esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### System Evaluation
#### 4.7.3 Comparison of Random Vibration Tests  
After the mirror component has been subjected to random vibration tests in the X, Y, and Z directions, Table 10 shows the comparison of the data of the mirror component in random vibration, through which it can be seen that in the random vibration of the mirror component in the X, Y, and Z directions, the position of the largest response amplification is located in the center of the base, and the magnification of the response is controlled to be less than five times. It shows that the mirror component can withstand sinusoidal vibration of this magnitude; at the same time, it proves that the stiffness of the component can overcome the influence brought by the mechanics of the launching stage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Direction of** \\ **Vibration** \\ \end{tabular} & \begin{tabular}{c} **POV** \\ \end{tabular} & \
\begin{tabular}{c} **RMS of Total Response** \\ **(grms)** \\ \end{tabular} &
\begin{tabular}{c} **Response Amplification** \\ \end{tabular} \\ \hline \multirow{3}{*}{X} & Input & 5.677 & \\ \cline{2-4}  & Mirror center & 16.91 & 2.97 \\ \cline{2-4}  & Base center & 20.16 & 3.55 \\ \hline \multirow{3}{*}{Y} & Input & 5.677 & \\ \cline{2-4}  & Mirror center & 19.86 & 3.49 \\ \cline{2-4}  & Base center & 22.4 & 3.94 \\ \hline \multirow{3}{*}{Z} & Input & 5.677 & \\ \cline{2-4}  & Mirror center & 18.68 & 3.29 \\ \cline{1-1} \cline{2-4}  & Base center & 21.15 & 3.72 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of random vibration data for mirror component.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Direction** \\ \end{tabular} & \begin{tabular}{c} **First Time** \\ \end{tabular} & \begin{tabular}{c} **Second Time** \\ \end{tabular} & \
\begin{tabular}{c} **Third Time** \\ \end{tabular} &
\begin{tabular}{c} **Magnitude of Change** \\ \end{tabular} \\ \hline X & 178.6 Hz & 176.4 Hz & 173.8 Hz & 4.8 Hz \\ \hline Y & 177.9 Hz & 177.3 Hz & 176.4 Hz & 1.5 Hz \\ \hline Z & 273.1 Hz & 272.7 Hz & 271.5 Hz & 1.6 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of mirror component intrinsic frequencies.
# Compressive Sensing-Based Coding Iterative Channel Estimation Method for TDS-OFDM System
## 3 Cyclic Compressive Sensing Channel Estimation Algorithm Based on Channel Coding
### BER Performance Simulation
When the SNR is 15 dB, BERs of the proposed method one iteration, two iterations and the LCS method are 0.111, 0.058 and 0.177 in the
+++  
channel, respectively. When the SNR is 15 dB, BERs of the proposed method after iteration one iteration, two iterations and the LCS method are 0.065, 0.051 and 0.108 in the eight-path channel, respectively. According to the experimental data, compared with the system without coding cyclic compressive sensing, the BER performance of the system with coding cyclic compressive sensing is enhanced obviously, and the BER after two iterations is lower than that after one iteration. With the increase in multiple paths, the effect of the BER is enhanced. Figure 14: Comparison of system communication performance in a 8-path channel. Figure 12: Comparison of system communication performance in a 4-path channel. Figure 13: Comparison of system communication performance in a 6-path channel.
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 3 Existing Equations Used for Two-Way Flat Slabs
### Euro-Code 2 (EC2)  
The Euro-Code 2 (EC2) recommends that the punching shear resistance be expressed as proportional to \((f_{ck})^{\frac{3}{3}}\), where \(f_{ck}\) is the compressive strength of concrete. In EC2, the influences of slab depth and steel reinforcement are also considered. The punching shear resistance according to EC2 may be calculated as  
\[V_{n}=\frac{0.18}{\gamma_{c}}Kb_{o}d(1000\times\rho\times f_{ck})^{\frac{1}{ 3}}\frac{2d}{a_{cnt}}\geq 0.035\ k^{\frac{3}{2}}f_{ck}^{\frac{1}{2}}\frac{2d}{a_{cnt }}\ b_{o}d, \tag{6}\]  
where \(\gamma_{c}\) is the material resistance factor for concrete = 1.5, \(d\) is the effective depth, \(K=1+\sqrt{200/d}\ \leq 2\) is the size factor of the effective depth, \(\rho\) is the flexural reinforcement ratio \(\leq 2\%\), \(f_{ck}\) is the cylinder compressive strength of concrete, and \(a_{cnt}\) is the distance from column face to the control perimeter.  
It should be noted that some codes do not consider the size effect in estimates of the punching shear strength of slabs, such as ACI 318-14, while some common codes, such as Model-Code-2010 and Euro-Code 2, consider the size effect in the design of slabs for punching shear in the same form as presented in Equations (4) and (6). Deferent forms of the size effect have been presented by many researchers to consider the effect of this factor on punching shear strength: More details about the size effect can be found in References [26; 27; 28; 29; 30].
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 2 Preliminary  
### Convolutional Neural Network  
The structure of a typical CNN model mainly includes a convolutional layer, a pooling layer, and a fully connected layer. The convolutional layer is the core of the CNN network, and its main function is to perform a series of convolution operations on the input data through the convolution kernel to obtain feature maps. The convolutional layer is usually connected with the pooling layer, whose main function is to reduce the dimension of the output feature graph of the convolutional layer. After feature extraction is performed on the input data through convolution operation, pooling calculation is used to further select effective features, remove redundant information, simplify the complexity of the model, and reduce the computing resources and time required by CNN. The last part of the CNN network is the fully connected layer. The main function of the fully connected layer is to further extract features and output the fault diagnosis results. In the fully connected layer, the features extracted by the convolutional layer and the pooling layer are expanded into a one-dimensional vector and are compared with the weight coefficients of the fully connected layer. The calculated result is multiplied then adjusted through the nonlinear activation function.
## 2 Background
### Internet of Things  
The IoT concept can be explained as a system of interrelated computing devices that have the ability to transmit data through a network without requiring human actions [26]. The IoT systems are made up by sensors and actuators to share information, perform tasks together, and to coordinate decisions. Frequently, in an IoT system, three layers are included:  
1. _Perception Layer_: Integrates physical devices, such as sensors and actuators. They measure and obtain data and process the information associated with the state of cited devices. In addition, this layer transmits the information of the devices to the upper layers.
2. _Network Layer_: Current layer receives the data from the perception layer and transmits them to the physical devices and applications.
3. _Application Layer_: The information provided by the network layer is received by the application layer, and uses this information in the services and applications developed to work with such data.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technology** & **Authors** & **Proposal** & **Signal** \\ \hline Depth Image & [45] & Visualization of human joint analysis for falls detection & Image \\ \hline Near Field Imaging sensor & [46] & The positioning accuracy is measured using raw observations & RFID \\ \hline Radar sensor & [47] & Doppler radar-based fall detection system & Eco \\ \hline Ultrasonic & [48] & Automated system for monitoring human activity using array of heterogeneous ultrasonic sensors & Ultrasounds \\ \hline Hybrid & [49] & Fusing camera and accelerometer data & Images and acceleration \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._The protocol used between the three layers was the Message Queue Telemetry Transport (MQTT) [55]. The MQTT protocol creates connections based on TCP/IP, and they remain open to be reused. Cited protocol is based on a publisher/subscriber architecture.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 4 Evaluation
### Analysis Based on the Number of Devices
#### 4.5.1 Energy Consumption  
As Figure 5 shows, HHS after GA has the least average energy consumption; also, the minimum energy consumption of GA and HHS is \(1.36*10^{7}\). The highest energy consumption is \(1.54*10^{7}\) megajoules, which is related to the ACO algorithm with 4 areas and 4 cameras with 22 FDs.  
The lowest energy consumption (megajoules) is \(1.36*10^{7}\) for the GA and HHS algorithms with one area, one camera, and four FDs. The average energy consumption by various algorithms is as follows: the GA algorithm, \(1.38*10^{7}\); the PSO, \(1.51*10^{7}\); ACO, \(1.52*10^{7}\); SA, \(1.51*10^{7}\); and HHS, \(1.40*10^{7}\).  
The proposed algorithm decreases the average energy consumption of PSO by 7.17%, ACO by 7.42%, and SA by 6.91%. Hence, the energy consumption of the proposed algorithm in different modes is less than that of SA, ACO, and PSO; thus, HHS has better performance  
Figure 4: Comparison of scheduling algorithms based on number of users: (a) Energy consumption, (b) Total execution cost, and (c) Delay of application loop.  
than the three mentioned algorithms. The energy consumption by GA and HHS are almost the same, but GA has a slightly smaller value.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 3 Results
### Comparison of Experimental Results for Different Defect Detection Methods
In the fourth column, it is evident that the method recommended in this paper can detect missing warp defects almost entirely, although discontinuous results are observed for sample 3 and sample 4. Figure 11: Comparison of experimental results of foreign matter defects by each method. (**a**) Defect sample; (**b**) detection result of ReNet-D; (**c**) detection result of SDDM-PS; (**d**) detection result of SA-Pix2 pix. In summary, the ReNet-D methodologies exhibit poor accuracy in blemish detection, occasionally even incorporating the original image background. This may be attributed to the self-encoder's loss of critical image information during training, hindering the ReNet-D method's ability to effectively reconstruct images and subsequently perform precise blemish image segmentation. While the SDDM-PS method demonstrates a degree of proficiency in detecting oil stains, foreign objects, and broken holes, limitations persist. This may be attributed to its reliance solely on a GAN network for sample training, as GANs exhibit limited capacity in processing image details. Their focus tends to be confined to local areas within images, lacking the capability to detect defects closely resembling the original fabric texture background. In contrast, the method advocated in this paper showcases notable completeness and accuracy in detecting various defect types compared to the aforementioned methodologies. Notably, it achieves high accuracy in detecting oil stains, foreign objects, and holes. However, it does encounter errors when identifying scuffs and warp defects. This is primarily due to the minor damage degree within the edge regions of scuff defects, which bears a slight difference from the fabric background. Consequently, the subtle dissimilarity between the reconstructed and original images in these areas impedes effective segmentation,  
Figure 12: Comparison of experimental results of the hole defect by each method. (**a**) Defect sample; (**b**) detection result of ReNet-D; (**c**) detection result of SDDM-PS; (**d**) detection result of SA-Pix2 pix. leading to smaller detected regions. Similarly, warp defects present challenges akin to scuffing defects. The low contrast between defective areas and the fabric background further diminishes differences between the reconstructed and original images. Consequently, this results in intermittent segmentation outcomes. In addition, we conducted an efficiency comparison of the algorithms using an in-house dataset. Table 2 illustrates the processing elapsed time of the three methods under uniform computational performance. The method advocated in this paper demonstrates an average detection time of 46.15 ms, slightly exceeding the processing times of the other two methods. However, it leads to higher detection accuracy.
## 2 Related Work and Motivation
In UWSNs, there is a major problem of high transmission collisions, which can not be handled by routing protocols. In [21], authors decrease the transmission collisions by suggesting the multichannel Medium Access Control (MAC) protocol. Depth-Based routing in underwater sensor networks (DBR) [22] is a trivial routing scheme in which sensor nodes use their depth information to transfer data towards the on-surface station. Nodes do not require information of their location, however, they identify their depth by using depth sensor. In acoustic communication, signal quality is largely affected by lower bandwidth efficiency and frequency. Therefore, in order to achieve increased throughput, frequency scaling is used. In [23], authors analyze the effects of frequency scaling on channel capacity. They also utilize multi-hop communication in dense UASNs to achieve high quality signal. Another way to increase the network lifetime of UASNs is by employing Remotely Powered UASN (RPUASN). In [24], authors suggest paradigm of RPUASN in which sensor nodes harvest along with storing the power supplied by an external acoustic source. In [25], authors employ both the ant colony optimization algorithm and artificial fish swarm algorithm to achieve a global solution for efficient data gathering. It also decreases delay and energy consumption of nodes in UWSNs. There is large number of routing protocols designed for application-oriented UWSNs. Applications such as oil spillage monitoring and seismic monitoring require cylindrical deployment of sensor nodes. However, there is a lack of energy efficient routing schemes for the cylindrical networks in UWSNs. In terrestrial WSNs, single-chain based routing schemes such as PEGASIS does not perform well in the underwater environment. Challenges such as increased energy consumption and unbalanced load distribution may only be tackled by improving chain-based routing schemes. There is also a problem of long link in the existing chain-based schemes such as PEGASIS.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
###### Abstract  
This paper proposes a human-centered risk assessment algorithm designed to find the intervention moment of drive mode and active safety mode while monitoring threat vehicles ahead to overcome effects of vehicular communication on risk assessment in automated driving vehicle. Although a conventional radar system is known to be best fitted on-board ranging sensor in terms of longitudinal safety, it is generally not enough for a reliable automated driving because of sensing uncertainty of the traffic environments and incomplete perception results due to sensor limitations. This can be overcome by implementing vehicle-to-vehicle (V2V) communication which provides complementary source of target vehicle's dynamic behavior. Using V2V communication with vehicle internal and surround information obtained from the on-board sensor system, future vehicle motion has been predicted. With accurately predicted motion of a remote vehicle, a collision risk and the automated drive mode are determined by incorporating human factor. Effects of the V2V communication on a human-centered risk assessment algorithm have been investigated through a safe triangle analysis. The computer simulation studies have been conducted in order to validate the performance of the proposed algorithm. It has been shown that the V2V communication with the proposed risk assessment algorithm allows a faster drive mode decision and active safety intervention moment.  
Active safety; vehicular communication; automated vehicle; drive mod decision Article
## 7 Results of Simulations
### Parameters of Simulation  
Table 3 shows a summary of the parameters used in the simulations. The four most common types of OFDM transmission modulations were used: 256 QAM, 64 QAM, 16 QAM, and QPSK. As indicated in Table 3, the FFT sizes of OFDM signals equal to 1024, 512, 256 and 128 were selected for analysis. The SNR of the received signals selected for analysis was between \(-25\) dB and 10 dB. Such an SNR range covers the practical environments of many contemporary communication technologies based on OFDM transmission. The signal detection and false alarm probabilities were analysed for the range 0-1. The results were obtained for 10,000 Monte Carlo simulations (Table 3). This number of Monte Carlo simulations was selected for analysis according to the trade-off between simulation duration and simulation accuracy. For the modeling of the different types of received PU signals, the characteristic values of the NU and DT factors were used (Table 3). To eliminate the influence of NU (\(\rho\)) and DT (\(\rho^{\prime}\)) in the modeling, the NU and DT factors were set to \(\rho=\rho^{\prime}=1.00\). To simulate a more realistic PU signal detection, NU and DT factors in the range between 1.01 and 1.05 were exploited for modeling of the different impacts of the NUs and DTs on the ED process (Table 3). The analysis was based on the worst-case detection and false alarm probabilities when the NU and DT are in the ranges specified by the \(\rho\) and \(\rho^{\prime}\) parameters.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 5 Offline Anomaly Clustering  
In this section, we consider the problem of clustering the detected collective anomalies based on the residuals. Clustering collective anomalies of different lengths based on their intensity and structure is a non-trivial task in itself, without an online approach. Therefore, we first normalise the original residual sequences with different distributional summaries (histogram and ECDF). Then, since the clustering usually depends on the distances between the targets, we explore two distance measures (Jensen-Shannon divergence and Euclidean distance), and finally, we compare various partitioning schemes (hierarchical clustering, k-means clustering, fuzzy c-means clustering, EPEmeans and HMCluster).
## 4 Prediction of Building Settlement under Different Pit Construction Conditions
### Comparison of Assessment Indicators under Different Working Conditions  
Using the proposed LSTM-RA-ANN model for the prediction of building settlement, the calculation results of the assessment indices under different working conditions are shown in Figure 9. As can be seen in Figure 9, when the building settlement is predicted using the proposed LSTM-RA-ANN model, the trends of RMSE and MAE are basically synchronised with each other, with the range of change from 0.14 to 2.50; under the three conditions, the RMSE and MAE of Building I are larger because the buildings on both sides of Building I are closely connected, and the settlement prediction value is more affected by the environment.  
Figure 10 also shows that the prediction accuracy is closely related to the construction conditions: the average values of RMSE for enclosure construction, earth excavation construction, and basement support construction are 0.50, 1.60, and 0.51, respectively; the average values of MAE are 0.46, 1.34, and 0.40, respectively; the average values of \(R^{2}\) are 0.89, 0.97, and 0.78, respectively; and the prediction accuracies corresponding to the construction conditions are as follows: basement support > enclosure construction > earth excavation construction; during the basement support construction, the RMSE and MAE fluctuate the least, 0.24-0.62 and 0.18-0.58, respectively, which may be related to the fact that all the concrete support removal adopts the static cutting method; in the enclosure construction, the RMSE and MAE change a little bit more; in the excavation construction, the RMSE and MAE fluctuate more, which may be due to the fact that, during earth excavation, there is a large gravity change in the vicinity of the building, which leads  
Figure 9: Predicted and monitored settlements of buildings under basement support construction based on LSTM-RA-ANN model: (**a**) building F; (**b**) building G; (**c**) building H; (**d**) building I; and (**e**) building J.  
to a large change in the trend of the settlement monitoring values at the monitoring points, which, in turn, may affect the predictive performance of the model.
## 3 Results
### Howland Forest, Maine--Hyperion NDVI Comparison to MODIS NDVI  
Phenological changes in NDVI were compared between EO-1 Hyperion and MODIS surface reflectances at three flux tower sites (US-Ho1, US-Ho2 and US-Ho3) in the Howland Experimental  
Figure 5: Statistics for median Normalized Difference Vegetation Index (NDVI) difference between Landsat and ALI sensors, per six National Land Cover Database (NLCD) classes. Red line is median, blue box is quartiles, and black crosses are outliers. NDVI values were derived from Top of Atmosphere (TOA) reflectance.  
Forest, Maine. NDVI maps were derived from EO-1 Hyperion images over the 6 km area surrounding the three Howland Forest flux tower sites across four seasons from spring (5 March 2014), summer (12 August 2014), fall (22 September 2008) and winter (9 December 2010). Figure 6 shows the expected NDVI seasonality, with both the early and late year observations of low NDVI and high mid-season NDVI responding to the presence of green leaves and higher photosynthesis.  
For the areas around the three flux tower sites, NDVI was differenced between Hyperion and MODIS data (Figure 6, lower plot). Similar to other results, the NDVI difference is small (i.e., \(\Delta\)NDVI \(<\) 0.15) and does not increase over time although it varies across years. Most of the large NDVI differences (from 0.10-0.15) occur in US-Ho3 site (East Tower, Harvest Site), which is not as homogeneous as the other two flux tower sites.