]. The most accurate modern extraction methods are based on convolutional neural networks (CNNs). Unfortunately, these methods require several milliseconds per image/frame when using modern GPUs. Therefore, the final cost of queries on a huge video corpus can be prohibitive [43].  
Query latency refers to the computation time between the start of query processing and the retrieval of the desired image or video event. There are two main processing times: offline processing (or query time) and online processing (or ingest time). Offline processing has no latency restrictions and can take minutes, hours, or even days. It is a very popular approach due to its low cost, requiring powerful hardware only when the application has latency constraints [12]. On the other hand, for online processing, latency is crucial for detecting events in real-time. It requires modern GPUs in video ingestion, deep learning models for image processing, and complex event detection algorithms. These features make it more expensive and complicated than offline processing. For example, the authors in [5; 12] used cheap CNNs at ingest time to create an approximate index of all possible object classes in each frame to process queries for each class in the future, balancing the use of cheap models and expensive convolutional networks at query time to improve latency and throughput as needed.  
As far as the authors of this article are aware, the VidCEP work [6; 13; 53] and other related studies, including the recent PhD thesis [33], are the first to show efficient query processing with low latency (e.g., near real time). However, the approach uses intensive graph database operations and short-term window analysis, which significantly limits the size of the search range, and the processing time increases significantly with large windowsizes. Currently, there is a gap in video querying with some real-time concerns, which promotes the need for new solutions, as explained in the following sections of this article.
## 3 Experimental Analysis of the Bearing Capacity of the Vertical Member
### Experimental Analysis of the Shear Capacity of the Wheel on the Vertical Pole  
#### 3.1.1 Theoretical Value of the Wheel Shear Capacity  
The connection between the vertical pole wheel and the horizontal bar was categorized by three working conditions during the normal construction of the formwork support. First, the wheel was in a four-sided shear state when the vertical pole was the central pole, and the wheel was connected to four horizontal bars. Second, the wheel was in a three-sided shear state when the vertical pole was used as the side pole, and the wheel was connected to three horizontal bars. Third, the wheel was in a two-sided shear state when the vertical pole was a corner pole, and the wheel was connected to two horizontal bars. As shown in Figure 11, the shaded area of each figure is the schematic diagram of the wheel shear zone under different working conditions.  
The ultimate compressive bearing capacity of the wheel under different working conditions was converted into the ultimate bearing capacity of the weld at the connection between the vertical pole and the wheel. The shear capacity of the wheel was calculated by assuming that the wheel was only subjected to shear force without a bending moment. The outer diameter (\(D\) = 48 mm) of the formwork support vertical pole, and the weld leg size (\(h_{f}\) = 6 mm) of the weld connecting the wheel and the vertical pole were obtained. According to the tables, the shear strength design value of the weld was \(f_{v}^{w}\) = 160 N/mm\({}^{2}\), and the weld of the wheel was double-sided fillet welding. The compressive bearing capacity of the wheel was calculated as follows:  
1. When the four sides of the wheel are sheared, \[F_{max}=\sum l_{w}\cdot h_{e}\cdot f_{v}^{w}=2\times 98\times 0.7\times 6\times 1 60=131.71\ \text{kN};\] (6)
2. When the three sides of the wheel are sheared, \[F_{max}=\sum l_{w}\cdot h_{e}\cdot f_{v}^{w}=2\times 73.5\times 0.7\times 6\times 1 60=98.78\ \text{kN};\] (7)
3. When the two sides of the wheel are sheared, \[F_{max}=\sum l_{w}\cdot h_{e}\cdot f_{v}^{w}=2\times 49\times 0.7\times 6\times 1 60=65.86\ \text{kN};\] (8)  
where \(\sum l_{w}\) is the measured weld length under different working conditions.
## 4 Experiments and Discussion
### Dataset Descriptions  
In this study, we used the ISPRS Vaihingen and Potsdam datasets to evaluate the PLGTransformer model. Both datasets contain high-resolution remote sensing images and digital surface models (DSM), providing detailed label information for semantic segmentation tasks.  
* Vaihingen Dataset: The Vaihingen dataset consists of 16 high-resolution orthophotos, each measuring 2500 \(\times\) 2000 pixels. Each image includes three spectral channels: near-infrared (NIR), red (R), and green (G), along with a DSM at 9 cm ground sampling distance (GSD). The dataset includes five foreground classes (buildings, trees, low vegetation, cars, and impervious surfaces) and one background class (clutter). In our experiments, 12 images were used for training and 4 for testing, following a commonly used protocol. Additionally, 20% of the training samples were randomly selected as a validation set. All images were divided into non-overlapping 256 \(\times\) 256 patches for training and evaluation.
* Potsdam Dataset: The Potsdam dataset contains 24 orthophotos with a resolution of 6000 \(\times\) 6000 pixels, and four spectral channels: red (R), green (G), blue (B), and infrared (IR), at 5 cm GSD. It shares the same class definitions as the Vaihingen dataset. We adopted the same patch generation and training/validation strategy as used in Vaihingen, with an 80/20% split for training and testing, and 20% of training samples used for validation.
## 2 Materials and Methods
### Validation with NWP Data Reanalysis
#### 2.3.2 Comparison Methods  
On a daily basis, the MG-APP software processes GNSS observations collected every second, while the output ZPD values have a time frequency of 30 s. Such ZPD values are further filtered to eliminate outliers. ZPD physically plausible values are between 1800 and 2800 mm [45; 46], so this range was the one adopted in the filtering phase.  
The values of ZPD were obtained from the MERRA-2 data by discrete piecewise linear integration (i.e., sum) of the refractivity, for any selected pixel, along the vertical profile given by the 42 pressure levels, as described in [6]. Note that since MERRA-2 data do not contain the surface values of T, QV and HGT, these were estimated through a linear extrapolation or interpolation, when feasible, of the values at the closest vertical levels.  
The comparison between the observations and model-derived data started from the spatial and temporal matching. The model pixels were selected according to the ship position at any observation time. As the MERRA-2 data are provided at synoptic time, only a very limited number of perfect pairs matching is possible, each six hours. So the MG-APP ZPD data were further processed to extract the average over a one-hour window centred on the synoptic time (i.e., 30 min before and 30 min after). The resulting points available for the comparison are visible on Figure 2, where the main ship routes clearly emerge as the most densely pointed areas. However, there are also a high number of points outside of these main routes, which show how the measurement dataset from a ship is much wider regarding the spatial coverage.
## 4 Experiments and Results
### Ablation Study  
Comprehensive ablation studies on the LiTS dataset were conducted to evaluate the effectiveness of the proposed G-UNETR++ for liver segmentation. We evaluated the effectiveness of the proposed gradient-enhanced encoders (G-Encoders) for liver segmentation. Table 3 shows the result of the ablation study of G-UNETR++, which indicates that the proposed G-Encoders effectively improve the performance of liver segmentation. To evaluate the effectiveness of the proposed loss function, we trained our G-UNETR++ with dice loss + CE loss and the proposed loss, respectively. Table 4 shows that the proposed loss function is effective in improving the performance of liver segmentation. Lastly, we evaluated the effectiveness of the proposed post-processing method for liver segmentation. Table 5 shows that after post-processing, the performance of liver segmentation has been improved.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss Function** & **DSC** & **VOE** & **RAVD** & **ASSD** & **MSSD** \\ \hline Dice loss + CE loss & 0.9731 & 0.0523 & 0.0189 & 0.7541 & 19.0292 \\ The proposed loss & 0.9737 & 0.0511 & 0.0201 & 0.6510 & 18.4870 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study for loss function over the proposed G-UNETR++ on LiTS dataset.  
Figure 7: Precision–recall curve analysis for liver segmentation over one example in the most challenging dataset, Sliver07.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **DSC** & **VOE** & **RAVD** & **ASSD** & **MSSD** \\ \hline UNETR++ (Baseline) & 0.9726 & 0.0531 & 0.0186 & 0.7876 & 18.9311 \\ + G-Encoders (G-UNETR++) & 0.9737 & 0.0511 & 0.0201 & 0.6510 & 18.4870 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study for the proposed method on LiTS dataset.
## 3 Simulation Results and Discussion  
### Dynamic Properties of Charge Density  
The dynamic characteristics of the surface charge density of the XS anode were studied, as shown in Figure 4. The electron cloud follows a Gaussian distribution on the anode, and the electrons are collected by horizontal strips and vertical strips. The diffusion radius of the electron cloud increases with the distance d. On the one hand, a charge cloud that is too narrow can cause image distortion. On the other hand, excessive diffusion of charge cloud footprints can lead to distortion at the edges of the image, resulting in a reduction in the signal-to-noise ratio and thus in resolution. The number of strips we want to cover is 3-5, so we want the distance between the MCP and the anode to be as small as possible, but the actual distance also needs to be considered. So, we chose the distance of d to be 0.5-2 mm.
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 5 Analysis and Discussion of Experimental Results  
The experimental results are discussed on the bases on imperceptibility, robustness, complexity, cost effectiveness, and comparison with existing work. Imperceptibility means measuring the quality of stego file using different evaluation parameters. Whether the secret message can be exposed or not using some attacks, measures the robustness of the technique. We also discussed concealing capacity and total time taken by the technique. Cost effectiveness of the proposed technique is also discussed. Lastly, a comparison of the proposed work is done with some existing techniques. For comparison, we have selected two papers: one for comparing imperceptibility and robustness, and the second paper is used to compare the complexity of work in terms of time and concealing capacity.
## 4 Discussion
### Mitigation of GHG Emissions  
Mitigating greenhouse gas (GHG) emissions in railways is essential to reduce the environmental impact of this transportation sector.  
In the railway system of Pernambuco, electric energy is the main source of GHG emissions, as demonstrated in graph 01. Adopting electrified railway systems, using renewable sources such as solar energy, can significantly decrease GHG emissions. Installing solar panels on station rooftops can significantly reduce electricity consumption, taking advantage of the abundant sunlight in the northeast region of Brazil, where Pernambuco is located.  
Currently, Pernambuco's light rail operates on diesel energy, a fossil fuel with high GHG emission potential. Replacing this fuel with renewable energy can substantially cut down on GHG emissions. A study by Mariko, which analyzed the feasibility of photovoltaic energy for Brazilian light rail, revealed that installing photovoltaic systems on the rooftopsof the fleet operated in Brasilia's railway could save around BRL 800,000 and reduce CO\({}_{2}\) emissions by 540 tons over 10 years [53].  
Efficient electricity use plays a crucial role in reducing energy consumption and emissions in urban railway transportation. Recycling is an effective method to save energy, as demonstrated by Delhi's metro. Electricity generated during train braking is repurposed for other operational modes, resulting in nearly 35% of Delhi Metro's electricity consumption being regenerated by the system [54].  
Additionally, managing energy consumption in stations and administrative buildings can further decrease GHG emissions. Mariko's research, which implemented a conscious and sustainable energy consumption program in a Brazilian Northeast railway system, achieved a 56% reduction in monthly electricity consumption and over 50% in GHG emissions [55]. Applying energy-saving techniques like this can be beneficial in reducing traction emissions in Brazilian railway systems.  
Improving the energy efficiency of Pernambuco's light rail is an important form of mitigation. This can be achieved through engine modernization, traction system enhancement, route and speed optimization, and the use of more efficient technologies. As demonstrated by [PERSON] [52], a modern engine with a common rail system, powered by biodiesel RME (rapeseed methyl ester) and its blends with diesel, can reduce average concentrations of carbon monoxide, hydrocarbons, and particulate matter.
# Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain
###### Abstract  
The shift from traditional on-site to off-site construction marks a significant evolution in the construction industry, characterized by increasing levels of prefabrication. These advancements enhance construction efficiency, reduce lead times, and mitigate environmental impacts, leading to modular integrated construction (MiC). However, MiC presents complex supply chain challenges, particularly in the transportation of prefabricated components and fully integrated modules. This study addresses these challenges by employing a multi-agent simulation using AnyLogic to optimize MiC transport logistics. The simulation models the interactions of various agents involved in the MiC process to improve operational efficiency and reduce costs. Results demonstrate that using three vehicles per supplier minimizes total transport costs, effectively balancing fixed and variable expenses while eliminating penalties for project delays. The findings highlight the cost efficiency of MiC, showing potential savings due to centralized assembly and optimized logistics. These significantly reduce material transportation and related costs, contributing to the overall efficiency and sustainability of construction projects. These insights underscore the value of multi-agent simulation in addressing the complexities of MiC supply chains.  
off-site construction (OSC); modular integrated construction (MiC); multi-agent simulation; construction supply chain management; transportation optimization +
Footnote †: journal: Applied Sciences
## 3 DSPOT-Enhanced Self-Evolving Neural Networks  
Motivated by the observations in Section 2, we seek to design a short-term forecast of a wind farm generation method that accounts for not only the heterogeneous dynamics of each class of wind turbines but also the time-varying dynamics of ramp and non-ramp events. Inspired by the success of artificial intelligence (AI) in a wide range of fields, our goal is to use neural networks (NNs) to learn these different dynamics of power outputs. Although there are several attempts along this line (e.g., ANNs [41] and LSTM [42]), these approaches use a single model and overlook the extreme ramp events, which leads to a poor forecast performance, especially for ramp events. Additionally, to train good NNs, it is critical to have high-quality training datasets (i.e., the ramp and non-ramp datasets should be well separated), which is a challenging task due to the time-varying dynamics of ramp and non-ramp events. Further, when training NNs, it is challenging to find the optimal topology as well as the hyperparameters of NNs.  
Figure 4: Empirical ramp distributions of GE and Mitsubishi turbines in different time windows \(l\) and different time periods, which follow the generalized Pareto distribution.  
To tackle these challenges, we propose DSPOT-enhanced self-evolving neural networks, namely the DSN, for the short-term wind farm generation forecast. The idea is to (1) first classify non-ramp and ramp events using DSPOT, which uses dynamic ramp thresholds to account for the time-varying dynamics of non-ramp and ramp events, and (2) then train different NNs for each dataset to learn the heterogeneous generation dynamics of the different classes of wind turbines, where these NNs can self-evolve based on the data, in order to account for the non-stationarity and reduce the overhead of tuning the topology and hyperparameters of NNs.  
The design of our model is illustrated in Figure 1. The historical data are first classified into non-ramp, ramp-up, and ramp-down datasets by DSPOT, in which dynamic thresholds are determined based on recent observations in a moving window with size \(d\), in order to appropriately define ramp and non-ramp events over time.  
Then, we use NeuroEvolution of Augmenting Topologies [39] to train NNs using the classified datasets, in which the NNs evolve based on a genetic algorithm to obtain the best topology and hyperparameters of NNs. As a result, 6 NNs, i.e., 3 for Mitsubishi and 3 for GE, are built (see Figure 1). As the efficacy of NNs relies on the quality of training datasets, i.e., how good different ramp events are labeled, a Bayesian optimization-based method is proposed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the NNs. Ultimately, the proposed DSPOT-enhanced self-evolving neural networks form a closed loop for optimizing the performance of wind farm generation forecast purely based on the data. In what follows, the design of each component of the model is described in detail.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 3 Experimental Setup and HEM Algorithm
### Experimental Methodology  
Two 24-h experiments were performed to demonstrate the ability of the designed circuitries to monitor and control the electrical appliances after setting up the hardware and specifying the inputs and outputs of the energy management algorithm. The economic benefit of the HEM system was also analyzed. Firstly, the power consumptions of the appliances under normal utilization over the duration of a day were monitored and recorded. The temperature and illuminance were also recorded. The HEM system was then applied for another 24 h under exactly the same circumstances of the appliances, and similar recordings as in the previous experiment were obtained.
## 4 Results and Discussion
### Experimental Setup and Benchmark Sets  
The proposed approaches were executed on a PC equipped with a 64-bit Windows 11 Pro operating system, an i5-11400H processor clocked at 2.70 GHz, and 32 GB of RAM. The swarm size and maximum iteration are set to 30 and 500, respectively. Also, the proposed algorithms were run 30 times. Then, average optimization results were obtained.  
The proposed approaches were tested on 23 benchmark functions, including the unimodal functions shown in Table 1 (F1-F7), multimodal functions shown in Table 2 (F8-F13), and fixed-dimension multimodal functions shown in Table 3 (F14-F23) [1,28].  
Figure 4: HHHOWOA1 PSO and HHHOWOA2 PSO approaches’ pseudocode.  
During the exploitation phase, the optimization algorithm's strength and proficiency may be evaluated using the unimodal functions. The multimodal functions assess the exploration stage and the ability of the algorithm not to get stuck at the local optimum. The equilibrium between the exploitation and exploration stages of optimization approaches may be assessed using fixed-dimension multimodal functions, and these functions imitate the difficulty of finding the best responses to problems in the actual world with several local optima.
## 5 Experiments
### Response Time for Actuators and Events  
One of the advantages of a fog computing system is the reduction of the response latency. With ZiWi, it is straightforward to compare the difference in response time between OpenHab (the home automation service in ZiWi's fog) and remote cloud services like Telegram and IFTTT.  
Regarding the fog response, the time to turn a light on was measured when the LDR detected low luminosity, and an average of 0.02 s were measured. The average time to activate a relay was very similar: 0.018 s for switching it on and 0.016 s for switching it off.  
With respect to the use of third-party services running in a remote cloud, the time required by Telegram to send messages with every relay commutation was first measured. The results showed that the average time to receive the messages in a smartphone making use of push notifications was 2.735 s. In the case of IFTTT, a rule that checked ZiWi's luminosity every 30 min was configured and, if the values received were too low, it turned a light bulb on. In such a scenario, it was determined that an average of 51.388 s went by since the rule was checked until the light was turned on.  
Figure 15: Communications architecture for the demo tests.  
The obtained results allow for concluding that fog services react clearly faster than cloud services. In the case of Telegram notifications, they are roughly 160 times slower than fog event detections, while such events are 2569 times faster than IFTTT when turning a light on when detecting low-luminosity. Nonetheless, note that the experiments were performed in very specific circumstances, so, although these first measurements are promising, a detailed analysis should be performed. In addition, note that cloud services offer many complex and compute-intensive applications that would be difficult to run smoothly in a simple fog layer so, for every service conceived, the IoT designer has to look for a good trade-off between response time and functionality.  
ZiWi response times can be compared to the ones obtained by other state-of-the-art systems, but it is important to note that it is difficult to compare them in a fair way due to their differences in architecture, topology, hardware, protocol compatibility or communication transceivers (as it is was previously described in Section 2.2.1). Thus, the system presented in [19] requires a 2 s delay for executing commands on an actuator in an indoor scenario (using the ZigBee network), while 25 s are required outdoors (using a GSM interface). In [25], it is indicated that 0.5 s are needed both for reading sensor values and for executing commands on actuators. In addition, other authors measured response times for actions performed by the sensor nodes (without relying on upper layers), which clearly reduces response time, but usually increases the computational requirements of the nodes. Examples of such a kind of response time are measured in [41; 43], where 1, 233.19 \(\upmu\)s and 32.9 ms were needed to detect events and react to them. As a summary, Table 8 compares all the previously mentioned response times.