esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Works  
### Conclusions  
A D2D aided CRS exploiting SM (D2D-CRS-SM) considering two different scenarios for a downlink transmission was proposed and investigated, where each scenario consists of a BS, a NU, a FU, and a D2D transmitter (D\({}_{1}\))-receiver (D\({}_{2}\)) pair. In the proposed D2D-CRS-SM, D\({}_{1}\) cooperatively relays the traffic of a FU with no direct link with the BS. At the same time, D\({}_{1}\) gets the opportunity to communicate with D\({}_{2}\) without causing any mutual interference due to the exploitation of SM in D\({}_{1}\). Moreover, no leasing of time slots or power allocation are required between the cellular and D2D user. The performance of D2D-CRS-SM is studied, in terms of BER, SE and sum SE, considering \(M\)-PSK and \(M\)-QAM. Moreover, a comparative study with VBLAST-ZF MIMO is also provided. The outcome of this study suggests that the proposed model can be a promising paradigm for the next generation wireless communications, where it is expected that cellular communication and D2D communication will coexist. It is worth mentioning that this is the basic model for a D2D aided CRS using SM.
## 3 System Architecture and Channel Models
### Bayesian Learning-Based Channel Estimation Architecture  
A typical UWA-OFDM communication scenario was shown at the top of Figure 2. The receiver can obtain the transmitted OFDM signal through dynamic multipaths, and the received signal may be affected by random colored noise. At the bottom of Figure 2, the system architecture of Bayesian learning-based channel estimation is given. To characterize the delay power spectrum and temporal correlation of each cluster in the UWA channel, the clustered-sparse channel distribution model is constructed and the model hyperparameters can be optimized by the iterative partition-based cluster evolution. Demodulated signal and pilot symbols were utilized to measure the instantaneous channel response, and the iterative noise measurement and data detection can improve the anti-noise performance of the channel measurement model. Based on the channel distribution model and the channel measurement model, the Bayesian channel estimation can be obtained.  
Important notations used in following sections are given by:  
* Uppercase and lowercase bold symbols are reserved for matrices and vectors, respectively. Particularly, \(\mathbf{I_{M}}\) denotes the identity matrix with size \(M\times M\). When the dimension is evident from the context, for simplicity, we only used \(\mathbf{I}\);
* \(\mathbf{A}^{T}\) and \(\mathbf{A}^{H}\) represent the transpose and conjugate transpose of \(\mathbf{A}\), respectively;
* \(\mathbf{A}\otimes\mathbf{B}\) denotes the Kronecker product of the two matrices \(\mathbf{A}\) and \(\mathbf{B}\);
* \(vec(\mathbf{A})\) denotes the vectorization of \(\mathbf{A}\) formed by stacking its columns into a single column vector;
* \(Tr(\mathbf{A})\) denotes the trace of \(\mathbf{A}\);
* \(Toeplitz(a_{1},a_{2},\ldots,a_{N})\) denotes a Toeplitz matrix taking \(a_{1},\ldots,a_{N}\) as first row;
* \(diag(\mathbf{a})\) or \(diag(a_{1},a_{2},\ldots,a_{N})\) denotes a diagonal matrix with principal diagonal elements being \(a_{1},a_{2},\ldots,a_{N}\) in turn; \(diag(\mathbf{A}_{1},\mathbf{A}_{2},\ldots,\mathbf{A}
# Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition
## 2 Literature Review
Deep learning methods show good results in keyframe selection in video recognition systems such as HAR, but require a complex and time-consuming training step. At the same time, they need a huge number of labeled training samples, which are usually not available in HAR applications. The last group is hybrid methods, which use a combination of features and machine learning schemes for keyframe and ROI selection. [PERSON] et al. [21] proposed an inertial measurement unit (IMU) to make a video depth map. The authors used keyframe selection to pick frames in an input video that involves minimal human movement. Usually, a smartphone needs 30 frames per second to create a depth map of the environment, but by selecting keyframes, a depth map can be made using just five frames. [PERSON] [22] proposed a robotic imaging system for selecting the best shot among several portraits. [PERSON] et al. [23] selected the minimum number of frames, i.e., keyframes, required in a high resolution (HR) imaging task. [PERSON] and [PERSON] [24] extracted the video background using a cut set by selecting keyframes from a sequence of frames. They found patches with a similarity between successive forms in a video, removed frames with less information and preserved keyframes. To select the best frames in a HAR video, [PERSON] et al. [25] counted the moving parts of the human body that form action in each frame. The frames where the number of moving parts or amount of movement in them are low were removed from the training process. [PERSON] et al. [26] presented a video object segmentation scheme using deep learning that can be used for human detection in HAR systems. The suggested system showed good results in different applications, but its applicability in complex scenes with several humans simultaneously is still unclear. [PERSON] et al. [27] proposed two adaptive activation functions to accelerate deep learning method convergence. [PERSON] et al. [28, 29] showed the applicability and flexibility of the adaptive activation functions in various applications, such as video processing. The adaptive activation functions can be combined by keyframe selection in order to enhance the efficiency of deep-learning-based HAR systems. The hybrid methods are usually limited in terms of applications, and specifically in terms of the types of actions involved in HAR systems. According to the literature review, previous methods to be used as a pre-processing step in HAR systems need to be redesigned or, at least, demand new examples for retraining. The proposed method selects a predefined number of frames, which should include a relatively complete expression of the human action, regardless of the type of action. After that, the proposed method crops the frames considering the input size of the HAR system and the maximum action information. The main advantage of the proposed method is the improvement of the learning process of any HAR system by pre-processing the input videos without the need for training.
## 1 Introduction
This paper proposes an FE model updating method founded on parameter optimization for the 25 m aperture radio telescope antenna shown in Figure 1. We use the RBF surrogate model technology, combined with the antenna primary reflector surface deformation data obtained by photogrammetry technology, optimize the elastic modulus parameters of the different components of the antenna FE model BUS, update the original FE model, and reduce the impact of the BUS's deformation on the antenna primary reflector surface deformation error. In the model updating, the RBF surrogate model is used instead of the FE model to perform parameter sensitivity analysis and optimization. Finally, the updated FE model's analysis results are closer to the antenna primary reflector surface deformation results than the original FE model. Figure 1: A 25 m aperture radio telescope antenna: (**a**) actual antenna structure; (**b**) schematic sketch of the structure.
# Adaptable and Automated Small UAV Deployments via Virtualization
## 1 Introduction
Nonetheless, we want to highlight that the full realization of this vision is challenging due, among other reasons, to the limited capacity of the hardware and software platforms that can typically be on-boarded on SUAVs; the need to automatically manage the resources provided by that platforms and deploy virtual functions on top of them, despite being transported by flying vehicles; the requirement to specify appropriate placement policies for virtual functions (e.g., to indicate which virtual functions should be executed over the same SUAV unit); and the energy consumption of the battery-powered SUAVs, which is a limiting factor of the operation time. Figure 1: Deployment of SUAVs offering diverse network functionalities (NFs). This article continues a research line initiated in [10], where we presented the preliminary design of an NFV system capable of deploying network services over SUAV platforms, and we demonstrated the practical feasibility of this design. The work presented in this article extends our previous research covering the following aspects:  
* It extends the original design of the system, incorporating specific mechanisms to support wireless control communications between the GCS and the NFV infrastructure provided by the SUAVs. * It provides a new design for the SUAV device, which does not require specific routing functions at the SUAV to handle the traffic exchanged by the virtual functions hosted by the device. * It provides a new mechanism to support data-plane communications. This mechanism is based on virtual networks that are established over the aerial network infrastructure provided by the SUAVs, which now run a flying ad-hoc network routing protocol such as Ad hoc On-Demand Distance Vector (AODV) [11] or Optimized Link State Routing (OLSR) [12]. This simplifies the definition of the services that are to be virtualized and eases the reconfiguration of the aerial network formed by the SUAVs. * It presents a functional prototype of the platform with the defined extensions, as well as a validation of the solution with the deployment of a realistic network service (an IP telephony service). * Finally, it evaluates the performance that may be offered by different flying ad-hoc network routing protocols to support control and data communications through the network formed by the SUAV devices. The rest of this paper is organized as follows. After reviewing the existing literature on the utilization of SUAVs to support network functionalities and softwarization in Section 2, the main challenges related with the utilization of NFV technologies in SUAV environments and the design of our proposed NFV system are detailed in Section 3. Section 4 presents our prototype implementation and the tests that have been done to validate our system design. Finally, Section 5 concludes the paper and presents the short-term directions of our work.
## 4 Experimental Results and Analysis
### Qualitative Analyses  
Figure 6 shows the qualitative analysis results of crude-oil futures prices by multiple models, including BiLSTMModel, CNNModel, CNNLSTMModel, LSTMModel, TransformerModel, and MDFFEModel. Each subgraph clearly presents two curves of'real' (real value) and 'pred' (predicted value), and intuitively compares the accuracy and stability of each model in predicting crude oil futures prices. Through observation, it is found that although the predicted value of BiLSTMModel has a certain follow-up with the actual value, there are obvious deviations in some areas, indicating that it lacks in capturing details. Compared with the BiLSTMModel, the fit of the CNNModel is improved, but there are still deviations. The fluctuation frequency of CNNLSTMModel is high, showing a certain follow-up, but the deviation phenomenon is still obvious. The predicted value of the LSTMModel deviates significantly from the actual value, and the fluctuation range and frequency are large, indicating that it is difficult to effectively capture the complex fluctuations of futures prices. The fit of TransformerModel is better than that of LSTMModel, but there are still fluctuations in capturing long-term dependence and complex nonlinear relationships. The most noteworthy is that the MDFFE model has the highest degree of fit,  
Figure 5: The ablation experiment point line diagram (shows the performance changes of different model configurations on key evaluation indicators and verifies the effect of feature engineering and model fusion).  
its prediction curve almost coincides with the actual value, and the fluctuation amplitude and frequency are the smallest, which intuitively proves that the MDFFE model reached the best level in the accuracy and stability of crude oil futures price prediction.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 2 Materials and Methods  
As the potential of LEO navigation has begun to be explored, the concept of LEO-PNT has emerged. There are three methods for utilizing LEO constellations for positioning: leveraging LEO signals of opportunity, hosting payloads on satellites designed for PNT applications, and utilizing fused payloads embedded within telecommunication payloads [4, 5]. This paper is focused on the hosted payload approach.
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
## 4 Results
### Simulation Results
#### 4.3.2 Intra-Arrival  
Figure 8 displays the sample mean and confidence interval (CI) of intra-arrival count. This metric is a distance between two packet fragments from the same flow, so it is an indicator of traffic locality. For instance, a CDF value 0.6 for distance 8 means that in 60% of cases, the next packet from the same flow was at most eight packets apart. This chart demonstrates the effect of increasing the throughput of the packet traces used for testing. For example, for a distance of 1, which means that the next packet is from the same fragment flow, the possible range of values is between 0.35 for \(R=0.9\) and 0.6 for \(R=0.1\).  
The main conclusion from Figure 8 is that traffic locality is strong for all tests. Even accounting for confidence sets, there is at least a 50% chance that one of the next five packets is from the same flow, which can be derived from the lower confidence interval for series with \(R=0.9\).  
The high traffic locality is the key to efficient flow processing and supports the results from Figure 7. The data should be interpreted as follows: fragmented IP packets are sent as a consecutive series of network frames and retain that property even in the aggregated traffic. This allows for processing them with relatively low memory consumption.  
The notable trend that can be observed in Figure 8 and subsequent charts is that increasing the mix ratio \(R\) decreases the overall size of the confidence sets. This can be explained by the fact that the original diversity contained in the packet traces (smaller than synthetic) is recovered when the sampling ratio approaches 100%.
## 2 Study Area and Method
### Model Parameters and Input Data
#### 2.3.5 Leaf Area Index (LAI) Data  
The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data of Zhejiang province was derived from the US National Aeronautics and Space Administration (NASA) website. Due to the noise and other defects of LAI\({}_{\text{MODIS}}\)[46; 47], the LAI\({}_{\text{MODIS}}\) data was smoothed using the locally adjusted cubic-spline capping algorithm [48], then the integrated ensemble Kalman filter algorithm was used to assimilate the LAI after smoothing to obtain high-precision Zhejiang Province LAI data [49; 50].
# Framework for BIM-Based Repair History Management for Architectural Heritage
## 5 Development of a BIM-Based Repair History Management Framework
### Data Validation and Repair History Database Update  
After automatically creating object classification codes and a shared parameter-based data container, the data between the repair history DB and BIM are synchronized. The integer- and string-based repair history records are input to each part of BIM. In this study, for BIM of the Great Hero Hall of Sudeoksa Temple, the developed data synchronization system for repair history records allowed for the entry of details on 1592 parts, covering 18 repair and maintenance projects since its establishment in 1308; however, some parts have no repair history and some details, such as the exact cause or type of repair, are omitted for partial repair and maintenance work. Hence, discovering relevant data in the future will require adding missing information or correcting incorrect information by updating both the repair history DB and BIM.  
Wooden buildings have a high number of part types and volumes of data, making real-time interworking with the repair history database potentially hinder system performance. Therefore, in the proposed data synchronization system, the BIM data are updated based on the database only when the execute button is pressed. Conversely, the data updated in the BIM model can be exported to an Excel file through the export function to ensure data consistency and reliability of integrated HBIM.  
When new maintenance and repair work is performed on architectural heritage, the first task is to verify whether new parameters are required to incorporate the new repair data into the family element of the existing integrated HBIM. If new parameters are needed, they should be appended to the pre-existing shared parameter file, followed by the operation of the developed API to update the data containers of HBIM. The parameter's unique identifier (Global Unique Identifier, GUID: A unique identifier with a series of hexadecimal numbers assigned to every object in BIM. It serves as a unique reference to the object, which helps in the identification, tracking, and management of objects throughout the project lifecycle.) must remain unchanged by using the existing shared parameter file to  
Figure 13: The outcome of the repair history record synchronization routine using Revit Dynamo.  
avoid the risk of deletion due to overwriting the current parameter. Finally, new columns with the same parameter names should be added to the Excel spreadsheet to document the repair work's contents. The proposed Dynamo should then be used to conduct data synchronization and update the integrated HBIM with the new repair history database (Figure 14).
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 5 Simulation Results
### Performance in Sparse Scenario
#### 5.1.1 Effects of Varying the Number of Subchannels  
First of all, the effect of varying the number of subchannels in MCBR (Neighborhood Information) is demonstrated for \(K=\{6,9,12,15,18\}\). In Figure 2, it is noted that as the number of subchannel increases, the subchannel allocation collision decreases; hence the convergence speed to a steady state increases. However, since the total bandwidth is divided with the number of subchannels in Equation (2), the achieved total capacity decreases with an increase in subchannels.  
For simplicity, in the rest of simulations, we fix the number of subchannels to six.
## 2 Materials and Methods
### Experimental Measurements and Battery Simulation
#### 2.3.2 Implementation of the MATLAB/Simulink Simulations
This temperature range is compatible with the specifics of the batteries considered in this study. However, to avoid the occurrence of temperature conditions outside the allowed limits, the battery pack was equipped with cooling fans that are activated when the outer casing temperature of the single cell exceeds \(40\)\({}^{\circ}\)C.
# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
## 2 Methods
### Vibration Experiments  
Figure 1 shows the steel frame used in the experiments. The damage was introduced in any one of the 9 rods by cutting off 50% of its cross-sectional area (Figure 3a).  
Figure 1: Steel frame with 355 rods. (**a**) Laboratory steel frame, (**b**) Damage parts.  
Figure 2: Finite element model with 355 rods.  
Experimental equipment (Figure 3) included a dynamic data acquisition instrument (JM3840, Jing-Ming Technology Inc., Yangzhou, China), 13 accelerometers, a hammer, and a laptop. As the frame weighs 135 kg, while the accelerometers weigh 0.52 kg and the cable 0.84 kg, therefore, the effect of the added mass (of the accelerometers and cable) on the measurement results can be ignored. The steel frame was excited by the hammer and the accelerometers were used to obtain the vibration signals of the 13 measurement points.
## 3 Simulation Methodology  
### Simulation Model and Setting Variables  
In this study, simulations using EnergyPlus were performed to analyze each technology contribution to the energy saving rates in different climates. Figure 3 shows the simulation base model. The U.S. Department of Energy (DOE) developed ASHRAE 90.1 prototype building models. These models can serve as references for approximately 70% of U.S. office buildings. According to the DOE's national renewable energy laboratory report [33], the models serve as a baseline for comparison, and improve the accuracy of energy simulation software. Table 1 shows the base model envelope conditions, air conditioning and plant system outline, and detailed energy performance input conditions for the EnergyPlus simulation. Figure 3: Simulation base model. For the base model envelope conditions, such as walls and glazing, the Korean energy saving design standard (Korea Ministry of Land, Infrastructure and Transport and Korea Energy Agency 2018) [24] was used to compare relative energy saving rates in each climate region. U-values of 0.26 and 1.5 W/m\({}^{2}\)\(\cdot\)K for the wall and the glazing system, respectively, were used, which have been used as minimum standards until recently in Korea. The roof condition was treated as adiabatic, to assume that there is no heat exchange between different floors. For equipment, lighting, and people load, the Korean energy saving design standards, the Ministry of Land, Infrastructure and Transport (MOLIT), and the Korea Institute of Civil Engineering and Building Technology (KICT) report (2017) [34] was used.
# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 5 Results and Discussion
### Image-Level Detection Performance
#### 5.4.2 Balance of Detection Rate and False-Alarm Rate
In the case of a high IoU threshold, the false-alarm rate is high and almost constant with an increasing confidence score. As the IoU threshold decreases, the false-alarm rate decreases as the confidence level increases. When the IoU threshold is very low, the false-alarm rate first has a significant decrease with the confidence level, which is the process of reducing error detections. The curves of single-image detection rate and false-alarm rate with IoU are shown in Figure 9b. The detection rate decreases with an increasing IoU threshold, and the false-alarm rate increases with increasing IoU threshold. It is interesting to note that the trend of the effect of the IoU threshold on the detection rate for different confidence scores is very similar, while it is different for the false-alarm rate. The change in the IoU threshold at a detection box confidence threshold of 10% has little effect on the false-alarm rate, while at a detection box confidence threshold greater than 20%, the change in the IoU threshold has a greater effect on the false-alarm rate. An enlarged view of the curves of the single-image detection rate and false-alarm rate is shown in Figure 9c. In practice, we need to select the appropriate confidence threshold and IoU threshold according to the task requirements. For example, the false-alarm rate of detection in a security check is strictly required to be less than 15%. According to Figure 9c, there are only the bottom two IoU threshold curves to choose from. Among the candidate IoUs, the one with the highest IoU is generally chosen to ensure the accuracy of detection. The figure shows that the curve of IoU 0.2 is the best. After selecting the IoU threshold, it is necessary to select the confidence threshold on that threshold curve. The confidence threshold on the current curve that matches the highest detection rate and lowest false-alarm rate is SCO 0.5. Figure 8: The curve of detection rate and false alarm rate with NMS, IoU, and confidence score. The last subplot represents the curve of the lowest false-alarm rate and its corresponding detection rate variation with the NMS threshold for the previous subplots. Therefore, in this terahertz dataset, the best thresholds are NMS 0.1, SCO 0.5, and IoU 0.2.