esa-sceva/satcom-chunk-collection huggingface dataset:
# User Acceptance of Information Feed Advertising: A Hybrid Method Based on SEM and QCA
###### Abstract  
It is of great significance for enterprises' development to effectively use mobile Internet to carry out information feed advertising. This paper aims to study the influence factors and effect of the users' acceptance intention of information feed advertising through empirical analysis to provide references for further optimizing information feed advertising strategy. Traditional quantitative analysis methods, such as the Structural Equation Model (SEM), can only measure a single factor's influence from an individual perspective. Therefore, we introduce the Qualitative Comparative Analysis (QCA) and present a two-stage hybrid method based on SEM and QCA. In the first stage, we analyze the influence of a single variable on the acceptance intention of information feed advertising by SEM. Then, in the second stage, we analyze the impact of different variable combinations by QCA. Based on the actual questionnaire data, we define six independent variables and use AMOS, SPSS, and fsQCA to carry out SEM analysis and the fuzzy-set-based QCA analysis, respectively. The SEM analysis results show that the four factors (including consistency, informativeness, sociability, and advertising reward) have a significant positive impact on user acceptance of information feed advertising. On the contrary, perceived advertising clustering has a significant negative impact. In addition, accuracy has no considerable effect. The QCA analysis reveals that seven constructs of six variables can all significantly enhance information feed ads' acceptance intention.  
information feed advertising; user acceptance; structural equation model; qualitative comparative analysis +
Footnote †: journal: Future Internet
## 1 Introduction
[PERSON] et al. discusses the performance of three state-of-the-art networks (AlexNet, VGG-19 and Inception-v3) on characterization of coronary artery pathological formations from OCT imaging. In the experiment of finetuning, classification layers are adapted to their data set, and in the experiments of feature extraction, features are extracted before the last fully connected layer right before the classification layer (pre-trained AlexNet, VGG-19) or before the last depth concatenation layer (mixed10 layer in pre-trained Inception-v3). Experimental results show that finetuning method outperforms the feature extraction method with RF as classifier [24]. [PERSON] et al. compares training GoogLeNet from scratch and finetuning pre-trained GoogLeNet on retinal OCT dataset, and their results show that fine-tuned DNN performs better both in convergence speed and classification accuracy [25]. Extensive experiments based on 4 distinct medical-imaging applications from [PERSON] et al. demonstrate that deeply fine-tuned convolutional neural networks (CNN) are useful for medical image analysis, performing as well as fully trained CNNs and even outperforming the latter when limited training data are available. They also observed that the required level of finetuning differs from one application to another, which means that the strategy of finetuning still remains an open question [26]. Figure 1: (**Far left**) Choroidal neovascularization (CNV) with neovascularization membrane. (**Middle left**) Diabetic macular edema (DME) with retinal-thickening-associated intraretinal fluid. (**Middle right**) Multiple drusen present in early (dry) age-related macular degeneration (AMD). (**Far right**) Normal retina with preserved foveal contour and absence of any retinal fluid/edema. Since pre-trained networks are trained on 1.2 million natural images of 1000 classes from ImageNet, the filters learned may not be suitable for the retinal OCT classification. To further explore the filters and improve performance of pre-trained DNNs, some visualization techniques of DNN are developed [27,28]. Their visualizations show the increase in complexity and variation on higher layers, comprised of simpler components from lower layers. The variation of patterns increases with increasing layer number, indicating that increasingly invariant representations are learned. Inspired by these results, we remove the deeper layers of the pre-trained DNNs such that in the process of transfer learning, the DNNs classify OCT images with the help of low-level features of natural images, without the interference of high-level features. We investigate the performance on ImageNet dataset of some modern DNNs such as VGGNet, GoogLeNet, ResNet, DenseNet, MobileNet [29,30] and NASNet [31]. In consideration of the balance between classification accuracy and computational efficiency, we choose Inception-v3, ResNet50 and DenseNet121 as base networks in our experiments. Our work is organized as follows. First, the architectures of modified DNNs are illustrated in Section 2. Datasets, experiments and results are presented in Section 3. Finally, this study is concluded in Section 4.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Packet Types in ERPMSA-UWSN  
Following are the four types of packets in ERPMSA-UWSN (i.e., NR, DATA, ACK, and CTRL).  
* Neighbor Request Packet (NR): The source node uses NR to get information about the neighbor forwarders. The format of the NR packet is NR (TYPO, SID, D, VA), where TYPO is the two-bit number indicating the type of the packet. For NR, the value of TYPO = 00. SID is the ID of the source node and D is the depth of the source node.
* Acknowledgment Packet (ACK): ACK packet is a reply from the neighbor node in the response of NR, containing full information of its sender. The format of the ACK is ACK (TYPO, SID, D, E), where the value of TYPO for ACK is \"01\", SID is the ID of that neighbor node, D is the depth and E is the energy of the ACK packet's sender node. We store these values in a separate table called MTAB, in descending order corresponding to their IDs. In the table, the MSF value of the first index node is highest among all nodes and other indices are occupied accordingly.
* Control Packet (CTRL): CTRL packet is used to invoke the masters for the forwarding procedure. By awakening the masters, it automatically turns slave nodes into the indolent state. The format is CTRL (TYPO, SID, PTR1, PTR2, D) where TYPO for this packet is \"10\", SID is the ID of the sender node, PTR1 and PTR2 are pointers to the array that contain IDs of the three descending indexed Masters of PFZ1 and PFZ2 respectively, while D is the depth of the sender node.
* Data Packet (DATA): DATA packet contains the real data with the payload and header. The format of the DATA packet is DATA (TYPO, SID, DID, D), where TYPO value is \"11\", SID represents the source node ID, DID indicates the destination node, while D represents the depth of the sender node.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 3 Related Work
### USRP/GNU Radio and RP3 Testbeds  
In this section, related works on USRP/GNU radio with and without RP3 testbeds are presented. For the testbed without RP3, the data packet processing unit is a PC; while for the testbed with separate processing unit, RP3 is used. The different processing units can affect performance in terms of end-to-end delay and packet loss. In our experiment, user datagram protocol (UDP) is used. In general, multimedia applications have stringent quality of service (QoS) requirements (i.e., delay sensitive yet loss tolerant), while data applications (e.g., email) require a higher data transfer reliability (i.e., loss sensitive yet delay tolerance). UDP has been the preferred transport layer protocol for multimedia applications because it is connectionless and does not perform retransmission during packet loss, which reduces delay at the expense of acceptable packet loss. Due to the challenges of meeting the stringent QoS requirements of multimedia applications, most investigations using testbeds in the literature use UDP rather than transmission control protocol (TCP) [15; 16; 28; 29; 30]. In this experiment, we use multimedia applications, whereby the source host sends a video stream to the destination host that plays out the video using the VLC media player.
# GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures
## 4 Network Training and Testing  
We trained the GAN-based framework on the training dataset for 500 epochs. We used the Adam optimizer with a learning rate of 0.01 and a normal distribution initializer to start the training process. It took 19 h and 37 min to train the network for 500 epochs on a desktop PC equipped with Intel i9-9920X CPU (sourced from Intel Corporation, Santa Clara, CA, USA) and NVIDIA RTX 2080 Ti graphics card (sourced from NVIDIA Corporation, Santa Clara, CA, USA). Figure 6 shows the training losses of the low- and high-resolution GANs. The loss curves of the generators decreased sharply in the first 80 epochs and converged gradually to around 200 at the 500 th epoch, indicating that the image quality of both the low- and high-resolution generators gradually improves with the training epochs, as shown in Figure 6a. Moreover, the MSE value of the low-resolution generator reached approximately 1400, while the high-resolution GAN further reduced the MSE value to around 400. This demonstrates that the high-resolution GAN greatly optimizes the low-resolution results. Figure 6b depicts the loss curves of the low- and high-resolution discriminators when distinguishing real and fake images. The loss values of the discriminators gradually stabilized with the training epochs, and all converged to around 0.25 at the 500 th epoch. This was mainly because the two discriminators failed to tell apart the generated fake images and the real images. As a result, they gave a neutral score of 0.5 to both the real and fake images, which corresponds to an MSE value of 0.25. In general, we can infer from the training curves that the generators and the discriminators can reach a balanced state at the 500 th epoch, which demonstrates that the GAN-based framework achieves the ideal training effect.  
After training, we evaluated network performance on the test dataset, which contained 100 numerical models. We computed the ACC, SSIM, and RAscore of the network, and the test metrics were consistent with the findings of the training process. On the test dataset, we obtained the network performance of ACC = 91.36%, SSIM = 0.93, and RAscore = 91.77.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modeling Parameter** & **Value** \\ \hline Model size (width \(\times\) depth \(\times\) thickness) & 2.00 m \(\times\) 4.00 m \(\times\) 0.40 m \\ Discretized grid & 0.005 m \(\times\) 0.005 m \(\times\) 0.005 m \\ Antenna detection depth range & 0.2–3.8 m \\ Transmitting–receiving baseline distance & 1.8 m \\ Sampling interval & 0.1 m \\ Source wavelet & Ricker \\ Center frequency & 500 MHz \\ Time window & 50 ns \\ Sampling points for each A-scan & 5194 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the FDTD modeling parameters.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 3 Requirements of Internet of Drones (IoD)
### Communication Requirements
#### 3.1.2 Real-Time and Remote Communication  
Real-time and remote communication capabilities permit remote controllers to issue a time-based command and control instructions on the basis of the drone flight status report in real-time, such as space co-orders and equipment status. Real-time and remote controls are primarily used in the monitoring of flight conditions, drone task, and equipment and emergency control. The latency and rate of certain data requirements should be met to allow remote control for drones. The downlink (from the base station to drone) data rates in many application scenarios are about 300-600 kbps, and the existing 4G+ networks will fulfill this requirement. For potential implementations, such as remote real-time operations, the latency criterion is strict to guarantee the precision and experience of service [19].  
Figure 2: The key requirements of IoD.
## 3 Method of the Study
### Data Composition  
The data used in this study were extracted from the databases of Company E's operational system and POS (Point of Sales) system, covering the period from 1 January 2022 to 31 December 2022, for the duration of one year. This data can be categorized into two types: master data, which include reference information, and various transaction data used for store operations.  
The master data include store master data containing store codes, store names, store locations, store types, store opening dates, and store sizes. Additionally, there is promotion master data, which hold information on promotion codes, promotion names, promotion types, promotion periods, and the stores where the promotions apply. Furthermore, the product master data comprise product codes, product names, product categories, product attributes, and product suppliers. They also include store-specific product master data containing products available at each store. As for transaction data, they involve sales performance data generated from each store's POS system, inventory movement data for each store, and product ordering data for each store. Customer data that could be identified for individual purchases in each store accounted for less than 5% of the total purchase data, making it challenging to consider customer data for assessing store similarity. Therefore, these data were excluded.  
Data preprocessing was conducted in this study prior to data analysis, involving the selection and extraction of relevant items from the entire dataset, removal of inaccurate or incorrect data, and data modification to enhance data accuracy. Outliers primarily originated from sales quantities within the POS data. Data exhibiting distinct patterns from the typical customer sales patterns due to special store sales (such as B2B sales) or specific purpose-driven customer purchases (e.g., bulk purchases for event preparation) were eliminated. Additionally, new items were added for analysis, including daily totals of transaction data, store-specific sales summaries, and recommendation scores. Finally, new database tables, encompassing metric tables and category conversion tables necessary for model generation, were created during the model creation phase. Subsequently, essential data from each table were merged to construct the analysis dataset. As a result, operational and transaction data of approximately 400 million records from Company E were leveraged for analysis.
# Structural Augmentation in Seismic Data for Fault Prediction
## 3 Seismic Fault Detection Based on U-Net
### Neural Network Architecture  
Our network architecture is modified from a 2D U-shaped network used to implement 2D medical image segmentation [88], in which the input seismic data are first downsampled and feature extracted by an encoder, and then, they are up-sampled by a decoder to map the features into fault zones. As shown in Figure 8, our network utilizes skip connections to efficiently aggregate multi-scale semantic features from shallow and deep layers in the encoder. These skip connections can effectively reduce the long-path dependence of the network and transfer feature details between the encoder and decoder, which helps to improve the accuracy and computational efficiency of network learning.  
Figure 7: Demonstration of oblique-slip fault drag. (**a**) Normal drag in sinistral-reverse fault. (**b**) Reverse drag in sinistral-reverse fault.  
In our network architecture, the encoder part consists of seven 3D convolutional layers in total. After inputting 3D seismic data, we apply successive convolutional blocks for feature extraction and down-sampling, which compresses the computational cost while increasing the receptive field. Each convolutional block contains two \(3\times 3\times 3\) convolutional layers, two rectified linear unit (ReLU) layers, and one 3D maxpooling layer. As shown in Figure 8, multi-scale semantic information is gradually extracted from shallow and deep networks through three convolutional blocks, and each scale is composed of two 3D convolutional layers.  
The decoder, consisting of three convolutional blocks and three transposed convolutional layers, upsamples and maps features into fault labels with the same size as the 3D seismic volume. The network layers at each scale are also composed of two \(3\times 3\times 3\) convolutional layers and two ReLu layers.
## 3 System Model and Design Objectives
The data fragmenting node forwards fragmented data to a 1-hop neighborhood, and this fragmenting process keeps moving until it locates a DAN. The DAN collects the data from the data fragmenting node and applies the aggregation processes before sending it to another DAN or the base station. Furthermore, DANs can communicate with other DANs. A DAN can use typical aggregation functions, including AVERAGE, MIN, MAX, SUM, COUNT, and authentication and authorization features. The additive aggregation functions comprise standard deviation, grouping, and variance that can quickly expand into additive SUM functions [40]. This secure data aggregation supports data fragmentation, as depicted in Figure 1.  
Figure 1: SDAAA healthcare monitoring system architecture model.
## 3 The Software of TLE Observation System
### System Operation  
The operational scenarios of the VisionCube have been devised to observe blue jets by taking into account the TLE's size (up to 20 km), duration (up to 250 ms) and possible occurrence rate [5]. Figure 8 illustrates a sequence of overall mission operation including the transaction activity between C&DHS.  
When the mission start signal is triggered by the C&DHS, the Nios-II initiates the detection process by enabling digitization of the signals from MaPMT. The DDC264 integrates the current outputs of the MaPMT and digitizes them at the sampling rate of 6 MHz, providing the digital data to the discriminators continuously. Three discriminators described in Section 3.1 are written in Verilog-HDL (hardware description language) to determine whether the sampled signals represent the TLE existence within the FOV, and if that is the case, the location of the TLE on the MaPMT's sensing plane is estimated. It should be noted that the DDC264 measures signal uninterrupted until a TLE is detected, while the temporal and energy discriminators continues to check the signals from the MaPMT. As a voting logic consisting of three discriminators decides the detection of TLE, the camera IP (intellectual property) logic is triggered by the detector system with the estimated location of the TLE. Because the 8 \(\times\) 8 cell array of the MaPMT is associated with the CMOS sensor pixel array (2592 \(\times\) 1944 px) by a lookup table, the image data on the CMOS sensor can be partially read over the ROI to the onboard SDRAM buffer memory. This feature allows consecutive acquisition of as many  
Figure 8: Operation sequence of TLE observation system. The TLE observation system conducts its mission independently after receiving the start command from C&DHS. The Nios-II manages the system and communicates with C&DHS.  
frames of TLE images at the reduced size (640 \(\times\) 480 px), albeit the limited availability of onboard memory. In the meantime, the Nios-II reads the digitized data of the MaPMT's 64 channels as the mission log data. Note that the Nios-II also raises a flag to the C&DHS in order to request the mission information, which includes the mission identification number, UTC time, and the GPS position of the VisionCube, etc. The mission information is merged with the data generated by the detector and camera subsystem to create a time-stamped mission log files. Taking into account the limited data capability of downlink channel, the data collected per event are chosen to be three gray-scale images in VGA format, 128 frames of MaPMT data, and a mission status file, of which the data volumes are 921.6 KBytes, 20.48 KBytes, and 40 bytes, respectively. After being requested by the C&DHS, the TLE observation system sends the collected mission log files. The C&DHS stores the mission log files on the flash memory and transmits them to the ground station whenever the ground communication is ready. Because the capacity of downlink communication is approximately 340 KBytes per a single access, three or four access will be required to downlink the entire mission data of a single event.
# Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory
## 1 Introduction
However, the partial comprehension of the physical mechanisms underlying memristor operation prevents the application-oriented design and tuning of the devices. Physical multiscale modeling and simulation provide a powerful tool for investigating the physical mechanisms responsible for analog switching in memristors and to highlight the effects of the material properties (including defects) on the device performance. This information can then be used to further develop memristive technology, optimizing the properties of the devices (i.e., geometry and materials) to match the specifications required for the desired application (i.e., electric properties, data retention, variability, and noise), and strongly reducing its time-to-market. In this paper, we use a multiscale modeling platform to simulate RRAM devices and investigate the physical mechanisms underlying their operation. The simulations are designed to highlight the effects \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **NVM 1[26]** & **DNN 2[39]** & **SNN 3[40]** \\ \hline Feature size & \textless{}12 nm & \textless{}10 nm & - \\ Number of levels & \textgreater{}2 (1 bit) & \textgreater{}100 (6.45 bits) & \textgreater{}64 (6 bits) \\ Dynamic range (on/off ratio) & - & \textgreater{}100 & \textgreater{}200 \\ State retention & \textgreater{}1 year & \textgreater{}10 years & \textgreater{}10 years \\ Device endurance & \textgreater{}103 cycles & \textgreater{}109 cycles & \textgreater{}109 cycles \\ Energy consumption & \textless{}100 p\textgreater{}/write & \textless{}10 f\textgreater{}/programming pulse & \textless{}10 f\textgreater{}/spike \\ Linearity & - & Yes & Yes \\ Symmetry & - & Yes & - \\ Switching time & \textless{}100 uS & \textless{}100 ns & - \\ \hline \hline \end{tabular}  
* System level performance for replacing a NAND flash memory. 2 Performance for memristors in a deep neural network (DNN) accelerator with a crossbar memory array architecture. 3 Performance for memristors as a Spiking Neural Network (SNN) artificial synapse.  
\end{table} 
Table 1: Summary of desired performance metrics for memristors. of the device geometry, materials, forming conditions (i.e., temperature, current compliance, voltage stress mode), and programming. The discussion on the simulation results shows the relevance of the obtained insights for the different AI applications (i.e., non-volatile memories, deep neural networks, or spiking neural networks) and provides useful design principles for RRAM application-oriented design.