esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Benchmark FEM vs. BEM  
To verify the accuracy of the BEM formulation, a comparative analysis was conducted between the BEM and FEM simulations. The BCs are illustrated in Figure 13a. The boundary grain interfaces were modeled as perfectly bonded, thereby ensuring continuous displacements and traction equilibrium. Additionally, low-reflection BCs were enforced on the left and right sidewalls to minimize the impact of side wall reflections on the receiver probe. The transmitter probe was defined at the upper edge of the micro-structure, and a prescribed traction with a 3-cycle tone burst with a Hanning window and center frequency of 1 [MHz] was set (as depicted in Figure 13b). The overall simulation duration was 35 [\(\upmu\)s], with a time step of 10 [ns] and an excitation time interval of 3 [\(\upmu\)s]. The micro-structure dimensions were 12.5 \(\times\) 30 [mm\({}^{2}\)] [20]. A plane strain condition was assumed for the analysis by neglecting the stiffness constants associated with the third dimension, thereby reducing the rotated stiffness matrix from 3D to 2D [20; 41; 42; 43; 44]. Two materials were considered in the study: aluminum (Al), with its material properties being identical to the previous simulation; and Copper (Cu), with its material properties being specified as a Young's modulus of \(E~{}=~{}124\) [GPa], a Poisson's ratio of \(\
u=0.34\), and a density of \(\rho=8900\) [\(\frac{\upmu\text{kg}}{\text{m}^{3}}\)].  
Figure 14 presents the comparison between the BEM and FEM solutions, in which both utilized 300 grains, for the Al and Cu materials. Since the wave velocity is influenced by the material properties, it was observed that the BEM model accurately captured this variation. Specifically, the Time of Arrival (ToA) for Al was 25% smaller compared to Cu, thus reflecting the fact that the wave velocity in Al is 25% greater than in Cu. Furthermore, a parametric study was conducted in the BEM simulation, whereby different numbers of grains were considered as ranging from 30 to 300. Figure 15 showcases the A-scan (back wall) signal received by the receiver probe for Al (Figure 15a) and Cu (Figure 15b). The symbol # in the figures refers to the number of grains. The figures demonstrate a remarkable agreement and alignment in terms of signal amplitude and phase between the BEM and FEM models, thus confirming BEM's consi  
Figure 13: Input model. (**a**) Input model and boundary conditions. (**b**) Prescribed tone burst.
## 3 Application
### Selection of Near-Fault Ground Motion Records  
The object of this paper was choosing near-fault ground motion. The accuracy and efficiency of the structural fragility analysis relies on selecting a reasonable classification and the appropriate number of seismic ground motions. The seismic ground motions should meet two requirements: the first is the ability to simulate the random process  
Figure 3: The element model for gap.  
Figure 2: The isolated girder bridge structural configuration.  
of an earthquake disaster itself, and the second is the selection of a large number of adequate seismic ground motions to reduce the impact of ground motion uncertainty on the results of the seismic fragility analysis. The general criterion for ground motion selection was to propagate uncertainty in the magnitude and epicentral distance. In the present study, a suite of ground motions typical of the bridge location were chosen from the Next Generation Attenuation database of the Pacific Earthquake Engineering Research Center (NGA-West2). Then, 100 near-field ground motions with a fault distance of less than 20 km and with significant velocity pulse effect were selected. The general properties of the selected ground motions were as follows: (1) the source-to-site distance (\(r\)) was \(0\leq r\leq 20\) km, (2) the moment magnitude of the selected near-field ground motions ranged from 6.5 to 7.6, (3) the average shear-wave velocity of the upper 30 m was accepted from 260 to 500 m/s, and (4) there was significant pulse velocity (a pulse-like waveform with a long period, rich medium and long-period components, and a large peak value). The selected near-fault ground motions covered a wide range of intensities to ensure that the selected suite represented both large and small earthquakes [23]. The distribution of PGAs in 100 near-fault ground motion records is shown in Figure 4.
## 3 Thermal Buckling Analysis and Optimization
### Thermal Buckling Analysis Model  
In the finite element model, the insulation material is not considered since Saffil insulation is much softer than other materials that make up the webs and face sheets. Therefore, it can be safely omitted from all structure analyses without introducing any palpable error. While the 2D configuration of the unit cell is determined, the length of the panel is still undefined. The length can be characterized by the number of unit cells instead. In this study, the number of unit cells in one panel was fixed at four and the shape of the panel was assumed to be square. In this case, bucking analysis should be carried out by including the whole ITPS panel because the boundary conditions for a unit cell were not defined. By imposing symmetric boundary conditions, two cells are sufficient to model the overall panel. An eight-node shell element with six degrees of freedom at each node is used for the buckling FE model. Figure 4 shows one-quarter of an ITPS panel. In this study, edge A and edge B are actual panel edges; the BFS edges were fixed in all three displacements and all three rotations. TFS edges C and D were fixed in all three rotations while allowing all three displacements. Plane A and plane B are symmetric planes. Edges on plane A were fixed in \(x\)-direction displacement and \(y\)- and \(z\)-direction rotations. Similarly, edges on plane B were fixed in \(y\)-direction displacement and \(x\)- and \(y\)-direction rotations.  
The loads for the buckling problem are temperature loads and aerodynamic pressure loads. Temperature distributions were obtained from previous 2D analysis at different re-entry times in the form of interpolation functions in one variable (\(z\)-coordinate). These functions are plotted in Figure 5. By using interpolation, the temperatures were assigned to each node of the 3D shell mesh according to the \(z\)-coordinates. The buckling problems were separately analyzed for different re-entry times. In this study, four critical times were chosen: (1) Time 1: when TFS reaches maximum temperature; (2) Time 2: when the mid-point of the web reaches maximum temperature; (3) Time 3: when the BFS reaches maximum temperature; and (4) Time 4: when the temperature difference between the TFS and BFS reaches the maximum. It is worth noticing that temperature distribution in Time 1 and Time 4 is very similar. Moreover, the overall temperature in Time 4 is lower than Time 1. Considering that thermal stresses caused by the temperature load were the critical load in this problem, thermal buckling analysis at Time 4 can be removed.  
Figure 4: Origin shell model for thermal buckling analysis.  
In the thermal buckling analysis, 40 smallest vectors and eigenvectors were extracted in case of negative values. For this design, negative values do not make any sense and could be ignored. When the smallest positive eigenvalue is above 1, the structure is presumed to be safe; otherwise, structural buckling occurs.
# Technological Development and Application of Photo and Video Theodolites
## 2 Technological Development  
We can differentiate two main phases in the development of photo and video theodolites. The first phase is considered when the theodolite was a supporting tool for photogrammetric cameras, called photo theodolites, and the second phase is considered when the camera became a supporting tool for theodolites, i.e., tachymeters and total stations, called video theodolites.  
The second phase involving the technological development of video theodolites can be divided into four main phases according to the main innovations in their development. In this section, each phase is described, and the main innovations are explained.
###### Abstract  
Multi-access edge computing (MEC) brings high-bandwidth and low-latency access to applications distributed at the edge of the network. Data transmission and exchange become faster, and the overhead of the task migration between mobile devices and edge cloud becomes smaller. In this paper, we adopt the fine-grained task migration model. At the same time, in order to further reduce the delay and energy consumption of task execution, the concept of the task cache is proposed, which involves caching the completed tasks and related data on the edge cloud. Then, we consider the limitations of the edge cloud cache capacity to study the task caching strategy and fine-grained task migration strategy on the edge cloud using the genetic algorithm (GA). Thus, we obtained the optimal mobile device task migration strategy, satisfying minimum energy consumption and the optimal cache on the edge cloud. The simulation results showed that the task caching strategy based on fine-grained migration can greatly reduce the energy consumption of mobile devices in the MEC environment.  
edge computing; task migration; task caching; genetic algorithm +
Footnote †: journal:  
0000-0001-4000-0000-0000-0000-0000-0000-0000-0000-0000-00000  
To the best of our knowledge, the existing work on MEC has mainly focused on the following two aspects: edge caching and task offloading. Unfortunately, because the main concern of edge caching is the storage capacity of the edge cloud, researchers have rarely considered the computing and storage capabilities of the edge clouds simultaneously. Furthermore, with regard to task offloading, most of the existing work has focused on task migration and has only considered coarse-grained migration or simple dependencies rather than complex dependencies. Therefore, in this paper, due to the low latency and relatively limited capability of the edge cloud, we study the problem of task caching for MEC, and propose a joint optimization of task caching and task offloading with complex dependencies using the fine-grained partitioning model to solve the problem of energy consumption optimization using the genetic algorithm, while satisfying user requirements.  
The rest of this paper is organized as follows: Section 2 examines the relevant studies on MEC and task migration and offloading. Section 3 presents the problem definition and solution, in which a task execution time model, energy model, and genetic algorithm (GA)-based optimization algorithm are given. Section 4 presents the simulation results and analysis, and the final section concludes the paper and discusses future work.
# Parallel Crossed Chaotic Encryption for Hyperspectral Images
###### Abstract  
Hyperspectral images (HI) collect information from across the electromagnetic spectrum, and they are an essential tool for identifying materials, recognizing processes and finding objects. However, the information on an HI could be sensitive and must to be protected. Although there are many encryption schemes for images and raw data, there are not specific schemes for HI. In this paper, we introduce the idea of crossed chaotic systems and we present an ad hoc parallel crossed chaotic encryption algorithm for HI, in which we take advantage of the multidimensionality nature of the HI. Consequently, we obtain a faster encryption algorithm and with a higher entropy result than others state of the art chaotic schemes.  
chaotic encryption; hyperspectral images; parallel computing +
Footnote †: journal: Applied
## 4 Results
### Comparing Models
#### 4.4.2 Applicability of Models Trained on DS1 (Climate Change Dataset) to DS2 (COVID-19 Dataset)  
Machine learning models trained on the DS1 were tested on the DS2 dataset in order to test the applicability of the trained models to deception data on another topic (Table 10). The table shows accuracies obtained using the machine learning models for each of the selected data processing methods. The data is also presented graphically in Figure 7. For each model, the maximum achieved accuracy obtained with the LIWC analysis (green), n-gram analysis (blue), and combined analysis with n-grams and LIWC (red) is indicated. The maximum of the three listed values is bold for each model.  
Figure 7: Models trained on DS1 (climate change dataset) and tested on DS2 (COVID-19 dataset).  
Considering the performance of models trained on DS1 and tested on the DS2 data, the maximum prediction was achieved with the multinomial Naive Bayes (73.82%) with the analysis using the combined approach. The random forest model gave the most accurate results (71.30%) analyzing n-grams. The results obtained with the LIWC analysis are generally less favorable than those obtained using other data processing approaches. When using models trained on DS1 and tested on DS2 data, a drop in the model's performance was recorded with all three approaches compared to training and testing the models on the DS1 data.
## 1 Introduction
-based techniques and detection-based techniques. Additionally, each of these categories can be grouped into a further two subcategories. Depending on how they are used, the intrusion detection systems can be of two types: host-based IDS (HIDS) and network-based IDS (NIDS). Depending on how intrusions are detected, IDS techniques can be categorized as either signature-based or anomaly-based systems. The first is established based on predefined behavioral patterns. As a result of that, a new or unknown threat cannot be detected with signature-based IDS. In order to detect any anomaly in the system's behavior and identify it as a potential threat, anomaly-based IDS employ specific network traffic characteristics. Currently, there are few ML-based solutions that are being used to produce a practical IDS to aid IIoT networks in discovering system irregularities. However, typical centralized systems are susceptible to a single point of failure (SPOF) [6], in addition to other limitations. One feasible solution to these issues is FL, which enables devices to train a common ML model without trading or receiving data. This paper presents a method for accurately identifying unwanted intrusions in wireless IIoT networks in order to protect the privacy and security of IIoT data. We designed and tested an FL-based IIoT network capable of detecting network breaches and increasing security where ML takes place locally on local distributed clients rather than a centralized server. The contributions of our work can be found below:  
* We have proposed an FL approach for IIoT intrusion detection in order to train the models at the local device end and accumulate their learning to ensure enhanced security against unwanted attacks. * We have deployed two deep learning classifiers, namely, convolutional neural network (CNN) and recurrent neural network (RNN) for both centralized ML and federated learning (FL). * We have utilized the Edge-IIoTset dataset [7] to demonstrate a thorough evaluation that contains real-world data about different types of attacks. * We compared our proposed method to other works to show that it is superior. The remainder of the article is organized as follows. Section 2 includes a review of the literature on the most current FL advancements related to IoT and intrusion detection. Section 3 discusses the proposed method in detail. Section 4 presents and thoroughly discusses the experimental data and evaluation. Finally, Section 5 summarizes our findings and suggests many potential future research topics.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 1 Introduction
[23], building upon NestFuse, designed a fusion network called the residual fusion network (RFN) to replace manually designed fusion strategies. By employing a two-stage training method, the RFN retains detailed information and salient features in the fusion features, significantly enhancing the fusion performance of the network. However, the complexity of the network structure leads to a large number of model parameters. To address the issues present in the aforementioned image fusion networks, we propose a lightweight multi-scale infrared and visible image fusion method based on nested connections and Res2 Net. This innovative combination not only enhances the feature extraction process, thereby substantially enhancing the quality of the fused images, but also ensures low computational complexity. Compared to existing techniques, the nested connection structure that we introduce can integrate multi-scale information more deeply, a facet often overlooked in traditional image fusion methods. Furthermore, by designing the multi-scale residual fusion network R2 FN to replace traditional manually designed fusion strategies, our method can effectively highlight key information in the images, thereby enhancing their expressiveness while preserving image details. The introduction of depthwise separable convolution significantly reduces computational complexity and memory requirements, making the algorithm suitable for resource-constrained mobile devices. The main contributions of our algorithm are summarized as follows:  
1. We employ the frequency-domain decomposition technique to split the source image into detail and base layers, allowing the network to operate on the image with greater precision. 2. We incorporate depthwise separable convolution into the infrared and visible-light image fusion network. In comparison to existing classical fusion methods, our network achieves the lowest number of parameters without compromising performance. 3. We propose a multi-scale residual fusion module (R2 FN) to replace manually designed fusion strategies, enabling the effective fusion of features across multiple scales. 4. We design a new loss function that preserves detail information while enhancing salient target features. 5. We conduct experiments on the TNO dataset to test the proposed fusion method. Comparative analysis with existing classical image fusion algorithms demonstrates that our method achieves optimal performance in these fusion tasks.
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 3 Research Method  
### Overview  
As mentioned above, we aim to improve the accuracy of iBeacon positioning method in smartphone, and fuse it with PDR in a designed Kalman filter (KF) for real-time applications. A novel aspect of the proposed BLE-based method is that we introduce a specific fingerprinting method to fuse with trilateration. This fusion can resist the ranging error caused by RSS fluctuations of iBeacon. This BLE-based method lays a solid foundation for the later fusion with PDR result. Similar to the method in [15], we adopt the single point positioning algorithm in GNSS for trilateration, and we set up a weight matrix to differentiate the contribution of beacons in various distances. For the fingerprinting method, we adopt _cosine similarity_ to reveal the consistent trend between two RSSI vectors. This trend can help us to exclude outliers and deal with the RSS differences between different smartphones. The proposed iBeacon-based approach achieves an accuracy close to that of WiFi-based approaches. The easy implementation of this approach in smartphone is also a valuable improvement of iBeacon-based positioning.  
In addition, we develop a new implementation of PDR to resist heading errors via trajectory segmentation. We detect turns first with gyroscope data and subdivide user trajectory into segments according to these turns. We design a KF with heading measurement and the current angular rate, apply it to each segment, and update the filter for the next segment. This implementation can alleviate the error of headings in smartphone. Finally, we design a fusion method based on KF to combine the BLE-based method and the PDR implementation. It ensures both the accuracy and continuity of real-time trajectories.  
The whole fusion workflow for indoor positioning is summarized in Figure 1. The related smartphone sensors include bluetooth module, gyroscope, accelerometer, and magnetic sensor. After merging the two RSS-based positioning methods (trilateration and fingerprinting), we can add the absolute coordinates into PDR computation. From the initial location, we can confirm each step with its length and check the altitude (including heading orientation). Meanwhile, we provide orientation filtering to correct the altitude measurement. The orientation filter is updated at each turn. After the whole PDR procedure, the location estimate and the current heading orientation are ready. BLE-derived location and PDR location are input to the designed KF (see Section 3.4), when the PDR location is out of the current room or the filtering interval has reached (e.g., 10 s). Finally, the fused location estimate can effectively resist drifting and jumping in a trajectory. The following subsections explain the details.
# Patient Mobility Support for Indoor Non-Directed Optical Body Area Networks
## 5 Experiments and Results
### Results and Discussion
Table 4 compares the proposed mobility model with the conventional mobility system [11] and also shows the characteristics. It is clear that the proposed OBAN-based mobility model has advantages of high spectrum bandwidth, less complex transceiver, error-free transmission for a relatively longer distance, and reliable mobility support to the patient over conventional approaches.
# Performance Assessment of Predictive Control--A Survey
## 4 MPC Performance Assessment
### Industrial Implementations  
MPC, as an important component of advanced process control, has been used in practice for more than 40 years. Any report informing about successful application of the Model Predictive Control uses some performance measures. Actually, mean square error of controlled variables is mostly used. These applications address mostly chemical and power generation industry, such as fluid catalytic cracking unit, distillation column, polyvinyl chloride plant, feed batch bioreactor or power generation efficiency, NOx emission control, and many others [26; 27; 64]. The specific aim of the MPC-CPA can be found, among others, in:* industrial validation of the multivariate MPC performance assessment at para-xylene production and poly-propylene splitter column processes in [163];
* kerosene and naphtha hydrotreating units in [164]; and
* model assessment performed on an industrial predictive controller applied to a propylene/propane separation system [165; 166], using the methodology proposed by [PERSON] et al. [145; 146].  
Generally, MPC performance assessment is used in industry during two phases of the controller life-cycle. First, it is required during the process of decision making, whether the application of MPC is technologically and financially feasible. The need to verify the results, or to confirm whether the initially obtained profits are sustained, is the second phase. Such activities are often called the control feasibility or performance study. On-line perpetual validation of the results may be very useful. There are several commercial CPA software packages available on the market supporting on-line assessment [9; 65; 113].  
A summary of the implementation experience collected during industrial APC and MPC applications may be found in [63; 65; 67]. It is interesting to notice that industrial reality of Model Predictive Control applications is not so clear. There are several issues that limit its applicability. Sustainability of the obtained benefits and the lack of experienced personnel constitute the two main limiting factors in further industrial MPC dissemination.