esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Flexible Resistive Random Access Memory
## 3 Material System of Flexible Resistive Memory
### Flexibility of RRAM  
In flexible thin film transistors (TFT), the neutral plane concept or island structure concept are well developed in order to realize a flexible device.  
Likewise, the flexibility and elasticity of RRAM has attracted increasing attention from the scientific community. An in-depth understanding of the mechanical and electrical failure mechanisms of RRAM is of great significance for realizing its flexible integration. Organic elastic polymers are used as resistive switching media, which have poor resistance to the rotation of molecular chains and environmental water and oxygen under stretching, and the electrical stability of the prepared resistive memory under strain is generally poor. [PERSON] et al. [61] further proposed to use organic-inorganic hybrid metal-organic framework (MOF) materials as resistive switching media to construct RRAM devices to simultaneously improve the storage performance, elastic mechanical properties and thermal stability of flexible RRAM devices. MOF materials are organic-inorganic hybrid crystal framework materials constructed by organic ligands and metal ions or clusters through coordination bonds. Through the rational selection of organic and inorganic constituent units, MOF materials can achieve material deformation (breathing effect) through the change in the coordination bond angle and the regular change in the framework structure. This characteristic provides more space for the flexible elasticization of a resistive medium. [PERSON] et al. [97] have made new progress in the field of high-performance flexible resistive memory. The CPR1-based RRAM device has a simple sandwich structure and has excellent non-volatile memory performance: it has a very high switching ratio (\(10^{8}\)), a super fast 29 ns fast response, good reliability and reproducibility and long-term stability. Finally, the flexible RRAM device array fabricated by the all-printing technique exhibits high RRAM performance and robustness.  
Compared with flexible TFT, the simple sandwich structure of RRAM is usually not the main factor affecting its flexibility. The currently reported work on flexible RRAM mainly considers the material structure and process to achieve good device flexibility.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 4 Over-the-Air Demonstrations
### Adaptive Waveform Development  
In previous studies [27; 77; 78], several researchers have investigated adaptive waveform protocols that evolve in response to time-varying channel conditions. To demonstrate the accessibility of WISCANet, we tasked a graduate student with implementing these techniques around the pre-built ping-pong application.  
The adaptive waveform toolbox includes a variety of basic dynamic spectrum access, frequency-hopping, dynamic forward error correction (FEC), and modulation techniques, all of which are prescribed automatically by a protocol recommendation engine (PRE). This engine optimizes the waveform properties while the system is operating, actively adapting to changes in received SNR, spectral opportunities, and channel impairments by communicating through the genie channel. The adaptive waveform toolbox leverages MATLAB's object-oriented programming capabilities to reuse the same code structure, but with dynamic algorithmic components. This toolbox is loosely depicted in Figure 12.  
We designed an object-oriented Application Programming Interface (API) to interface with our large library of software-defined waveform components. In this API, components such as encoders, modulators, etc. are defined as classes that implement a standardized component interface. Applications interact with this interface instead of individual components; consequently, the components may be modified or swapped automatically by exchanging pointers, even while running an experiment.  
This API enables dynamic component selection in response to user inputs or changes in the environment [77; 78]. While the ping-pong system is operating, we inject additional noise to degrade the SNR at both radios. This corrupts the decoded message, thereby limiting channel capacity, which the PRE assesses and adapts to by decreasing the coding rate and changing the modulation for subsequent transmit and receive cycles. Through the adaptation, the subsequent communications are decoded without error even with the injected noise. The same adaptation capability functions to switch between single-carrier and OFDM modulations, or OFDM parameters to adapt to changing channel delay spreads. These results are depicted in Figure 13.  
Figure 12: Block diagram of the ping-pong application with integrated waveform development toolbox. Two radios alternate transmitting and receiving a message while simultaneously performing channel measurements and spectral sensing. A protocol recommendation engine automatically optimizes the waveform and frequency allocation to adapt to changes in received SNR, spectral opportunities, and channel impairments. WISCANet allows users to test these kinds of PHY and MAC layer techniques without having to design a network themselves, thereby reducing the barrier to entry and allowing users to focus on research rather than the tedium of networking and configuration.
## 3 Results and Discussion  
### Surface Morphologies  
Figure 2 shows the surface microstructure of the coating on the quartz fiber. The bare quartz fiber was also characterized for comparison purpose. It is shown from Figure 2a that the bare fiber has a typically smooth surface. The deposition of the coating obviously changed the surface, evidenced by the formation of large amounts of clusters on the surface (Figure 2b). The nano- and micro-sized clusters were partially observed, while most coating was relatively smooth (Figure 2b). It was suggested that the formation of a ceramic coating was followed as the nucleation-growth raw, that is, the formation of the coating clusters was dependent on the fast grow-up partially, implying the inhomogeneous energy distribution in the electrolysis plasma.  
Three elements, i.e., Si, O, and Al, existed on the surface of the coated quartz fiber, detected by the EDS result in Figure 2d. Both Si and O were also detected on the fiber substrate (Figure 2c). Obviously, the coating was so thin that the element in the substrate was able to be detected by EDS. In addition, Al was confirmed to result from the coating, with the content up to \(\sim\)11 wt%. The atom ratio between Si and O was calculated to be 1:1.76 for the coated fiber, compared to 1:1.16 for the bare fiber (Figure 2d). Obviously, the deposition of the coating led to the increase of the O content in the EDS result. Undoubtedly the increased O mainly resulted from the coating, revealing that the coating was mainly composed of Al and O. The distribution of Al and O was relatively uniform in the coating, evidenced by the EDS mapping result in Figure 2e.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling
#### 3.2.8 OpCASH  
In [40], a tiling scheme with variable-sized tiles is proposed. To deliver optimal cached tile coverage to user viewports (VP), Mobile Edge Computing (MEC) cache usage is used. Next, an ILP-based technique is used to determine the best cache tile configuration to decrease the redundancy of stored variable tiles at a MEC server while limiting queries to faraway servers, lowering delivery delay, and increasing cache utilization. OpCASHsuccessfully reduces data fetched from content servers by 85% and overall content delivery time by 74% with MEC.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 4 Results
### Result of Encoding Sensitive Object  
Figure 13a shows an image detected by the object detector interacted with logger, containing sensitive information such as human objects. Therefore, object detection must be followed by information obfuscation of these objects. Figure 13b presents the encoding results of the sensitive object by the logger, and the verifier recovers the sensitive object through XOR operation with the public key. The encoded image is used for integrity  
Figure 12: Constructed hash tree.  
Figure 13: Result of verification.  
checking. Both the encoded image and the object information, including the contours of the sensitive object, are transmitted to the REST server using the POST method.  
Performance evaluation indicates that the Jetson device is capable of real-time object detection, with an average processing time of 65 ms per image.
# Performance Model for Video Service in 5G Networks
## 3 Approaches
### Interference Coordination Approach
#### 3.1.2 Intra-cluster Interference Graph Construction  
After we construct the clusters, we build a directed graph G = (N, V), referred to as an intra-cluster interference graph. In the graph, N nodes represent N UEs, V edges represent interference between UEs, the direction from A to B means if UE B is transmitting, it will cause \(w_{A,B}\) interference to UE A, where \(w_{A,B}\) is the edge weight. The interference graph is built using Equations (6)-(8) by assuming a fixed transmit power; i.e., one UE transmits at a time. During scheduling, interference among the UE is dynamically calculated, using the transmit power calculated according to the number of UE scheduled simultaneously, in Equation (7).
## 3 Comparison and Analysis of Acceleration Response Results
### Comparison of Flat Ground with Normal Elastic Modulus  
#### 3.1.1 Acceleration-Time History  
Figure 6 shows the acceleration-time history graph of 1 \(g\) shaking table test and numerical analyses using DEEPSOIL and ABAQUS for the same input motion.  
As shown in Figure 6, both the 1 \(g\) shaking table tests and numerical analyses show the similar acceleration-time history near the bottom. In particular, the 1D ground response analysis results obtained by DEEPSOIL have a good match with the 1 \(g\) shaking table test results. This implies that the 1 \(g\) shaking table tests with the laminar shear box can simulate the free field behavior of soil subjected to earthquake loading. As shown in Figure 6a-h, it was observed that the time history of the experiment and numerical analysis was slightly different as it goes to the top of the model. This is expected because the more short-period frequency components are added to the overall curve and the amplification difference increases. The short-period frequency components included in the input motion had a minimal effect on the lower part, but the effect on the amplification accumulated toward the upper part of the model is judged to affect the  
Figure 6: Part of acceleration-time history graph for experiment and analysis in flat ground: (**a–d**) Sine 8 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m; (**e–h**) Sine 10 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m.  
Figure 7: Spectral acceleration of experiment and analysis in flat ground: (**a–d**) Sine 8 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m; (**e–h**) Sine 10 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m.  
Figure 7a-d present the spectral acceleration graphs of the experiment and numerical analysis for sine 8 Hz. As the depth becomes smaller from 0.5 m to 0.05 m, the greater the difference occurs between the experiment and the numerical analysis. As the depth becomes smaller, the spectral acceleration of numerical analysis is smaller than that of experiment. Figure 7e-h display the spectral acceleration graphs of the experiment and numerical analysis for sine 10 Hz. Near the bottom of the model, DEEPSOIL amplifies slightly higher than the 1 \(g\) shaking table test, and the amplification of ABAQUS is low. In Spectral acceleration at a depth of 0.3 m below the surface, DEEPSOIL predicted the very similar SA with the 1 \(g\) shaking table test and ABAQUS amplified somewhat lower. Overall, the amplification of ABAQUS is low, so the gap of spectral acceleration between the shaking table test and analysis increases from bottom to surface. The analysis of DEEPSOIL is very similar with the spectral acceleration graph of the shaking table test at all depths.
# Retentive Behavior of Locator versus Ball Attachments on Parallel versus Non-Parallel Implants
###### Abstract  
Several factors determine the retention force in removable implant-retained overdentures using prefabricated ball- or locator-type attachment systems. In this context, it was the goal of this in vitro study to examine the effect of implant angulation and female part alignment. Two model situations with two parallel or 12\({}^{\circ}\) tilted implants were fabricated onto which locator or ball attachments could be mounted. Simulated prostheses (n = 5) were made as antagonist parts and the assemblies were positioned in a universal testing machine for repeatedly (three times per female attachment) quantifying retention force. Statistical analysis was based on Shapiro-Wilk tests, Levene tests, ANOVAs, [PERSON]'s HSD tests and Welch \(t\)-tests, with the level of significance set at \(p\) < 0.05. With tilted implants, the retention force of locators was significantly diminished (\(p\) < 0.004) by at least 21%, while with ball attachments, a maximum reduction of 8% was noted, with only yellow inserts showing a significant difference (\(p\) = 0.040) compared with the parallel situation. Not aligning female reattentive components on tilted implants for achieving a common path of insertion in ball anchors had only a minor effect on reentive force (6.5% increase as compared with aligned female parts), which was not statistically significant (\(p\) = 0.100). Not being able to establish a common path of insertion in locator attachments affects retention force. Ball anchors allow for aligning female reattentive components, but due to the spherical structure of the male component this seems not even to be necessary.  
implant overdenture; retention; attachment +
Footnote †: journal:  
A recent study by [PERSON] et al. could not find consistent effects despite using a stringent study design [10]. A greater number of publications claim that implant angulation leads to a lower retention force [11; 12] of attachments as compared with parallelized situations [13]. From a long-term perspective, a significant decrease in retention seems to be inevitable, but this also occurs earlier when attachments are supported by non-parallel implants [14].  
Clinical studies combining these single aspects showed that implant angulation negatively affected the longevity of attachment retention [15], with locator attachments requiring a higher rate of maintenance as compared with ball attachments [16]. Unfortunately, the large body of literature available in this field is either focusing on clinical performance or on product comparisons while basic aspects such as alignment of female retentive parts are hardly investigated. Given that locator attachments do not allow for aligning female retentive parts as compared with ball attachments, it was the goal of this study to evaluate retentive forces on parallel and tilted implant configurations in a study design, eliminating potential co-factors.
# Tensioned Multi-View Ordered Kernel Subspace Clustering
## 4 Experiments
### Experiments of Multi-View Face Images Datasets  
The TMOKSC model proposed in this paper has the best performance on the YALE [27] and ORL datasets [28] (seen from Tables 2-5), verifying the design of multi-view subspace clustering optimized by applying sequential kernel tensor learning in this paper. Under the attacks of any missing rate, this TMOKSC model can keep a good clustering structure by means of mutual propagation among the clustering structure tensors. Thereby, the experiments show that The TMOKSC model compensates for the loss caused by the missing samples among different views and learns more accurate consistent clustering results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**YALE** & **WMSC** & **AASC** & **AWP** & **TIMKSC** & **TMOKSC** \\ \hline
0.1 & 64.82\({}_{\pm 1.65}\) & 44.03\({}_{\pm 2.21}\) & 58.27\({}_{\pm 3.87}\) & 85.12\({}_{\pm 3.73}\) & 94.27\({}_{\pm 0.46}\) \\
0.2 & 55.76\({}_{\pm 1.70}\) & 49.03\({}_{\pm 2.69}\) & 58.76\({}_{\pm 2.83}\) & 75.39\({}_{\pm 0.95}\) & 96.94\({}_{\pm 1.12}\) \\
0.3 & 41.24\({}_{\pm 2.02}\) & 39.48\({}_{\pm 1.75}\) & 47.42\({}_{\pm 2.99}\) & 92.55\({}_{\pm 5.09}\) & 90.06\({}_{\pm 1.08}\) \\
0.4 & 30.70\({}_{\pm 1.12}\) & 24.86\({}_{\pm 0.87}\) & 30.52\({}_{\pm 168}\) & 79.97\({}_{\pm 4.05}\) & 72.03\({}_{\pm 2.02}\) \\
0.5 & 21.27\({}_{\pm 1.16}\) & 18.76\({}_{\pm 0.54}\) & 23.64\({}_{\pm 1.47}\) & 55.03\({}_{\pm 1.94}\) & 79.27\({}_{\pm 1.09}\) \\
0.6 & 22.64\({}_{\pm 1.15}\) & 17.73\({}_{\pm 1.36}\) & 22.24\({}_{\pm 1.32}\) & 47.55\({}_{\pm 1.09}\) & 84.94\({}_{\pm 1.13}\) \\
0.7 & 22.48\({}_{\pm 1.63}\) & 18.30\({}_{\pm 1.34}\) & 21.48\({}_{\pm 1.85}\) & 67.12\({}_{\pm 1.27}\) & 89.23\({}_{\pm 1.52}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accuracy (%) scores over 20 runs by different approaches on the YALE dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**YALE** & **WMSC** & **AASC** & **AWP** & **TIMKSC** & **TMOKSC** \\
**Missing Rate** & & & & & \\ \hline
0.1 & 68.13\({}_{\pm 1.03}\) & 51.01\({}_{\pm 2.09}\) & 63.91\({}_{\pm 2.86}\) & 87.72\({}_{\pm 2.73}\) & 93.44\({}_{\pm 0.43}\) \\
0.2 & 58.75\({}_{\pm 1.00}\) & 53.07\({}_{\pm 2.55}\) & 61.86\({}_{\pm 1.80}\) & 78.96\({}_{\pm 1.16}\) & 96.46\({}_{\pm 1.12}\) \\
0.3 & 45.39\({}_{\pm 1.53}\) & 43.87\({}_{\pm 1.21}\) & 59.79\({}_{\pm 1.69}\) & 92.40\({}_{\pm 4.62}\) & 89.65\({}_{\pm 0.91}\) \\
0.4 & 31.45\({}_{\pm 0.90}\) & 29.30\({}_{\pm 0.79}\) & 34.48\({}_{\pm 1.68}\) & 82.04\({}_{\pm 3.32}\) & 85.67\({}_{\pm 0.34}\) \\
0.5 & 20.73\({}_{\pm 1.16}\) & 16.95\({}_{\pm 0.8}\) & 26.26\({}_{\pm 1.86}\) & 60.17\({}_{\pm 1.76}\) & 81.29\({}_{\pm 0.79}\) \\
0.6 & 26.26\({}_{\pm 1.60}\) & 17.44\({}_{\pm 1.59}\) & 27.55\({}_{\pm 1.65}\) & 47.55\({}_{\pm 1.09}\) & 84.37\({}_{\pm 0.36}\) \\
0.7 & 27.30\({}_{\pm 1.42}\) & 1
## 1 Introduction
Additionally, NDN provides a stateful forwarding plane with support for multicast transmission and inbuilt data caches, thus resulting in a more efficient usage of the installed transmission capacity. The main goal of this paper is to contribute to unveil the possibilities that emerge from applying the NDN architecture to large LEO satellite constellations using ISLs. Some open problems in these networks are related with forwarding and routing. In NDN, the routing plane is in charge of obtaining available routes, while the forwarding plane and the _strategy_ layer make decisions about the preference and usage of routes based on their performance/status. However, routing tables in NDN may consume more memory space and bandwidth in comparison to common IP routing tables [7]. In this paper, we propose a new location-based forwarding strategy for LEO satellite networks that takes advantage of the knowledge of the relative position of the satellites and the grid structure formed by the ISLs to perform the forwarding of NDN packets. So, forwarding at each node is done using only local information (node and destination locations), without the need of interchanging information between nodes, as is the case with conventional routing protocols. Simulation results show that this forwarding strategy results in high network utilization when we try to use the highest available bandwidth for multiple flows between several consumers and a producer, and so, it is a good candidate to promote the adoption of the NDN architecture in LEO satellite networks. This paper is organized as follows. Section 1 introduces the work in this paper. Section 2 is a review of the related work found in the literature. Section 3 describes the general architecture of an NDN network. Section 4 introduces the forwarding problem and presents our new GeoTag-based forwarding strategy. Section 5 evaluates the proposed solution. Finally, Section 6 presents some conclusions and future lines of research.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 4 Experimental Results and Analysis
### Model Training  
The experiment adopts the Adam optimizer to tune the parameters of the CIMKG model. The hyperparameter settings are shown in Table 1.  
In Table 1, \"batch size\" refers to the number of samples processed in a single training iteration. \"K-BERT encoder hidden units\" denotes the number of hidden units in the knowledge graph-based shared encoder. \"co-interactive module hidden units\" indicates the number of hidden units in the co-interactive module. \"co-interactive module number\" represents the number of co-interactive modules. \"attention_dropout\" is the dropout rate for the attention layer parameters, and \"learning rate\" is the learning rate of the model.  
The CIMKG model was trained using the hyperparameters listed in Table 1. The changes in the loss during the training process are shown in Figure 7.  
The \"Loss vs. Step\" figure on the left shows the loss values of the model at each training iteration. It is evident that the loss during the initial stage of training was exceedingly high. However, as the training progressed, the loss value decreased rapidly and stabilized at a certain threshold. This indicates that as the curves eventually flatten out, the current model training tends towards stability. The \"Average Loss per Epoch\" figure on the right presents the average loss values for each training epoch. As illustrated in the figure, the average loss  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Value** \\ \hline batch size & 16 \\ K-BERT encoder hidden units & 128 \\ co-interactive module hidden units & 128 \\ co-interactive module number & 2 \\ attention\_dropout & 0.2 \\ learning\_rate & \(5\times 10^{-5}\) \\ batch size & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model hyperparameters.  
Figure 7: Training loss curve.  
experiences a sharp decline within the first few epochs and subsequently slows down to a gradual and smooth decreasing trend. The stable low loss at the end of training suggests that the model effectively fitted the training data.
However, it is tedious to manually adjust the weights for the loss of different tasks. It was found that the optimal weights for each task are closely related to the task magnitude and ultimately depend on the task's noise level [36]. Therefore, in this paper, we propose an automatic weight adjustment method for a multi-task loss function based on homoscedastic uncertainty for seismic impedance inversion. The test results from the two synthetic datasets of the Marmousi2 and Over-thrust model, and one field dataset of the Volve model, show that the proposed method can automatically determine the optimal weight of the two tasks, and generate higher accurate impedance results than single-task model. The remainder of this paper is organized as follows. Section 2 describes the methodology, which includes the network architecture and the theory of multi-task loss function based on homoscedastic uncertainty. In Section 3, the three datasets and experimental results are presented. We discuss the limitations of the proposed method in Section 4. Conclusions are given in Section 5.