esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results  
In this section, we investigate the effectiveness of the proposed analysis using two well-known FOSs: the FO Lorenz system and the FO Liu system. Both systems serve as paradigms in the study of chaotic dynamics, providing valuable insights into complex behaviors that are pivotal in numerous applications, including robotics and automation, where precise control and adaptation to uncertain environments are critical.  
The FO Lorenz system extends the classical Lorenz system [44] into the FO domain, thereby exhibiting intricate chaotic behavior that differs from its integer-order counterpart. A recent survey [45] provides a new classification of FO Lorenz-type systems, detailing their equilibria, eigenvalues, and attractors within the three-dimensional state space. These characteristics make the FO Lorenz system particularly suitable for modeling and analyzing systems that are sensitive to initial conditions and exhibit long-term unpredictability.  
In the field of robotics and automation, chaotic systems like the FO Lorenz can be employed for secure communication between robotic units, as the chaotic nature of the system provides an inherent level of security in signal transmission. Additionally, the complex dynamics of the FO Lorenz system may be leveraged for trajectory planning and optimization algorithms, which are essential in scenarios where robots must navigate unpredictable or dynamic environments. The practical applications of the FO Lorenz system are extensive, encompassing fields such as weather forecasting, cryptography, and fluid dynamics. These applications underscore its significance in understanding and predicting complex phenomena, including those encountered in robotic control systems and autonomous navigation.  
Similarly, the FO Liu system, a variant of the classical Liu system, incorporates FO derivatives that introduce non-locality and nonlinearity, resulting in highly complex and chaotic dynamics. This system has attracted considerable attention in the field of nonlinear dynamics due to its potential applications in secure communications and cryptography. In robotics and automation, the FO Liu system could be applied to design robust controllers capable of handling nonlinearities and uncertainties in robotic systems, particularly in environments where precise synchronization and adaptability are required. Its chaotic behavior also makes it suitable for applications in swarm robotics, where chaotic motion patterns may enhance exploration and task distribution among robots.  
Thus, studying the stability, control, and synchronization of the FO Liu system is essential not only for comprehending its theoretical behavior but also for exploring its practical implications in automation and robotic systems where stability and precise coordination between components are critical. By applying the proposed analysis to these FO chaotic systems, we aim to deepen the understanding of their complex dynamics and demonstrate the efficacy of the advanced stabilization techniques introduced in this work.
## 2 Materials and Methods
### Molecular Docking Study  
The potential therapeutic efficacy of the natural compounds used as ligands, namely, 7\(\beta\),15\(\alpha\)-dihydroxy-36-palmitatelup-20(29)-ene (Ligand_1), 3-O-\(\beta\)-D-glucopyranosyl-28-hydroxy-\(\alpha\)-amyrin (Ligand_2), apigenin- 8-C-\(\beta\)-D-glucopyranoside (Ligand_3), globimetulin A (Ligand_4), globimetulin B (Ligand_5), globimetulin C (Ligand_6), bangwaoleanene A (Ligand_7), bangwaursene B (Ligand_8) and 3\(\beta\)-acetoxy-11,12-epoxytaraxerol (Ligand_9), against ten putative target enzymes, namely, acetylcholinesterase-release (PDB: 4 ARB), \(\beta\)-secretase (PDB ID:4L7)), Tau-protein kinase-I (PDB ID: 1J1C), cyclin-dependent kinase (PDB ID: 7 VDQ), glycogen synthase kinase-3 beta (PDB ID:1H8F), human 15-lipoxygenase-2 (PDB ID: 4 NRE), murine inducible nitric oxide synthase (PDB ID: 1 QW5), human inducible nitric oxide synthase (PDB ID: 4 NOS), cyclooxygenase-1 (PDB ID: 3N86) and cyclooxygenase-2 (PDB ID: 4 PH9), was investigated. Briefly, the ligand 3D-chemical structures were retrieved from the PubChem database, and some of the ligand structures were designed via ChemDraw 21.0 software due to their unavailability in PubChem. As per the requirement and to obtain reliable docking results, all ligands in 3D were optimized using Avogadro software and saved in a dot.pdb (.pdb) file. Similarly, all target enzyme 3D protein structures were retrieved from the protein data bank with individual PDB IDs, and mostly the fragmented protein structures were rebuilt through the SWISSMODEL tool before molecular docking [50]. In addition, all attached heteroatoms from each retrieved protein structure were removed and saved in.pdb format for docking studies. Following our previous report, we carried out the molecular docking study using AutoDock 4.2 [51; 52]. Based on the minimum docking score (kcal/mol), we selected the best docking pose of the proposedligands. Then, BIOVIA-Discovery Studio Visualizer (BIOVIA-DSV) software was employed for protein-ligand molecular interaction visualisation of the docking complexes [53].
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 5 Evaluation  
### Testing with Indoor/Outdoor Sample Test Set  
Figure 11 shows the accuracy of the test set with indoor and outdoor samples. We also included I/O detection using a combined sensor [1; 11] for comparison. The first test set included 70 indoor and 50 outdoor samples, with high differences in both the number and strength of the received GPS signal. The proposed scheme demonstrated an impressive result, with the highest overall rate of 98% and lowest of 96%. The results indicate that the strength of the GPS signal from the smartphone sensor can be used to extract features to be used in the I/O detection classifier, and the method can be used for other applications. Note that the sample data can be representative for the overall feature of indoor/outdoor areas because the GPS signal is essentially the same at other locations on Earth. With the indoor/outdoor test set, the I/O detection application can obtain impressive accuracy, even without performing a preprocessing phase. This high accuracy is due to the large bias of the indoor and outdoor environments, in which the GPS signal is very weak inside buildings. However, this test set is not included the semi-indoor environment, which is the most difficult environment for I/O detection; therefore, the application does not promise smooth transfer from an outdoor environment to an indoor environment (and vice versa). Thus, another test set for semi-indoor environments was added and evaluated with the indoor/outdoor environment test set. Here, we tested using the semi-indoor environment and set the samples to indoor samples.
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 4 Discussion and Future Prospective
### Rapid Construction Technology and High-Quality Filling Materials for GSER Technology  
After more than 70 years of continuous research and engineering application, China's GSER technology has formed a GSER mode based on different construction methods of entry-side filling bodies according to different geological conditions and engineering needs. Among them, the filling systems of the entry-side concrete filling, paste filling, high-water material filling, and other material filling need to be established above and below the well. Moreover, the amount of filling materials used in the entry is large, leading to the high cost of entry-side filling, while its performance cannot fully meet the long-term strong effect of ground pressure of the retained entry. Engineering applications have shown that the employment of GSER technology inevitably increases the complexity of the mining process in the working face. In recent years, with the rapid increase in coal mining intensity and raw coal production in China, working face mining speed has been increasing day by day. Currently, the entry retaining speed cannot meet the requirements of rapid advancement of the working face. Therefore, it is necessary to further deepen research on fast GSER technology by combining the problem of the mismatch between entry retaining speed and rapid mining in the working face. Additionally, high-quality filling materials with low costs, large-scale mass production, and suitability for GSER implementation processes should be developed.
## 4 Proposed HTPR Method
### Integrating Similarity Calculation and User Clustering
Furthermore, based on the definition of user-item category tendency, the calculation method of integrating similarity between users is introduced as follows:  
\[sim_{int}(u,v)=\frac{\overset{\rightarrow}{tnd}_{u}\cdot\overset{ \rightarrow}{tnd}_{v}}{\left\|\overset{\rightarrow}{tnd}_{u}\right\|\cdot \left\|\overset{\rightarrow}{tnd}_{v}\right\|} \tag{14}\]  
where, \(sim_{int}(u,v)\) denotes the integrating similarity between the user \(u\) and the user \(v\), \(\overset{\rightarrow}{tnd}_{u}\) and \(\overset{\rightarrow}{tnd}_{v}\) are respectively the feature vectors of category tendency for the user \(u\) and the user \(v\). Through integrating similarity calculation considering item clustering and user behaviors, users who have the highest similarity will be assigned into one cluster. As similar with item clustering, user clustering applies the K-Means clustering algorithm as well. Algorithm 2 illustrates the user clustering method for\(n\) users. ```
Input: The user set \(U[n]=\{u_{1},u_{2},\ldots,u_{n}\}\) The user-item category tendency matrix \(tnd[n][k_{1}]\), The number of user clusters \(k_{2}\) Output: The user clusters \(U_{1},U_{2},\ldots,U_{k_{2}}\)
1: select some users whose number is \(k_{2}\) from user set \(U\) at random, i.e., \(u_{1},u_{2},\ldots,u_{k_{2}}\), as the initial cluster centers
2:for each user \(u\in U\)
3: calculate its similarity with each cluster center, in turn, according to (14), and assign it to the nearest cluster
4:endfor
5:for each cluster
6: adjust the cluster center according to the average value of item category tendency for all users in a cluster, i.e., \(c^{\prime}_{i}=\frac{1}{m_{i}}\sum\,tnd,i=1,2,\ldots,k_{2}\), where \(m_{i}\) is the number of users for the i-th cluster
7:endfor
8:return to step 2 till the square error of the cluster criterion function, i.e., \(J=\sum\limits_{i=1}^{k_{2}}\sum\limits_{j=1}^{m_{j}}\left|tnd_{ji}-c^{\prime} _{i}\right|^{2}\) reaches convergence
```  
**Algorithm 2**User-Clustering-K-Means  
At this point, users with similar user-item category tendency will be assigned to one cluster, and thus the search scope of neighbor users can be narrowed through the offline preparation process. In this way, we reduced the computational complexity of the online recommendation.
## 4 Discussion
Therefore, such CT values show that high-density inserts and implants such as metals in 3D printed materials should not be used for DQA due to the artifact effect. The uniformity was confirmed based on the electron density of the fabricated material, and it was confirmed that it can be applied to a linear accelerator beam for treatment. All the values obtained using a 6 MV photon beam are within a difference of 3%. Since a sufficient build-up region was formed in the slab phantom for the photon beam, the uniformity of the beam profile was within 3%. Since the lung phantom, which is a low-density area on CT, was fabricated using less than 70% of the infill density material, the air density increased compared to the material and the value was not uniform. However, it can be observed that \begin{table}
\begin{tabular}{c c c c c} \hline
**Tissue** & **CIRS Material (HU)** & **Std. Dev** & **Filament** & **3D Printing Materials (HU)** & **Std. Dev.** \\ \hline Water & 3.0 & 1.6 & ABS (100\%) & \(-2\) & 6 \\ Adipose & \(-63.3\) & 2.9 & PLA (90\%) & \(-60\) & 8.3 \\ Breast & \(-35.1\) & 2.5 & TPU (100\%) & \(-32\) & 8 \\ Liver & 47.1 & 2.0 & PLA (100\%) & 42 & 8 \\ Lung inhale & \(-772.1\) & 7.5 & TPU (30\%) & \(-766\) & 84 \\  & & PLA (50\%) & \(-490\) & 53 \\ Lung exhale & \(-492.1\) & 6.1 & TPU (70\%) & \(-484\) & 41 \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 3: Comparison of CIRS phantom tissue equivalent materials and 3D printing materials. the uniformity of the profile was also within 3% in the photon beam, which is less affected by the air gap due to the characteristics of the high-energy photon beam. It is possible to experimentally measure the dose profile for the treatment beam when the output from the air cavity is used in photon beam therapy [32]. The dimensions obtained from the CT scan of the fabricated 3D printed material were reproducible within the plane and slice resolution of the CT scan we used. As a result, it was possible to manufacture and compare filament materials with similar densities to Gammax materials, and to verify human-like materials using them. Although it does not have a very low uncertainty as a solid water material does, it was able to produce a product with good performance, and the 3D printer provided the desired shape and usability.
###### Abstract  
This study constructs a system dynamics (SD)-based energy efficiency model for retrofitting existing residential buildings by considering urban residential buildings in Liaoning Province, China. In this study, we sorted the boundaries of building energy efficiency accounting, analyzed the current status of related research and system dynamics theory, determined the influencing factors and transmission mechanisms of residential building energy efficiency renovation, set the research boundaries and assumptions, drew the circuit diagrams of various subsystems, determined the main equations and parameters of the model, and ensured the reliability of the model through stability and historicity tests. Through scenario simulation and analysis, it was found that, from 2016 to 2035, the comprehensive benefits of urban residential building renovation will grow slowly in the early stage and rapidly in the late stage, which will potentially have large benefits. The socio-economic environment, governmental policies, energy-saving technologies, market demand, and other factors influence each other, among which the progress of energy-saving technologies has the greatest impact on the comprehensive benefits. Changing the inputs of a certain aspect cannot make the benefits of energy consumption, environment, and economic tripartite grow equally. The energy consumption benefit potential of centralized heating renovation is larger. The results of this study provide a theoretical basis and decision support for the energy-saving transformation of existing residential buildings.  
system dynamics; building energy efficiency; retrofit 2025  
11 May 202  
turns to marketization, retrofit programs shift focus from government to market [9; 10]; however, the marketization of retrofit programs is a huge challenge [11; 12]. The above study found that the decision of retrofit programs was related to many factors.  
Although impact studies have been conducted on the renovation of old urban residential communities, retrofit programs are affected by a series of complex processes. These studies generally ignore the fact that retrofit programs are complex and dynamic systems. The interaction between the various influencing factors indicates that even small changes can have significant effects. Moreover, relevant studies that focus on how retrofit program strategy can maximize energy savings while maintaining cost are lacking.  
Therefore, in order to overcome these limitations mentioned in the literature, this study developed a system dynamics model considering market policies, regulations, and technology. We first considered various factors that affect retrofit programs on a macro scale, and based on factor interconnection, we established a transmission mechanism model for the old urban residential communities. In order to validate the proposed model, this research conducts a case study on Liaoning Province in China and assesses retrofit program effects.
## 6 Current Applications of DTs in Transportation Infrastructure
### Case Studies
These innovations have been successfully applied in the dynamic testing, modal identification, condition assessment, and comfort evaluation of significant projects like the large-scale pedestrian bridge in Matagarup and the long-term monitoring and performance evaluation of the Rockingham Freeway Bridge, among other landmark structures. 6. In the United States, state Departments of Transportation (DOTs) are piloting digital twin (DT) projects to enhance real-time traffic management and asset maintenance. For example, California's I-210 Pilot integrates sensor data, connected vehicle inputs, and traffic signals to reduce congestion through adaptive control. These DT-driven insights also improve safety, incident response, and operational efficiency [120]. Colorado's I-70 corridor project combines continuous sensor data with AI-based traffic predictions and cloud analytics to optimize real-time traffic flow [121]. These initiatives aim to streamline congestion management and enable predictive maintenance for highway infrastructure, contributing to cost-effective and resilient transportation networks. The U.S. Federal Highway Administration collaborates with research institutions on DT studies, reflecting a growing interest in leveraging DTs for nationwide infrastructure modernization. 7. The Netherlands, renowned for its advanced logistics and water management, has deployed a digital twin at the Port of Rotterdam to optimize maritime traffic and multimodal coordination. Real-time data from ships, road transport, and rail systems are integrated into a unified DT platform for route optimization and congestion reduction. Additionally, Dutch authorities are exploring national-level, cross-sector DT strategies that integrate water management, energy, and transportation infrastructure to enable holistic planning and operational efficiency [122; 123]. To illustrate how various cities and regions have leveraged DT technologies to address specific transportation challenges, Table 3 presents concise case studies highlighting their primary focus, key technologies, and notable achievements. These case studies illustrate the diverse applications of DTs in transportation infrastructure and underscore the need to address challenges such as data integration, model accuracy, and system interoperability to fully capitalize on the benefits of DT technology.
## 4 Experiments
### Experimental Results  
In this study, we utilized data collected over 36 months from 14,744 interconnected elevators as our research subject. Specifically, data from the 30 months leading up to March 2023 were designated as the training set for model training and optimization, while data from March to May 2023 were selected as the test set for evaluating the algorithm's performance and effectiveness. To thoroughly assess the predictive capabilities of the proposed model, we conducted a detailed comparative analysis with currently popular time series prediction models, including Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNNs), and Transformer models. Additionally, to further explore the unique value of the proposed model, we performed a series of ablation experiments. These experiments contrasted the performance of the RPM-ETC model without the temporal compression mechanism against the RPM-ETC model that incorporates the temporal compression mechanism, thereby verifying the impact of the temporal compression mechanism on model performance. Through this approach, we aim to comprehensively evaluate and demonstrate the superiority and practicality of the proposed model in time series prediction tasks.
## 4 Experimental Datasets and Evaluation
### Experimental Settings  
The model training and testing in this study are based on the PyTorch deep learning framework and use open-source image processing modules, including TorchVision, Skimage and Matplotlib. To accelerate the calculation, the computing device used in this study is the NVIDIA GeForce GTX 3090 graphics processing unit, with a memory capacity of 24 GB, and CUDA 11.0 has been installed for GPU acceleration. Considering the constraint of GPU memory resources, all image samples are randomly cropped to 256 \(\times\) 256 pixels to meet the memory requirements for model training and cross-validation in each round. In the hyper-parameter configuration of the model, we performed multiple comparative experiments to determine the optimal settings for each model. The Adam optimization algorithm [51] is used for model training, with an initial learning rate set to 0.0001. To suppress overfitting, L2 regularization is introduced in all convolutional layers, and the weight decay coefficient [52] is set to 0.0001. Additionally, considering the limitation of the GPU memory of the computing device, the training of each model takes batches of 18 images as input and iterates 200 epochs on each dataset. To ensure the fairness of comparative experiments between different models, all comparison models are trained under the same software and hardware environment. The training accuracy and loss profiles [53] of the proposed AGSC-Net architecture on both datasets are presented in Figure 9. As shown, the model accuracy consistently improves over training iterations and eventually converges to approximately 0.94 with minor fluctuations. Concurrently, the loss monotonically decreases and reaches a plateau, indicating effective optimization of the AGSC-Net model. Unless otherwise specified, all comparative models were trained under the same hardware and software configuration detailed previously to enable fair benchmarking against the proposed AGSC-Net.
## 4 Experimental Setup
### Illuminance Characteristic Analysis of the ExDark Dataset  
As users of the public dataset, we conducted a detailed analysis of the images in the ExDark dataset to infer their illuminance characteristics. Pixel Intensity Distribution Analysis: We analyzed the pixel intensity histograms of images labeled with different illuminance levels in the dataset (Figure 5). The results showed that images labeled 0.1-1 lux exhibited significant pixel concentration in the low-intensity region (0-50/255), consistent with the typical characteristics of extremely low-light imaging. Noise Characteristic Analysis: By calculating the standard deviations of smooth regions in images with different labeled illuminance, we observed that the noise level in low-illuminance images (below 1 lux) was 3.2\(\times\) higher on average than that in images labeled 5-10 lux. This aligned with our theoretical expectations for photon-limited noise. Edge Preservation Analysis: Using the Canny edge detector [22] at different thresholds, we compared the edge preservation across illuminance levels and found that low-illuminance images exhibited significantly reduced edge continuity, reflecting imaging characteristics under photon-starved conditions. Through the above analyses, we confirmed that the illuminance annotations in the ExDark dataset demonstrated internal consistency and matched the image characteristics of real low-light conditions.