esa-sceva/satcom-chunk-collection huggingface dataset:
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 3 Experiments and Analysis
### Experiment Result
#### 3.2.2 Experiment Results on DFC2018 Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Class** & **UNet** & **DeepLab v3+** & **SegFomer** & **Swin-UNet** & **UNet-m** & **SS3 FCN** & **ENLFCN** & **SSDGL** & **FSSANet** & **CTAFNet** \\ \hline Healthy grass & 78.88 & 74.67 & 64.39 & 87.59 & 82.68 & 87.01 & 80.77 & 84.56 & 85.36 & 77.79 \\ Stressed grass & 86.36 & 83.94 & 69.95 & 88.68 & 88.92 & 90.34 & 86.86 & 85.30 & 87.41 & 88.08 \\ Evergreen trees & 81.53 & 84.70 & 78.05 & 81.47 & 83.75 & 87.67 & 82.83 & 85.48 & 80.27 & 89.47 \\ Deciduous trees & 61.14 & 68.57 & 36.77 & 68.18 & 65.83 & 75.80 & 60.05 & 51.59 & 61.20 & 77.39 \\ Bare earth & 84.67 & 93.53 & 71.37 & 82.90 & 86.68 & 88.98 & 70.52 & 89.12 & 93.53 \\ Residential buildings & 66.30 & 83.75 & 64.26 & 70.33 & 70.05 & 81.89 & 79.40 & 78.61 & 72.18 & 91.21 \\ Non-residential buildings & 86.99 & 89.68 & 69.82 & 86.89 & 86.82 & 93.00 & 88.03 & 85.80 & 89.92 & 93.69 \\ Roads & 55.15 & 57.47 & 32.47 & 50.39 & 55.79 & 65.72 & 51.22 & 48.82 & 53.57 & 69.71 \\ Sidewalks & 50.69 & 55.47 & 34.60 & 47.74 & 56.89 & 64.23 & 53.96 & 43.93 & 53.19 & 62.98 \\ Crosswalks & 2.61 & 12.13 & 4.20 & 14.15 & 23.34 & 29.23 & 17.57 & 8.84 & 11.31 & 14.00 \\ Major thoroughframes & 38.41 & 72.58 & 48.41 & 47.83 & 69.96 & 72.84 & 66.66 & 61.08 & 53.62 & 83.77 \\ Highways & 76.58 & 80.54 & 75.60 & 38.04 & 86.48 & 76.20 & 74.53 & 82.80 & 53.67 & 90.06 \\ Railways & 79.06 & 90.99 & 59.26 & 94.60 & 92.41 & 96.99 & 92.36 & 98.96 & 93.89 & 97.17 \\ Paved parking lots & 67.52 & 88.01 & 69.21 & 81.18 & 92.97 & 92.85 & 92.46 & 84.20 & 91.47 & 95.96 \\ Cars & 69.57 & 79.15 & 54.47 & 54.41 & 69.04 & 63.23 & 84.98 & 81.62 & 65.22 & 93.56 \\ Trains & 96.19 & 94.57 & 90.42 & 72.24 & 96.13 & 94.80 & 89.53 & 98.43 & 88.52 & 99.16 \\ Stadium seats & 57.83 & 85.46 & 64.68 & 82.01 & 93.90 & 95.40 & 88.87 & 62.24 & 81.17 & 96.52 \\ \hline pixel acc. & 84.84 & 88.66 & 73.44 & 83.56 & 87.56 & 91.12 & 87.24 & 84.72 & 85.97 & 92.74 \\ mIoU & 68.29 & 76.19 & 58.11 & 67.57 & 76.57 & 79.60 & 75.61 & 71.34 & 71.24 & 83.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification results for the DFC2018 test set. Figure 8: Visually comparison of different methods on the DFC2018 dataset.
# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
## 4 Experiments and Analysis
### Perceptual Robustness  
In order to evaluate the perceptual robustness of the proposed PIH scheme (LLRR-RiuLBP), we conducted some robustness experiments under the common content-preserving attacks, such as JPEG compression, Gaussian filtering, median filtering, noise addition, scaling and rotation (as listed in Table 2), based on the CVG-UGR image database [41]. Four of the standard test images derived from them are shown in Figure 2. The robustness comparison experiments with previous image hashing schemes, such as [PERSON] et al.'s [13], [PERSON] et al.'s [24] and [PERSON] et al.'s [26] schemes are illustrated in Figure 3 in terms of the normalized Hamming distance. Note that each average normalized Hamming distance in Figure 3 is calculated according to all the hash pairs of all the test images and the corresponding attacked images.  
It can be seen that the average normalized Hamming distance of the proposed scheme (LLRR-RuiLBP) is less than [PERSON] et al.'s and [PERSON] et al.'s methods. That is to say, our PIH scheme is more robust to content-preserving attacks than existing schemes [13; 24; 26]. This is partly because the LLRR adopted in the proposed scheme can effectively extract principle features from corrupted data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Average Running Time** \\ \hline [PERSON] et al.’s scheme [13] & 2.36 \\ [PERSON] et al.’s scheme [24] & 1.49 \\ [PERSON] et al.’s scheme [26] & 1.62 \\ Proposed scheme & 2.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average running times of different PIH methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attacks** & **Parameters** \\ \hline JPEG compression & Quality factor \(\in\) [10; 90] \\ Gaussian filtering & Standard deviation 0.4, 0.6,..., 1.8 \\ median filtering & Filter size \(\in\) [3; 15] \\ Salt \& pepper noise density \(\in\) [5\%, 15\%] \\ scaling & Scaling ratio \(\in\) [0.2, 2.0] \\ rotation & Rotation angle \(\in\) [0, 5.0] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The content-preserving attacks for robustness testing.  
Figure 3: _Cont_.  
Figure 2: Four of the test images from Computer Vision Group, University of Granada (CVG-UGR) image database [41].
## 3 Network Model
### Failure Model  
Communications over wireless channels are more insecure and susceptible to various attacks when compared to wired networks, e.g., the eavesdropping is easier on the wireless link. There are many reasons that cause network node(s) or link(s) failure. The failures may be caused by physical damage, such as fire, animal or vehicular accidents [27], or disasters, like deluges, earthquakes, sandstorms, _etc_. In the network, the failure can be modeled as the removal of nodes or links, or as the probability of nodes (links) failing. The difference is that when the node is removed, the corresponding links connected to it must be removed, as well. The work in [43] considers the statistically-independent and -dependent link failures under low stress (probability lower than 0.2) and high stress (probability more than 0.5) conditions, but without considering the node failure. The node failure model and link failure model seem quite different. However, [29] shows that the link failures are not qualitatively different from what is observed from the node point of view.  
The failure model is very important and has significant influence on the algorithm design. It is possible that one algorithm is only suitable for a specific failure, e.g., a single node failed at one time. In  
Figure 4: **(a)** Symmetric link: Node 1 can communicate with Node 2 and _vice versa_; **(b)** asymmetric link: Node 1 can send data to Node 2, but Node 2 cannot send data to Node 1; **(c)** non-omnidirectional radio.  
practice, the malfunctioning of a single component of a real system can generate a cascading effect. The work in [8] considers two failure types: (1) isolated failures, where each node in the multi-path has a probability of failure during some small intervals \(T\); and (2) patterned failures, the failure of all nodes in a circle within a given small time interval \(T\), which is Poisson distributed. The isolated failure models the independent node failure, and patterned failure models the geographically correlated failure. The works in [44; 45; 46] consider simultaneous failures. Nevertheless, losing a large number of nodes simultaneously is a low probability event [36].  
In this paper, the failures are classified into permanent failures, transient failures and faulty readings. To detect the node or link failure, the most common approach is broadcasting the messages and waiting for the response in a certain time interval in order to test the connectivity to other nodes. For example, in routing protocol Octopus [47], the package is broadcast by each node every hello timeout period. If a node does not hear from a neighbor for more than two times the hello timeout, it removes this neighbor from its neighbor list. Failure node detection is not the focus of this paper. A survey on failure detection in WSNs is given in [48].
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 3 Results
### Signal Quality Analysis  
#### 3.2.1 Spacecraft Scan  
The carrier of deep spacecraft could be identified in the receiving channel of each station, and the Carrier Noise Ratio (C/N) is mainly depended on the Gain-to-noise-Temperature ratio (G/T) value. The following Figure 11 compares the receiving signal power spectrum at the same scan with the same integration time in one cross-support DeltaDOR session. Since the CLTC JMS station keeps tracking Tianwen-1 during the DeltaDOR session, the spacecraft signal received by the JMS station is also analyzed here.  
Figure 10: Resolving phase ambiguity of DOR tones, dot point means original phase, star point means ambiguity resolved phase, color blue represents carrier, red +1 DOR, green \(-\)2 DOR, yellow +2 DOR: (**a**) Baseline CEB-NNO; (**b**) Baseline CEB-KSH. (**c**) Baseline NNO-KSH.  
The C/N of each station is listed in Table 3. It could be identified that the C/N value of JMS is 3-4 dB higher than KSH, which is approximate to the designed G/T difference value, as expected. While the C/N of KSH is about 5 dB lower than that of the ESA deep space antennas, they are basically the same.  
Antenna pointing angles (see Figure 12) are analyzed in order to exclude whether pointing accuracy of big dish antennas in lower elevation angle causes the loss of C/N. Additionally, it could be testified that the elevation angle is normal for the three antennas with the same size dish.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station** & **C/N(dB)** & **Antenna Dish (m)** \\ \hline KSH & 45.9 & 35 \\ JMS & 47.8 & 66 \\ CEB & 47.9 & 35 \\ NNO & 42.8 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: C/N statistics of each station.  
Figure 11: Spacecraft signal received at each station: (**a**) JMS; (**b**) KSH; (**c**) CEB; (**d**) NNO.
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 3 Results and Discussion  
### Preprocessing Dataset  
The experimental data was collected by the Intel Lab wireless sensor network research team from the University of California, who placed 54 sensors to collect environmental data information in the laboratory from 28 February to 5 April 2004. The sensor node distribution by Intel Labs is shown in Figure 4. The weather board sensor collects time stamp topology information as well as temperature, humidity, light and voltage values every 31 s. Due to node failure, there are several temperature values above 100 \({}^{\circ}\)C and below \(-30\)\({}^{\circ}\)C. We, therefore, preprocessed the temperature data by taking the threshold of \(-5\)\({}^{\circ}\)C and \(45\)\({}^{\circ}\)C by a priori knowledge.  
In order to reduce the difference between the input data of the compressed model, and converge the algorithm more quickly, we mapped the original data to \([0,1]\) by \(max-min\) normalization, where \(max\) is the maximum value of the node sensing data, \(min\) is minimum value.  
\[x^{*}=\frac{x-min}{max-min} \tag{14}\]  
After the preprocessing is completed, the node temperature database is converted to a data set. Each node collects temperature data as a column vector, and stores this data in the temperature.txt file. Due to node failure, some nodes only recorded a small amount of data. The average number of data by each node after preprocessing is 29,665. The maximum number of data in all nodes is 55,080, and the minimum number is 2507. We divide each node data into two parts:  
Figure 4: Sensor node distribution in Intel Lab.  
test set. In order to ensure that each node has enough training data to train the model, we did not use common split standards such as 7:3 and 6:2:2, and instead used 9:1.  
The more the number of samples in the training set, and the closer the empirical distribution is to the true distribution, the more accurate will the fitness of model distribution be. Increasing the number of training sets is an effective measure to prevent over-fitting. We provide following three methods: (1) Obtain data from the source; (2) estimate the data distribution parameters of the training set by statistical methods such as point estimation and interval estimation, and use this distribution to generate more data; (3) augment the training set by interpolation in the original training set, such as Kriging Interpolation and Natural Neighbor Interpolation.
## 4 Simulation Studies
### Bode Plot Analysis of Power Transfer with and without Data Transfer  
The Bode plots of the power transfer without data transfer (\(G_{pp1}\) shown in Equation (6)) from \(i_{i}\) to \(u_{eq1}\) and power transfer with data transfer (\(G_{pp2}\) shown in Equation (11)) from \(i_{i}\) to \(u_{eq2}\) are shown in Figure 8, where we can see that the difference between \"with data transfer\" and \"without data transfer\" can be ignored. This verifies that the addition of the data transfer channel has almost no impact on the power transfer.
## 6 60 GHz WiFi: IEEE 802.11 ay
### MIMO Operation  
As discussed under the modulation and coding schemes, having multiple spatial streams can multiply the overall throughput. Yet, the first 60 GHz WiFi standard did not support any MIMO schemes. The IEEE 802.11 ay standard, however, supports MIMO in up to eight streams in both single-user mode (SU-MIMO) and multiuser mode (MU-MIMO) [58]. Using the SU-MIMO scheme, two users can establish a high-throughput link between them, while the MU-MIMO is generally used in the AP to transmit downlink information to multiple users simultaneously.  
The adoption of 60 GHz WiFi has been slow compared to other WiFi standards. However, many vendors are steadily adopting the 60 GHz range into their products. For instance, the Nighthawk X10 router by Netgear supports 60 GHz operation [63]. Moreover, chip manufacturers are increasingly designing chips that support 60 GHz WiFi, which will mitigate the lack of consumer devices that support 60 GHz WiFi in the market [64].  
Table 3 presents a comparison of key features and limitations of the current WiFi standards. Sections 4-6 show that WiFi networks are evolving fast towards imminent integration with 6G networks. The sub-6 GHz networks are adding more capacity and improving their latency and guaranteed access features. On the other hand, the 60 GHz version is also expanding its scope in terms of modulation schemes, channel configurations, and MIMO operation. Hence, WiFi has the potential to be the first WLAN standard to fully integrate with the 6G ecosystem and provide seamless connectivity to users while meeting the 6G QoS requirements.  
These latest developments of WiFi 6, WiFi 7, and 60 GHz WiFi will play a key role in the 6G ecosystem by providing an added means of connectivity for devices that do not have embedded 6G transceivers and/or for situations where WiFi is more suitable than mobile network connections. For example, in dense urban locations where weakened 6G connectivity is available inside the buildings due the multitude of obstacles, WiFi can serve as a complementary solution to provide the high-bandwidth, low-latency, reliable connectivity required by indoor users. Further, WiFi and 6G could be simultaneously used to provide seamless connectivity and enhanced user experiences. For example, in a smart city/home/office environment, WiFi can be used to connect IoT devices with higher data rate requirements, whilst 6G wireless connections could be used for human-centric ultra-low-latency and high-bandwidth applications, including 4k video streaming and AR/XR.
## 2 Theoretical Model and Hypotheses
### Technology Acceptance Model and Diffusion of Innovations  
The technology acceptance model (TAM) was constructed to explore the factors determining the acceptance of computer technology by users. The two key variables [PERSON] et al. (2014) put forward included perceived ease of use (PEU) and perceived usefulness (PU). According to [PERSON] et al. (2014), PU is the degree to which an individual believes that using an innovation improves performance, while PEU is \"the perception of the extent to which a person believes that using an innovation is easy to do\" ([PERSON] et al., 2015). This paper thus proposes that the technology acceptance model (TAM) can be applied to new technology adoptions, with a particular interest in seniors' adoption of fitness apps. However, TAM-based research tends to concentrate more on the subjective views of users and their usage behavior towards new technology products and less on the characteristics of the product and the social factors involved ([PERSON] et al., 2015). This limitation can be overcome by including it in the diffusion of innovations.  
The diffusion of innovation (DOI) is a theory that explains why people adopt or reject innovations based on their beliefs about them ([PERSON] et al., 2015). [PERSON] (1988) argued that users typically progress through a series of natural steps, from first learning about an innovation to ultimately accepting it into their lives. Additionally, research has demonstrated a direct relationship between innovation acceptance and decision-making, with innovation attributes being measurable across five key dimensions: relative advantage, compatibility, complexity, trialability, and observability ([PERSON] et al., 2015). By combining TAM's focus on user perceptions with DOI's emphasis on the characteristics of the innovation, we can gain a more comprehensive understanding of the factors influencing older adults' acceptance of fitness apps.
## 4 Applications of Integrated Photonics
### Optical Switches  
The optical switch is a crucial component of PICs, finding extensive use in optical communications and networks, optical computing, and sensing applications such as LiDAR [81]. Typically, Si-integrated optical switches utilize the TO [82; 83; 84; 85] or carrier dispersion effect [86; 87; 88] to achieve reconfigurable signal routing. It is evident that the TO effect is associated with a high level of power consumption. Furthermore, the carrier dispersion effect is characterized by a restricted refractive index alteration. In addition to this, both effects are non-latching, thereby resulting in the continuous utilization of power even when switching is not required. To address these limitations, phase change materials (PCMs) have been integrated into Si-integrated optical switches [89; 90; 91; 92]. Non-volatile (NV) hybrid EO plasmonic switches and innovative architectures for NV combinational and sequential logic circuits are presented by [PERSON] et al. [90]. The EO switches in question featured a plasmonic WG coated with a thin layer of PCM, as illustrated in Figure 3d. The optical losses in the WG were managed by modifying the phase of the PCM between its amorphous and crystalline states. The phase transition in the PCM was induced by two methods: either by electrical threshold switching or thermal conduction heating, utilizing external electrical heaters, or the plasmonic WG metal itself as an integrated heater. Figure 3e,f illustrate the field distributions of the plasmonic slot WG in both the amorphous and crystalline phases of Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\) (GST). The implementation of logic gates, a half-adder circuit, and sequential circuits utilized these plasmonic switches as active components. The plasmonic switches and logic operations were demonstrated to have minimum extinction ratios (ERs) exceeding 20 dB, compact structures, low operating power, and high-speed performance. By integrating photonics, plasmonics, and electronics on the same platform, an efficient architecture for logic operations was created [90].
## 4 Numerical Simulations and Analysis  
Consider a mmWave MIMO system in the downlink, operating at a frequency of 28 GHz. The base station is equipped with 64 antennas, while the user terminal is equipped with 16 antennas. Both adopt a uniform linear array (ULA) with an antenna spacing of \(\frac{\lambda}{2}\). The number of RF chains is \(N_{RF}^{t}=N_{RF}^{r}=4\), and the number of data streams is 1. Assuming that both the base station and the user terminal have perfect knowledge of the channel state information, the mmWave channel \(\mathbf{H}\) is modeled as consisting of six scattering clusters, with each cluster contributing eight propagation paths. For the \(i\)-th cluster, the departure (arrival) azimuth angles \(\phi_{il}^{i}\) (\(\phi_{il}^{i}\)) of the eight paths are assumed to follow a Laplace distribution with a mean of \(\phi_{i}^{i}\) (\(\phi_{i}^{r}\)) and an angular spread of \(\sigma_{\phi_{i}^{i}}\) (\(\sigma_{\phi_{i}^{r}}\)). \(\phi_{i}^{r}\) (\(\phi_{i}^{r}\)) are uniformly distributed within \([-\frac{\pi}{3},\frac{\pi}{3}]\), and \(\sigma_{\phi_{i}^{r}}=\sigma_{\phi_{i}^{r}}=5^{\circ}\). Considering practical scenarios, the departure azimuth angles are constrained within the range \([-\frac{\pi}{3},\frac{\pi}{3}]\), while the arrival azimuth angles are restricted to the range \([-\frac{\pi}{2},\frac{\pi}{2}]\).
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
## 3 Mathematical Model of Noise Removal from the LTE System
### Influence of Other Undesirable Signals
Bearing in mind that both the quick code correlation value and the fact that the function is in an intermediate position, hence the maximum of the correlation function is shifted from its maximum value. A condition must be met in which the delay of the signal code is zero, so it is not aligned with the position of the corresponding bit in the code. Assume the case that the distorted course of the correlation function contributes to the appearance of zero values in the discriminant correlation function, which results in reducing errors in the trace process. Then, it can be concluded that the situation of occurrence of zeros in the function is caused by the occurrence of a disturbing signal from the LTE system; therefore, \(f(\varepsilon)=0\) takes place, assuming that \(\varepsilon\) is a tracking error resulting from the ongoing process of receiving an interference signal from the LTE system. In the case where only the SLTE signal is present, the corresponding error for the zero point of intersection of the phase discriminator function is \(\varepsilon=0\). The DLL (Delay Locked Loop) blocks the signal by tracing the zero point of the phase discriminator function. However, when multipath noise is received, due to the effects from interference of the LTE system, the zero point of the phase discriminator function deviates from the SLTE signal code delay. When the receiver receives only one multipath interference, whereby the discriminant function in \(f(\varepsilon)=0\), the corresponding DLL error can be derived as (15):  
\[\varepsilon=\left\{ \begin{array}{ll}\frac{\Delta\tau_{1}\cos\left(\varphi_{1 }^{\prime}-\varphi_{k}^{\prime}\right)}{\alpha_{0}^{\prime}/\alpha_{1}^{ \prime}+\cos\left(\varphi_{1}^{\prime}-\varphi_{k}^{\prime}\right)}&0<\Delta \tau_{1}\leq\tau_{L}\\ \frac{d}{\alpha_{0}^{\prime}/\alpha_{1}^{\prime}}\cos\left(\varphi_{1}^{ \prime}-\varphi_{k}^{\prime}\right)&\tau_{L}<\Delta\tau_{1}\leq\tau_{H}\\ \frac{\cos\left(\varphi_{1}^{\prime}-\varphi_{k}^{\prime}\right)}{\alpha_{0}^{ \prime}/\alpha_{1}^{\prime}-\cos\left(\varphi_{1}^{\prime}-\varphi_{k}^{ \prime}\right)}(d/2+T_{c}-\Delta\tau_{1})&\tau_{H}<\Delta\tau_{1}\leq T_{c}+d/ 2\\ 0&\Delta\tau_{1}>T_{c}+d/2\end{array} \right. \tag{15}\]  
where \(T_{c}\) is the length of the integrated circuit; then, \(\tau_{L}\) and \(\tau_{H}\) can be represented as (16):  
\[\left\{ \begin{array}{ll}\tau_{L}=\frac{\alpha_{0}^{\prime}/\alpha_{1}^{ \prime}+\cos\left(\varphi_{1}^{\prime}-\varphi_{k}^{\prime}\right)}{\alpha_{0} ^{\prime}/\alpha_{1}^{\prime}}d\\ \tau_{H}=\frac{d\cos\left(\varphi_{1}^{\prime}-\varphi_{k}^{\prime}\right)}{2 \alpha_{0}^{\prime}/\alpha_{1}^{\prime}}+T_{c}-d/2\end{array} \right.. \tag{16}\]