esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Components of the Transcriptional Apparatus
The pair anti-sigma factor kinase RsbA/SCO0599-anti-sigma factor antagonist RsbB/SCO0598 [52; 53], and the anti-sigma factor antagonist RsbV/SCO7325 [52] are also present in this cluster. These proteins are thought to control the activity of sigma B/SCO0600, a sigma factor playing a role in osmoprotection and differentiation [53] not detected in our study. Cluster c includes 7 proteins more abundant in \(SL\) than in \(SC\) in both Pi conditions. This cluster includes sigma factors SCO1723 and SigN/SCO4034 that are involved in general stress response and have a positive impact on antibiotic production and morphological differentiation [54; 55] as well as two ECF sigma factors (SCO3715 and SCO7112). It also includes the elongation factor GreA-like/SC04945, the antitermination protein NusB/SCO1490, and the nucleic acid binding protein SCO2300. Cluster d includes 4 proteins more abundant in Pi proficiency than in Pi limitation in both strains but mainly at 60 h. These include the anti-sigma I factor/SCO3066/PrST that together with the anti-sigma I factor antagonist Arsl/SCO3067 regulates the activity of SigI/SCO3068 whose expression is induced by OsS [56]. This cluster also includes the sigma factor SCO6996, the oligoribonuclease OrnA/SCO2793 necessary for active vegetative growth and morphological differentiation [57], and an ATP-binding RNA helicase/SCO2000. These data revealed that 71% of the proteins of transcriptional apparatus detected, including eight ECF sigma factors, were more abundant in \(SC\) than in \(SL\) in both Pi conditions (16 proteins of cluster B) or mainly in Pi limitation (12 proteins of cluster A). Many of these proteins were shown to be involved in the resistance to various stresses such as cell wall stress (SigE, SigU), osmotic stress (sigH, sigB), and oxidative stress (SigR) and to have a negative impact on antibiotic production and morphological differentiation. This indicated that \(SC\), in contrast to \(SL\), \"suffers\" multiple stresses and this raises the question of the origin of these stresses in \(SC\) (see Discussion).
## 5 Discussion
### The Effect of Input Images on the Predicted Values of EFSDAF  
Like FSDAF, the minimum input images were used to predict the final Landsat-like images in EFSDAF. Hence, the fusion accuracy of EFSDAF was directly influenced by the input images [10; 19]. Particularly, the spatial information and temporal information of the input images would be fully utilized in EFSDAF. Figure 10 showed the differences between the predicted Landsat-like image of EFSDAF and input images in dataset 2. From Figure 10b-d, it can be seen that the land cover type has changed, and the information of land cover change can be captured by comparing Figure 10b with Figure 10c. However, there was an obvious difference between the predicted image in the yellow ellipse area of Figure 10b and the true image (Figure 10a). After comparing and analyzing the input images and the predicted image in Figure 10a,d, it can be concluded that the input MODIS image at \(T_{P}\) lacks the information of land cover change in the yellow ellipse from Figure 10c; therefore, the final predicted image in Figure 10a was different from the true image at \(T_{P}\) from Figure 10a. However, comparing the abrupt change area of MODIS images from \(T_{B}\) to \(T_{P}\) in Figure 10c,d, EFSDAF completely capture the spatial details in Figure 10b. In other words, EFSDAF can only accurately monitor the information of abrupt change in MODIS images. Hence, the quality of the input images is crucial to the fusion accuracy of EFSDAF.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Dataset 1**} & \multicolumn{6}{c}{**Dataset 2**} \\ \hline \hline
**Endmember** & **SVD** & **3** & **4** & **5** & **SVD** & **3** & **4** & **5** \\ \hline \multirow{3}{*}{AD} & Green & 0.0197 & 0.0198 & 0.0197 & 0.0198 & 0.0127 & 0.0128 & 0.0130 & 0.0130 \\  & Red & 0.0167 & 0.0168 & 0.0168 & 0.0168 & 0.0151 & 0.0152 & 0.0156 & 0.0156 \\  & NIR & 0.0379 & 0.0390 & 0.0381 & 0.0374 & 0.0227 & 0.0227 & 0.0230 & 0.0236 \\ \hline \multirow{3}{*}{RMSE} & Green & 0.0229 & 0.0229 & 0.0229 & 0.0230 & 0.0193 & 0.0193 & 0.0197 & 0.0197 \\  & Red & 0.0212 & 0.0213 & 0.0213 & 0.0213 & 0.0231 & 0.0232 & 0.0236 & 0.0238 \\  & NIR & 0.0504 & 0.0504 & 0.0496 & 0.0490 & 0.0326 & 0.0327 & 0.0331 & 0.0336 \\ \hline \multirow{3}{*}{CC} & Green & 0.845 & 0.845 & 0.846 & 0.843 & 0.672 & 0.674 & 0.656 & 0.657 \\  & Red & 0.915 & 0.914 & 0.914 & 0.914 & 0.674 & 0.675 & 0.659 & 0.650 \\  & NIR & 0.914 & 0.914 & 0.915 & 0.918 & 0.827 & 0.827 & 0.822 & 0.815 \\ \hline \multirow{3}{*}{SSIM} & Green & 0.804 & 0.804 & 0.805 & 0.802 & 0.653 & 0.645 & 0.625 & 0.626 \\  & Red & 0.890 & 0.889 & 0.889 & 0.889 & 0.648 & 0.647 & 0.635 & 0.618 \\ \cline{1-1}  & NIR & 0.902 & 0.902 & 0.906 & 0.908 & 0.815 & 0.815 & 0.810 & 0.800 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fusion precision of EFSDAF by using different endmember numbers in two datasets (NIR-red-green).
## 2 Detection Model and Mechanism  
### Traditional Detection System Based on Duffing Equation  
The Duffing equation is a typical nonlinear system and has a relatively simple expression and structure. The phase trajectories of this system show distinct characteristics when driven by different types of signals with different parameters, which can be used for weak signal detection. The expression of a traditional Duffing oscillator with a weak signal detection system is as follows:  
\[\left\{\begin{aligned} \dot{x}=\omega y\\ \dot{y}=\omega\big{(}-0.5y+x-x^{3}+F\cos(\omega t)+\\ A\cos(\omega_{0}t+\varphi_{0})+n(t))\end{aligned}\right. \tag{1}\]  
In a physical sense, \(x\) stands for displacement, \(t\) represents time, \(k=0.5\) is the damping coefficient, and \(-x+x^{3}\) is the spring's nonlinear restoring force. \(F\cos(\omega t)\) is the internal periodic driving force of the detection system with amplitude \(F\) and frequency \(\omega\); \(A\cos(\omega_{0}t\) + \(\varphi_{0})\) is the external input weak periodic signal we are interested in; \(n(t)\) is a stochastic disturbance, which we suppose to be a Gaussian white noise with distribution \(N(0,\sigma^{2})\).  
In Equation (1), we set \(\mid\!\Delta\omega\!\mid=\!\mid\!\omega_{0}-\omega\!\mid\) as the frequency difference between the oscillator's internal driving signal and the external input signal. The system has a critical threshold \(F_{\rm c}\approx 0.826\), which can be solved by the maximum Lyapunov exponential method. When \(F<F_{\rm c}\), the system is in a chaotic state, and the phase graph is irregular; when \(F>F_{\rm c}\), it is in a large-scale periodic state, and the phase trajectories show a stable periodic distribution. At the state transition critical point \(F=F_{\rm c}\), if the signal to be detected has the same frequency as that of the internal periodic driving force, namely, \(\Delta\omega=0\), the system state will suddenly change to regular large-scale periodic state; while if \(\Delta\omega\
eq 0\) and \(\mid\!\Delta\omega\!\mid/\omega\leq 0.03\), the system will be in an intermittent chaotic state, which changes regularly and intermittently between the large-scale periodic state and the chaotic state over time, with a period of \(T=2\pi/\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 4 Experiment
### Comparisons with State-of-the-Art Methods
#### 4.3.1 Comparisons on VeRi-776  
Table 1 compares the performance of the SOTA methods on VeRi-776. First, our SaGT-ST method demonstrated superior performance by incorporating spatial-temporal information, achieving the highest mAP of 86.59% and Rank-1 accuracy of 98.75%. Furthermore, among methods relying solely on visual features, our SaGT method outperformed most GF- and LF-based approaches, securing the fourth place. Specifically, SaGT combined local features to complement global feature learning compared to GF-based methods, while avoiding local feature misalignment issues associated with LF-based methods. However, it is important to note that without considering background interference, SaGT exhibited a relatively lower performance than MART on VeRi-776. Moreover, SaGT exhibited comparatively lower performance than DFR and MsKAT. This difference can be attributed to the integration of attention mechanisms that effectively combine multiple features in DFR and MsKAT. Obviously, the incorporation of spatial-temporal cues significantly enhanced the performance of vehicle re-identification. For example, both DFR-ST and DPGM-ST achieved superior performance compared to their counterparts DFR and DPGM which only adopt visual features. It is noteworthy that, since only the VeRi-776 dataset contained timestamp and camera position information, similar to DGPM-ST and DFR-ST, we also only evaluated our SaGT-ST method on VeRi-776.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 3 Interference Alignment in Cognitive Radio: First Paradigm
### Applications  
Given the volume of work that has gone into this research question, it is expected that it will find its applications in practical network scenarios. One of such applications is in Femtocell deployments. It is known that femtocells may coexist with macrocells in the same band and significantly improve the spatial reuse (SR) of spectrum. As such, the work in ref. [106] tries to avoid high interference to the scheduled macrocell users (which in this case are the PUs) from the femtocells acting as the SUs. The femtocells, just like typical SUs will sense and access the channel opportunistically. Using massive MIMO [107] to form guard zones, this work proposes spatially aligning the guard zones so that more SR opportunities can be provided to the femtocells. By proposing a scheme for user scheduling, the guard zones are aligned by taking into consideration the geographic locations for massive MIMO use scheduling as well as the interfering femtocell information. Simulations results show significant improvement for femtocell throughput without degrading the macrocell performance.  
Another application of this paradigm can also be found in refs. [57; 108; 109], which proposes to employ IA for aligning the signals from the macrocell (MU)/SU that tend to increase interference at multiple femtocell base stations (FBS)/PU which serves as an adequate interference managementtechnique from base station to user equipment. The proposed solution includes a selective IA (SIA) algorithm that comprises of a cautious selection of the macrocell interferers to align at several FBSs, and identify the subspace where cross-tier interference signals can be aligned. Subsequently, a distributed technique used for identification of the precoders required at the selected interferers is employed. The interference from FUs which is the intra-tier interference, is then overseen by making use of MMSE interference suppression [18; 110].
## 3 Results and Discussion
### Validation of PDMS-Based Microdevices
The same protocol as before was applied for the adsorption of miR-20a and miR-222 on Chip A, obtaining a Ct of \(31\pm 2\) and of \(32\pm 4\), respectively. These data were compared with those obtained by using a PDMS microdevice already validated for capturing circulating microRNAs [19; 20]. This microdevice acts in this context as positive control. The Ct resulting from the capture of miR-20a and miR-222 from the same human plasma sample were \(32\pm 0.1\) and \(34\pm 0.1\), respectively. Therefore, the results of the spiral-shaped microdevice are not only in-line with the positive control but even better. As a conclusion, the spiral-shaped microdevice was demonstrated as a good candidate for the easy detection of microRNAs biomarkers from biological samples, even if a further implementation, especially in terms of fluids manipulation is necessary. Still, the device here presented could be an initial platform for the realization of compact, rapid, non-invasive, easy-to-use and economic tools to be employed in real-settings. Figure 9: RT-qPCR results expressed as threshold cycles (Ct) measured when different amounts of synthetic microRNA were spiked in water (Panel (**a**)) or in blood plasma (Panel (**b**)). The absorbed microRNA was reverse transcribed directly on-chip, while the cDNA products were amplified in standard conditions. A dotted line, from a logarithmic fit of all data is also shown.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 2 Fundamentals of Array Processing  
### Signal Model  
Let us consider an antenna array formed by \(L\) elements receiving one contribution directly from the satellite constellation, i.e., the line-of-sight signal (LOSS), along with \(I\) undesired signals, i.e., the interference. The combination of the signals perceived by the antennas can be arranged into an \(L\times 1\) vector \(\mathbf{x}[n]\), resulting in the complex baseband signal depicted in Equation (1).  
\[\mathbf{x}[n]=\mathbf{a}(\theta_{0})s[n]+\sum_{j=1}^{I}\mathbf{a}(\theta_{j})i_{j}[n]+\bm {n}[n] \tag{1}\]  
From the expression in Equation (1), \(s[n]\) depicts the desired LOSS, \(i_{j}[n]\) the \(j\)-th interference, and \(\mathbf{n}[n]\) the noise experienced at each channel. The term \(\mathbf{a}(\theta)\in\mathbb{C}^{L\times 1}\) describes the spatial signature of the contribution arriving from direction \(\theta\) as perceived by the \(L\) elements in the array, where the subscript \({}_{0}\) is used to refer to the LOSS and \({}_{j}\) to the different interference.  
For the sake of simplicity, under the assumption that a single interference is impinging the array along with the desired GNSS signals, the signal model becomes the one presented in Equation (2).  
\[\mathbf{x}[n]=\mathbf{a}(\theta_{0})s[n]+\mathbf{a}(\theta_{j})i_{j}[n]+\mathbf{n}[n] \tag{2}\]  
Lastly, mind that the previous notation describes the signal before the despreading process, i.e., before the correlation with the local code is performed, and thus the GNSS signal, although present in the expressions above, is not visible for the receiver at this stage, since it is well-buried below the noise floor. Following this reasoning, and under the assumption that the power of the interference is noticeable, one can benefit from this property to simplify the expression in Equation (2) into the one in Equation (3), which will be very useful when applying the beamforming techniques presented.  
\[\mathbf{x}[n]=\mathbf{a}(\theta_{0})s[n]+\mathbf{a}(\theta_{j})i_{j}[n]+\mathbf{n}[n]\approx \mathbf{a}(\theta_{j})i_{j}[n]+\mathbf{n}[n] \tag{3}\]
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme
### Clustering
When the similarity of two documents is indicated as a perfect score of four points, the distance value of these two documents can be expressed as \(1-(4/4)=0\). Since it represents the distance between two documents, a higher similarity results in a smaller distance value. The range of the distance value is between zero and one. Contrary to when calculating similarity, a distance value closer to zero indicates higher similarity between the two documents. The similarity matrix example from Table 4, which utilizes the sim2 diss formula, was represented in the form of a distance matrix, as summarized in Table 5. After calculating the sim2 diss formula for all similarities, it was represented in matrix form. Documents Jang Jun-hyuk_0 and Jang Jun-hyuk_1 had a distance value of zero, indicating similarity. The values in the white cells represent the distance matrix values calculated using the sim2 diss formula, while the values in the yellow cells are set to one, as they compare the same documents. In the distance matrix phase, names were disambiguated based on the distance values obtained from the document similarities. The _AgglomerativeClustering_ model in Python was used. When performing HAC, the number of formed clusters is uncertain. Hence, the hyperparameter value _n_clusters_ determining the number of clusters was set to none. As previously described, the pre-calculated distance was used to represent the distance matrix; therefore, affinity was set to pre-computed. Additionally, dist \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[PERSON]\_0** & **[PERSON]\_1** & **[PERSON]\_2** & **[PERSON]\_3** \\ \hline [PERSON]\_0 & 0 & 4 & 1.9 & 0.6 \\ [PERSON]\_1 & 4 & 0 & 2.6 & 0.8 \\ [PERSON]\_2 & 1.9 & 2.6 & 0 & 0.6 \\ [PERSON]\_3 & 0.6 & 0.8 & 0.6 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Similarity matrix example. Figure 6: Example of the creation of a similarity matrix. such as single, complete, and average linkages, were used. After an intrinsic evaluation, the most suitable linkage method was selected. Finally, the _distance_threshold_ value, which sets the stopping criterion for the clustering process, was also determined through intrinsic performance evaluation to determine the most suitable value. Figure 7 shows an example of a dendrogram, using which the results of the HAC were visualized. Figure 6 shows the grouping of clusters. The red dotted line in the figure represents the stopping criterion, _distance_threshold_. Clusters grouped by the stopping criterion are marked in orange, while those not grouped are marked in blue. Clusters divided by the stopping criterion indicate individuals with the same name. As an example from Table 5, when setting the stopping criterion to 0.4, the documents \"[PERSON]_0\", \"[PERSON]_1\", and \"[PERSON]_2\" were grouped into one cluster, while \"[PERSON]_3\" was represented in a different cluster. In other words, two authors were distinguished.
## 4 Experimental Results and Discussion
### Discussion  
The results of the four experiments showed that the two algorithms were effective and correct. Regardless of the various numbers of inputs in each experiment, the main influence in the model was the underlying MCDM. Based on the conducted experiments, it is evident that each selected underlying prioritization method had a different structure for ranking and evaluating the SP and resource criteria. The AHP used in Experiments 1 and 2 used a hierarchical structure with fewer pairwise comparisons than the ANP, which was used in Experiment 3. However, the ANP allowed decision makers to represent more real problems by considering the interdependence among network elements. The time consumed by the ANP due to its complex calculations makes it less commonly used than the AHP and other ranking MCDM methods. The Super Decision software is an automated tool that can be used to facilitate calculations in both the AHP and ANP. In Experiment 4, the BWM was used as an underlying prioritization method, and the main concept of this method is to overcome the limitations of both the AHP and ANP by allowing decision makers to solve decision problems with less time and effort. The BWM depends on determining the best and worst criteria and then comparing these two over the other criteria within the decision problem with no need to evaluate each criterion with respect to all others. The maximum number of pairwise comparisons needed in the BWM is \(2n-3\) comparisons, while the AHP, for example, requires \(n(n-1)/2\) comparisons. In this way, the number of pairwise comparisons is minimized, which makes the BWM more suitable for use with large numbers of criteria and alternatives.  
It is important to mention that the model lacks the ability to infer useful information and prioritize the SPs, customers, and tasks based on historical data. Instead, the model was only tested and validated by creating sets of inputs; this could be the main threat to its validity. Therefore, validating the model with more real datasets that incorporate historical data and enabling the model to process these data to prioritize SPs, customers, and tasks can lead to an enhancement of the efficiency and the effectiveness of the model.
## 2 Theoretical Approach  
### Using Dynamic Features to Detect Damage  
Considering a finite structure of degrees of freedom, the structure's partial vibration differential equation has the following form [49]:  
\[M\ddot{u}(t)+Ku(t)=0 \tag{1}\]  
for which:  
\(M\) = structure's mass matrix;  
\(K\) = structure's stiffness matrix.  
The equations of the vibration of the masses have the form:  
\[u_{i}(t)=A_{i}\sin(\omega t+\varphi_{i}) \tag{2}\]  
\[\ddot{u}_{i}(t)=-\omega^{2}A_{i}\sin(\omega t+\varphi_{i}) \tag{3}\]  
The equations and components can then be substituted into Equation (1):  
\[\begin{bmatrix}m_{1}&&\\ &m_{2}&&\\ &&\dots&\\ &&&m_{n}\end{bmatrix}\begin{pmatrix}A_{1}\\ A_{2}\\ \dots\\ A_{n}\end{pmatrix}+\begin{bmatrix}k_{11}&k_{12}&\dots&k_{1n}\\ k_{21}&k_{22}&\dots&k_{2n}\\ \dots&\dots&\dots&\dots\\ k_{n1}&k_{n2}&\dots&k_{nn}\end{bmatrix}\begin{bmatrix}A_{1}\\ A_{2}\\ \dots\\ A_{n}\end{bmatrix}=0 \tag{4}\]  
or  
\[\begin{bmatrix}\begin{bmatrix}k_{11}&k_{12}&\dots&k_{1n}\\ k_{21}&k_{22}&\dots&k_{2n}\\ \dots&\dots&\dots&\dots\\ k_{n1}&k_{n2}&\dots&k_{nn}\end{bmatrix}-\omega^{2}\begin{bmatrix}m_{1}&&\\ &m_{2}&&\\ &&\dots&\\ &&m_{n}\end{bmatrix}\end{bmatrix}\begin{bmatrix}1\\ \varphi_{2}\\ \dots\\ \varphi_{n}\end{bmatrix}=0 \tag{5}\]  
To obtain the equation for determining the specific type of vibration, this can then be simplified:  
\[\begin{bmatrix}\mathbf{K}-\omega_{j}^{2}\mathbf{M}\end{bmatrix}\varphi_{j}=0 \tag{6}\]  
Natural frequency (f = \(\omega/2\pi\)) and natural vibration mode shape (\(\varphi_{i}\)), determined through Equation (5) or Equation (6), are very important features of the structure. The dynamic characteristic is unique to a particular structure. With Equations (1)-(6), the modal properties of the structure (i.e., the frequencies and mode shapes, damping ratio, among others) at both an intact and damaged state are extracted.
## 3 Proposed Solution to Predict the AoA
### System Model and Mathematical Formulation
#### 3.3.4 Predictive AoA Models (DE-ACSW-R)
Otherwise, the system waits for the O-DU's SVRoutput or triggers fallback to MUSIC, as described in the section Support Vector Regression (SVR) for Non-Linear AoA Prediction and Section 3.3.6. LR predictions serve as the first-pass estimation mechanism in the dual-layered prediction structure of DE-ACSW-R. At each time step, the O-RU locally computes \(\hat{\theta}_{LR}(t+\Delta)\). Furthermore, forward it to the following:  
* The enhanced MUSIC module, which constrains its angular search window using \(\hat{\theta}_{LR}\). * The confidence-weighted fusion logic blends it with SVR-based predictions for robust output. In high-confidence regimes, LR alone suffices for both prediction and beam steering. In lower-confidence regimes, the LR prediction still provides directional prior knowledge to constrain subsequent spectrum computation, forming the backbone of latency-aware adaptive beamforming in Split 7-2x O-RAN systems.