esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 2 Related Work
### CASSI Imaging Model  
Figure 2 shows the schematic diagram of the CASSI system. The hyperspectral image corresponding to the physical scene is denoted as \(F\in R^{H\times W\times N_{\lambda}}\), where H, W, \(N_{\lambda}\) represent the height, width, and the number of spectral bands, respectively. The CASSI system first modulates F by the aperture mask \(M^{*}\), which is defined as follows:  
\[F^{\prime}(:,:,n_{\lambda})=F(:,:,n_{\lambda})\odot M^{*},1\leq n_{\lambda} \leq N_{\lambda}, \tag{1}\]  
where \(F^{\prime}\in R^{H\times W\times N_{\lambda}}\) denotes the modulated HSI, \(\odot\) represents the element-wise dot multiplication. After modulation, the disperser applies a shearing transformation to the modulated hyperspectral image. Taking the Y-axis shift as an example, the transformation process can be expressed as follows:  
\[F^{\prime\prime}(u,v,n_{\lambda})=F^{\prime}(x,y+d(\lambda_{n}-\lambda_{c}),n_{ \lambda}), \tag{2}\]  
where \(\lambda_{c}\) represents the reference spectral band, and \(d(\lambda_{n}-\lambda_{c})\) denotes the translation amount of the \(n_{\lambda}\)-th spectral band in the y direction, typically a linear function of the number of spectral index. All the spectral bands after shear transformation are projectedupon the two-dimensional sensor array, and the images of different spectral bands at the same position are accumulated, which can be calculated as follows:  
\[Y=\sum_{n_{\lambda}=1}^{N_{\lambda}}F^{\prime\prime}(:,:,n_{\lambda})+N, \tag{3}\]  
where \(Y\in R^{H\times(W+d(N_{\lambda}-1))}\) represents the captured two-dimensional snapshot measurement, and \(N\in R^{H\times(W+d(N_{\lambda}-1))}\) represents the measurement noise of the sensor during the imaging process. Equation (3) represents the forward observation equation of CASSI imaging. Based on this observation equation, the reconstruction algorithm recovers the original hyperspectral image from the two-dimensional snapshot measurements.
# Observations on the Computation of Eigenvalue and Eigenvector Jacobians +
## 2 Existence of Eigenvalue and Eigenvector Jacobians
### Constraining the Eigenvectors to the Unit Hypersphere  
Begin with the most commonly used normalization for the standard eigenvalue/eigenvector problem, \(\boldsymbol{x}=\mathbf{v}^{H}\). Substitution into Equation (3) leads to:  
\[\mathbf{v}^{H}\mathbf{v}=\alpha\qquad\rightarrow\qquad-\alpha+\mathbf{v}^{H} \mathbf{v}=0, \tag{7}\]which constrains the eigenvector to lie on the hypersphere of radius \(\alpha\). As usual, the superscript of \(H\) indicates the Hermitian (conjugate) transpose (\(\mathbf{A}^{H}=\text{conj}(\mathbf{A})^{T}\)). Expressing this normalization in terms of the elements of \(\mathbf{v}\) gives  
\[-\alpha+\sum_{k=1}^{n}\overline{v_{k}}v_{k}=-\alpha+\sum_{k=1}^{n}(x_{k}-iy_{k })(x_{k}+iy_{k})=-\alpha+\sum_{k=1}^{n}x_{k}^{2}+y_{k}^{2}, \tag{8}\]  
where \(v_{k}=x_{k}+iy_{k}\) is the \(k^{th}\) element of the eigenvector \(\mathbf{v}\) and \(\overline{\bullet}\) is the complex conjugate of \(\bullet\).  
We proceed by attempting to determine the partial derivative of this normalization with respect to the eigenvector in a domain around the eigenvector. First, since the eigenvector may be complex requiring a complex partial derivative, we need to check that the normalization equation is analytic in a region around the eigenvector. This is done by using the Cauchy-Riemann equations. Since the partial derivative and summation operators are linear, the required partial derivatives for checking the Cauchy-Riemann equations are  
\[\frac{\partial}{\partial x_{j}}\text{Re}\left[\sum_{k=1}^{n}x_{k}^{2}+y_{k}^{ 2}\right]=2x_{j}, \tag{9}\]  
\[\frac{\partial}{\partial x_{j}}\text{Im}\left[\sum_{k=1}^{n}x_{k}^{2}+y_{k}^{ 2}\right]=0, \tag{10}\]  
and  
\[\frac{\partial}{\partial y_{j}}\text{Re}\left[\sum_{k=1}^{n}x_{k}^{2}+y_{k}^{ 2}\right]=2y_{j}, \tag{11}\]  
\[\frac{\partial}{\partial y_{j}}\text{Im}\left[\sum_{k=1}^{n}x_{k}^{2}+y_{k}^{ 2}\right]=0, \tag{12}\]  
where \(\text{Re}\left[\bullet\right]\) takes the real component of \(\bullet\) and \(\text{Im}\left[\bullet\right]\) takes the imaginary component of \(\bullet\). Now, using the Cauchy-Riemann equations, it is easy to see that this function is differentiable only when \(x_{j}\) and \(y_{j}\) are \(0\) and is analytic nowhere. In addition, this shows that this normalization is only differentiable when \(\mathbf{v}=\mathbf{0}\), which is not generally a valid eigenvector. Therefore, this normalization cannot be used when the eigenvectors are complex because the normalization is not differentiable or analytic for valid eigenvectors. We do note, however, that when \(\mathbf{v}\in\mathbb{R}^{N}\) there are no issues with using this normalization.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## Case Study
### Analysis of Influencing Factors of Travel Time
#### 4.3.2 Effect of Holidays on Travel Time  
In this section, we compared travel time on holiday (21 February) with that on weekday (13 February). The study range is from Changsha toll station to Guanshan toll station on G5513. Travel time was analyzed from 9:00 to 20:00. Results of weekday and holiday are shown in Figure 7.  
From Figure 7, travel time on holiday is basically longer than that on weekday. This trend is more significant in the afternoon. For example, holiday travel time can double weekday travel time at 15:00 to 20:00.  
From above, we can observe that holidays have significant influence on travel time. On weekdays, traffic flow on G5513 is an average of 64,000 vehicles per day. In comparison, traffic flow will reach 93,000 vehicles per day on holidays (for example, Chinese Spring Festival), which is 1.6 times of the weekday.  
Also, a comparison of the weekday and holiday forecasting error evaluation criteria presented in the previous section indicates that the BP neural network has a greater prediction error than the weekday prediction results of the other models, probably due to the problem of building the network structure. However, the conventional SVM and the optimized SVM models have similar prediction error results. In the above analysis, there are large gaps in the holiday prediction results of the three different models, but whether it is on weekdays or holidays, the prediction accuracy of the optimized SVM model described in this paper is more accurate.
## 3 Construction of Mathematical Model
### CEI-Based Precision Measurements of Targets in Earth-Moon Space
#### 4.2.1 Atmospheric Delay Error  
The tropospheric delay correction equation is shown below:  
\[s_{i(space\to CEI)}^{\text{Troposphere}}=c_{dry}\cdot m_{dry}(\varphi)+c_{wet} \cdot m_{wet}(\varphi) \tag{24}\]  
where \(c_{dry}\) and \(c_{wet}\) are the zenith delay correction models for the dry and wet delay components, respectively; \(m_{dry}(\varphi)\) and \(m_{wet}(\varphi)\) are the mapping functions for the dry and wet delay components, respectively; and \(\varphi\) is the observed elevation angle of the CEI array to the non-cooperating spacecraft.  
The ionospheric delay correction equation is shown below:  
\[s_{i(space\to CEI)}^{\text{Ionosphere}}=[1-\frac{R_{e}\cos\varphi}{R_{e}+H}]^{ -\frac{1}{2}}\cdot\frac{40.3N_{total}}{c\cdot f_{iBD}} \tag{25}\]  
where, as mentioned above, \(f_{iBD}\) is the carrier frequency of the Beidou-3 satellite interstellar link, \(N_{total}\) is the total amount of electrons in the zenith direction, \(R_{e}\) is the radius of the Earth, and \(H\) is the height of the thin ionosphere.
## 3 Description of Data Integration Algorithm
\[C_{diff}=|C_{AIS}-C_{MRFT}| \tag{4}\]  
\[M_{c}=\left(1-\frac{C_{diff}}{180}\right)^{2}\text{, for }0\;\leq\;C_{diff}\;\leq\;180 \tag{5a}\] \[M_{c}=\left(1-\frac{C_{diff}}{360}\right)^{2}\text{, for }180\;\leq\;C_{diff}\;\leq\;360 \tag{5b}\]  
Figure 2.0 Step 2 of AIS-MRFT integration algorithm—Search for Candidates. If \(C_{AIS}\) and \(C_{MRFT}\) represent the vessel's course reported by AIS and HFSWR network respectively then \(C_{diff}\) is their difference. \(M_{c}\) represents the course matching coefficient for the candidate pair. Due to the nature of the course notation, two equations are a must for the definition of the course matching coefficient. At the end of this step, for every AIS track MFRT candidates are sorted by their matching factor value. During the fourth step, mutual integration affinities are cross-analyzed. Parameters which are taken into account are:  
* Current matching factor,
* Average value of matching factor during previous N integration periods and
* Number of candidates. At the end of this analysis, the old integration links may be broken or confirmed, while some new integration links may be created. Finally, the best possible integration pairs of data are chosen and processed further, while uniqueness of integration pairs (AIS MMSI, MRFT Id) is preserved. It is important to note that the integration link will not be created if the matching factor is less than 51%. In other words, the threshold level for the integration link creation or confirmation is 51%. At the final stage, all integrated MRFTs are marked with their corresponding MMSI. It is important to note that all MRFTs which cannot be integrated with any AIS data will be forwarded as they are. The same stands true for all AIS data which cannot be integrated with any MRFTs.
## 1 Related Studies
### Mapless Localization  
Accommodating a mapless situation might be valuable for secure localization operations by keeping information private. The pedestrian localization based on IMU and camera sensors easily produces the integral error so the user should carefully hold the receiver such as smartphone stationary and should not rotate it. This limitation restricts its practical usage. This study eliminates the need to restrict the users' behavior and the need to assume on the accurate signal propagation model.  
Crowd-sourcing has been a useful tool for indoor localization. Because the crowdsourced data are collected from a huge number of different users conveying various mobile devices, it has potential to help solving challenging problems such as heterogeneous hardware [38] and security issues [39]. In this study, we apply crowdsourced data to learn the hidden trajectory and extract the floor plan, to compensate for the absence of true map information. The trajectory learning algorithm originates in demonstration learning [40]. For the purpose of this study, trajectory learning is combined with semisupervised feature extraction in Section 1.1.  
Finally, as the position estimator, the particle filter is employed for two reasons: First, this filter can use the learned trajectories as a prior distribution. Second, in the particle filter framework, the likelihood function can be defined as the function referring to the relationship between the RSSI measurement sets and the positions. In this study, the likelihood is defined as the probabilistic model by the Gaussian process.
# Novel Designated Ownership Transfer with Grouping Proof
## 3 Security Analysis
### Message Integrity  
Assume an attacker can intercept all previously generated grouping proof messages transmitted between all communicating parties, and then attempts to modify the message to generate a bogus proof to fool the reader or verifier.  
In our protocol, each legitimate tag generates a different random number \(Nt_{i}^{q}\) in each session to compute the pieces of proof \(M_{j,i}^{q}\). Moreover, a verification code \(V_{j,i}^{q}\) is included in the response message to ensure the integrity of that message. The attacker might attempt to retrieve the shared key \(Kt_{i}^{q}\) from the verification code \(V_{j,i}^{q}\), but, owing to the assumption of OHF, no useful information will be obtained.
## 3 Main Results  
### Controller Design  
To facilitate the subsequent controller, the following coordinate transformation is introduced [24]:  
\[\xi=J(\psi)\
u \tag{16}\] \[\xi_{d}=J(\psi_{d})\
u_{d}\]  
Therefore, Equation (7) can be rewritten as:  
\[\left\{\begin{array}{rl}\dot{\eta}&=\xi\\ \dot{\xi}&=-J(\psi)\overline{M}^{-1}D(J^{T}(\psi)\xi)J^{T}(\psi)\xi+S(r)\xi\\ &+J(\psi)\overline{M}^{-1}\phi\tau_{c}+J(\psi)\overline{M}^{-1}\Omega\end{array}\right. \tag{17}\]  
where \(S(r)=\begin{bmatrix}0&-r&0\\ r&0&0\\ 0&0&0\end{bmatrix}\).  
Similarly, the desired reference trajectory is represented as follows:  
\[\left\{\begin{array}{rl}\dot{\eta}_{d}=\xi_{d}\\ \dot{\xi}_{d}=S(r_{d})\xi_{d}+J(\psi_{d})\overline{M}^{-1}(-D(\
u_{d})\
u_{d}+ \tau_{d})\\ \end{array}\right. \tag{18}\]  
where \(S(r_{d})=\begin{bmatrix}0&-r_{d}&0\\ r_{d}&0&0\\ 0&0&0\end{bmatrix}\).  
By defining the position tracking error \(\xi_{e}=\xi-\xi_{d}\) and combining (18) and (19), we can obtain:  
\[\begin{array}{rl}\dot{\eta}_{e}&=\xi_{e}\\ \dot{\xi}_{e}&=J(\psi)\overline{M}^{-1}\phi\tau_{c}-J(\psi)\overline{M}^{-1}D( J^{T}(\psi)\xi)J^{T}(\psi)\xi\\ &+S(r)\xi+J(\psi)\overline{M}^{-1}\Omega-S(r_{d})\xi_{d}-J(\psi_{d})\overline{ M}^{-1}(-D(\
u_{d})\
u_{d}+\tau_{d})\end{array} \tag{19}\]  
Then, the following fixed-time nonsingular sliding surface is defined as follows:  
\[\left\{\begin{array}{rl}s&=\xi_{e}+\varphi\\ \dot{\varphi}&=m_{1}sig^{a_{1}}(\eta_{e})+m_{2}sig^{a_{2}}(\eta_{e})+m_{3} sign(\eta_{e})\\ &+n_{1}sig^{b_{1}}(\xi_{e})+n_{2}sig^{b_{2}}(\xi_{e})+n_{3}sign(\xi_{e})\end{array}\right. \tag{20}\]  
where \(m_{1}>0\), \(m_{2}>0\), \(m_{3}>0\), \(n_{1}>0\), \(n_{2}>0\), \(n_{3}>0\), \(0<a_{1}<1\), \(a_{2}=2a_{1}/\left(a_{1}+1\right)\), \(b_{1}=2a_{1}+1\), and \(b_{2}=\left(2a_{1}+1\right)/\left(a_{1}+1\right)\).  
By taking the time derivative of \(s\), we obtain:  
\[\begin{array}{rl}\dot{s}&=\dot{\xi}_{e}+\dot{\varphi}\\ &=J(\psi)\overline{M}^{-1}\phi\tau_{c}-J(\psi)\overline{M}^{-1}D(J^{T}(\psi) \xi)J^{T}(\psi)\xi\\ &+S(r)\xi+J(\psi)\overline{M}^{-1}\Omega-\dot{\xi}_{d}+\dot{\varphi}\end{array} \tag{21}\]Then, a fixed-time reaching law is designed as follows:  
\[\dot{s}=-k_{1}sig^{p}(s)-k_{2}sig^{q}(s)-\frac{\hat{\beta}_{s}s}{2\sigma^{2}} \tag{22}\]  
\(\hat{B}_{s}\) denotes the estimation of \(B_{s}=B^{2}\), and the adaptive update law \(\hat{B}_{s}\) is designed as:  
\[\hat{B}_{s}=-\gamma_{1}\hat{B}_{s}+\gamma_{2}\frac{\|s\|^{2}}{2\sigma^{2}} \tag{23}\]  
where \(\gamma_{1}>0\) and \(\gamma_{2}>0\).  
In light of (22) and (23), the fixed-time adaptive sliding mode fault-tolerant controller is designed as:  
\[\begin{array}{ll}\tau_{c}&=\theta^{-1}(\overline{M}J^{-1}(\psi)(-k_{1}sig^{ p}(s)-k_{2}sig^{q}(s)-\frac{\hat{B}_{s}s}{2\sigma^{2}}-\dot{\varphi}-S(r) \xi\\ &-J(\psi)\overline{M}^{-1}\Omega)+\overline{D}(\
u)v+\overline{M}J^{-1}(\psi) \hat{\xi}_{d})\end{array} \tag{24}\]  
where \(k_{1}>0\), \(k_{2}>0\), \(0<p<1\), \(q>1\), and \(\sigma>0\).
## 1 Introduction
With increased national attention on ecological governance in the Yellow River Basin, experts and scholars have gradually expanded their research to encompass various provinces within the basin [22]. Existing research indicates that the vegetation cover in the Shanxi section of the Yellow River Basin has experienced a highly significant increase. The variations in the NDVI are influenced by climate changes, human activities, and the combined effects of both [23]. In the Henan section of the Yellow River Basin, there has been a significant improvement in vegetation cover from 2001 to 2020, with noticeable spatial disparities. Land-use type emerges as a primary influencing factor [24]. Through an examination of the NDVI changes across different land-use types in the Shanxi section of the Yellow River Basin, researcher [PERSON] has determined that forested areas exhibit superior vegetation cover compared to cultivated land and grasslands. Furthermore, there are significant spatial variations in the NDVI values among different land-use types [25]. In previous research, there has been a predominant focus on studying the long-term NDVI trends and influencing factors across the entire Yellow River Basin, with relatively limited attention given to ecological research within the mining areas of the Shanxi section of the Yellow River Basin. Shanxi province, known for its significant coal mining activities and numerous mining areas, serves as the backdrop for this study. Based on this, the study focuses on the Shanxi section of the Yellow River Basin and eight coal mining areas within this region. Given the multitude of mining activities within the study area that may potentially result in vegetation damage, the research aims to investigate the temporal changes in vegetation cover. By monitoring the dynamic changes in vegetation cover, the study intends to provide scientific guidance for the ecological restoration and management of vegetation in both the Shanxi section of the Yellow River Basin and the mining areas.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Designing an Energy-Conscious Algorithm
#### 5.1.2 Approximation of Arithmetic Operations  
The complex functions can also be approximated with simpler ones, having similar curves provided that the accuracy degradation is within an acceptable range [80]. Likewise, an operation involving multiplication or division by a particular constant can be avoided in AI operation because it linearly scales the data, and the classifier can adjust the hyperplane with the corresponding scale.  
The author of [80] exploited the fact that multiplication or division by a particular constant linearly scales the data. Removing such multiplication or division will not impact the classifier's output, as it will adjust the hyperplane with the corresponding scale. They also observed that the approximation of complex functions with simpler ones, having similar curves (e.g., natural log and square root), can also be used, provided that the degradation is within an acceptable range. On the other hand, the authors of [81] replaced conventional adders with approximate adders in order to reduce the power consumption by 21% at the cost of a 3% loss in accuracy.
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 3 AC-YOLO Target Detection Network
### Adaptive Memory Fusion Detection Head  
Given the significant size variation between insulators and their defective targets, conventional detection head structures that perform classification and regression simultaneously can lead to weight-sharing issues. These issues may cause the model to underperform in one task, impeding accurate differentiation between large and small targets. Taking into account the distinctive focuses of classification and regression tasks, we introduce the AMFDH (adaptive memory fusion detection head) in this paper. This approach utilizes distinct branches to execute these tasks, allowing for individual adjustment of the weights for each task. This ultimately enhances the model's convergence speed and overall performance. The architecture of the AMFDH is illustrated in Figure 7.  
Firstly, high semantic features of \(20\times 20\), \(40\times 40\), and \(80\times 80\) are standardized to 128 dimensions using a \(1\times 1\) convolutional layer. This process merges feature information and decreases the computational requirements. Following this, a pair of \(3\times 3\) convolutional layers are employed to handle the target classification and localization tasks for the insulators and their assorted defects independently and in parallel. This encourages feature decoupling and channel attention, allowing the model to better apprehend both global and local features of the insulator targets. This ultimately enhances the model's aptitude for differentiating objects from backgrounds and improves the model's detection accuracy and resilience. The target positioning branch also computes the confidence of the prediction box. Lastly, three \(1\times 1\) convolutional layers output the detection target category vector, detection box coordinate vector, and detection box confidence vector.  
Figure 6: AWDMSM with different embedding methods. (**a**) AWDMSM without residuals in the backbone; (**b**) AWDMSM with residuals in PANet.  
The conventional SiLU activation function, usually implemented in the decoupling detection head, typically has a fixed shape. As the shape of the activation function cannot adaptively adjust, it curtails the model's expressive capability and performance. To overcome this limitation, we replaced it with the Meta-ACON [21] activation function, which adaptively adjusts to the shapes of diverse input features. This improvement increases the robustness, the adaptability, and the capability to effectively handle noise and outliers within the input features.  
To showcase the superiority of the Meta-ACON activation function, we substituted it for various activation functions within the AMFDH. The results are displayed in Table 1.
## 1 Introduction
Yet, the difficulties that have arisen due to the inclusion of this new technology must be resolved before reaping the advantages of these paradigms. While NOMA provides various benefits, more users' increased information sensing capability due to this approach poses a greater security and privacy risk. To develop a strong, efficient, and successful system using this method, several security issues ranging from physical to application levels must be resolved. [34]. NOMA trumps conventional orthogonal multiple access (OMA) in several ways, including:  
1. It achieves superior spectral efficiency by serving multiple users simultaneously with the same frequency resource and mitigating interference through SIC. 2. It increases the number of simultaneously served users, thus supporting massive connectivity; and 3. It achieves superior spectral efficiency by doing multiple users simultaneously and with the same frequency resource, mitigating interference through SIC. Due to the nature of simultaneous transmission, a user does not need to send information within a predetermined time, resulting in shorter latency;
4. NOMA can preserve the user-fairness and diversified quality of service by flexible power regulation between strong and weak users [36]. Specifically, when more power is provided to a vulnerable user, NOMA provides greater cell-edge throughput, improving the user experience. To increase throughput, decrease latency, and conserve energy in RAN, NOMA approaches can be combined with MEC [37; 38; 39]. The authors presented the heterogeneous NOMA MEC network with numerous mobile edge server nodes providing computing for end devices [37]. The authors suggest an ideal offloading protocol that reduces latency and energy use. Similar findings were reported by [PERSON], [PERSON], [PERSON], and [PERSON] [38]. The authors use an optimisation technique to optimise the transmit power allocation of the users, the computing resource allocation of the MEC servers, and the time allocation. According to the findings, the system has a lower total delay than OMA systems. It has been established that the NOMA MEC-based system, created by [PERSON], [PERSON], [PERSON], and [PERSON] [39], increases system throughput and energy efficiency. In the model, two users cooperate with a MEC AP to communicate utilizing uplink NOMA. The NOMA MEC network performs better than other networks, as shown by the Monte-Carlo simulation with the message chain length, signal-to-noise ratio, and transmission time parameters.