esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 3 Results and Experiments
### Evaluation of the Models on the Farm Dataset
However, its precision is 64%, implying that only 64% of its positive classifications were correct. This translates to a recall rate of 72%, meaning that 72% of true positive instances were documented. Its F1_score of 67% shows a close balance of accuracy and recall, similar to VGG16's performance. Yolo v6 and Yolo v7 performed, comparatively, much better than the four models above. Yolo V8 outperforms the competition with 99% accuracy, almost flawlessly guessing outcomes. Its 98% precision implies that virtually all events classified as positive were genuine positives. Based on a 98% recall rate, Yolo V7 detected 98% of genuine positive instances. Furthermore, Yolo V8's top-tier performance is supported by an F1_score of 98%, demonstrating an excellent mix of accuracy and recall. Yolo V8 earns the top spot, with near-perfect ratings in all categories. Although Inception and Xception have the same accuracy and recall rates, Xception is less exact. Both VGG16 and AlexNet exhibit lower accuracy values when compared to the leading models, resulting in somewhat lower F1 ratings. Nonetheless, model selection should be firmly matched with the application's specific objectives, especially in terms of false positives and false negatives.
## 4 Discussion  
_MIPAS_ investigated the relation between PAS and MI in a sample of poststroke participants. Corticuscular PAS is a model of associative plasticity. It associates a peripheral muscle electrical stimulation to a cortical TMS stimulation administered repetitively at a constant interval of time. In its initial description [6], PAS was designed as a single ES to the median nerve followed by a TMS pulse over the abductor pollicis brevis (APB) muscle cortical representation. Long-term potentiation or depression phenomenon (LTP or LTD, depending on the ISI used) in relation to a mechanism of spatial summation has been proposed to explain the PAS mechanism of action [34]. To optimize its efficiency, some authors have associated a temporal facilitation component to PAS, by additionally administering a train of stimulation to the median nerve [7]. However, the functional prognostic of poststroke UE is mainly determined by the recovery of wrist and finger active  
Figure 3: Mean active range of motion changes (\(\Delta\)aROM) over time for the three interventions. \(\Delta\)aROM values, in degrees (\({}^{\circ}\)), adjusted to prestimulation values across different times of assessment (TOA at 5, 15, and 30 min postintervention) for the three types of interventions PAS/MI (green squares), PAS (blue squares), and ShamPAS/MI (orange squares). The three interventions induced increases in participants’ active paretic wrist extension amplitudes at different TOA. Intraintervention significance is indicated by asterisks (\({}^{\circ}\)\(p\) < 0.05; \({}^{***}\)\(p\) < 0.01; \({}^{***}\)\(p\) < 0.001).  
extension movements [35]. In an effort to be more suitable for stroke rehabilitation, we thus developed and tested a PAS intervention targeting the ECR muscle [8; 9]. Despite encouraging results obtained with single PAS intervention [8; 9], the result of an RCT trial published by our group [18], using daily PAS stimulation over five consecutive days during stroke rehabilitation against Sham, was negative. The _MIPAS_ protocol was therefore designed to investigate the potential of combining PAS and MI interventions to increase the effect size of each intervention administered separately. Indeed, more recently, it was shown that a short training combining overt movements, MI, and TMS increased post-training MEP amplitude in healthy individuals [36]. The observed results and the associated limitations are discussed in the appropriate sections below.
# Understanding Consumer Acceptance for Blockchain-Based Digital Payment Systems in Bhutan
## 2 Literature Review
### Prior Studies on Blockchain-Based Digital Payment Systems  
Studies have explored various aspects of blockchain integration into financial services. The key findings and methodologies of these studies are summarized in Table 1 below.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Methodology** & **Theoretical Framework** & **Key Findings** & **Limitations** \\ \hline
[78] & An empirical study was carried out through a survey and analyzed using PLS-SEM. & UTAUT2 & The study confirms a strong measurement model, with behavioral intention significantly driving cryptocurrency usage. & Regulatory support and experience were identified as key factors but not empirically tested. \\ \hline
[79] & An empirical study was carried out through a survey and analyzed using PLS-SEM. & UTAUT2 & Trust, transaction transparency, volatility, and facilitating conditions positively influence adoption through intention to use. & Does not address liquidity shortages in Malaysian banks, price volatility, acceptability, transaction factors, or pandemic effects, and relies solely on cross-sectional data. \\ \hline
[35] & Systematic literature review & UTAUT & Security, privacy, transparency, and regulation as key factors for trust, while performance expectancy, effort expectancy, social influence, and facilitating conditions drive acceptance. & Does not extensively explore blockchain adoption in digital payment systems from the user’s perspective. \\ \hline
[80] & An empirical study was carried out through a survey. & UTAUT & Personal innovativeness, trust, security, effort expectancy, performance expectancy, and social influence significantly influence blockchain adoption. & Lack of empirical validation for real-world blockchain adoption, and the unsupported role of technology awareness. \\ \hline
[81] & An empirical study was carried out through a survey and analyzed using PLS-SEM. & UTAUT & Performance expectancy, effort influence, personal innovativeness, and online reviews as key drivers of adoption. & Limited by the lack of existing academic work on blockchain adoption factors, the absence of empirical validation for the proposed model \\ \hline
[82] & An empirical study was carried out through a survey and analyzed using PLS-SEM. & UTAUT & Performance expectancy, social influence, personal innovativeness, and online reviews as key drivers of adoption. & Further development of technology adoption models is needed to include factors like age and gender and exploration of cultural and pandemic-related influences. \\ \hline
[83] & An empirical study was carried out through a survey and analyzed using PLS-SEM. & UTAUT & Performance expectancy, effort expectancy, hedonic motivation, perceived trust, and facilitating conditions significantly influence blockchain adoption for mobile banking. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prior studies on blockchain-based digital payment systems.
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 2 Basics for SDOF and MDOF Systems  
In the dynamic analysis of structures, from a practical point of view, a real structure is defined as a system with infinite degrees of freedom that can be modeled as a discrete system with SDOF or MDOF in a finite element approach. In many cases, these simple models include complex information of the real structure and are able to simulate the behavior of real structure with a good level of accuracy and high calculation efficiency. As an example, in blast-resistant design of structures, the basis of current books and codes and also simplified engineering tools have been established based on equivalent SDOF models of real structures [57; 58; 59].
## 3 Results
### Drought Impact on Phenology and Green Biomass Production
#### 3.4.1 Changes in Forest Canopy Water Content--4 nNDII7 PC
Analyses performed for the 4 nNDII7 drought impact class 3 spatial footprint. Categories plotted in transparent comprise less than 50 observations. Abbreviations are explained in Figure 3.  
Figure 3: Year-to-year variability observed within the drought impact classes 1 (in green) and 3 (in red) footprints representing respectively, negative and positive correlation to the 4 nNDII7 PC: (**a**) CF (Cyclic Fraction); (**b**) GPP (Gross Primary Production); (**c**) SBD (Season Begin Day) and (**d**) SL (Season Length). Significance of class-dependent year-to-year differences was assessed with ANOVA model and is given in Table 2.  
A comparison among forest types was not performed due to insufficient representation of broadleaved and mixed woodland (Table 2). Role of inclination in governing forest status under drought conditions was limited and is briefly discussed in Appendix A.1. Forest rendering elevated canopy water content, associated with the 4 nNDII7 drought impact class 1, revealed higher green biomass production as well as on average 28.9 days longer vegetation season (Figure 3b,d, respectively) comparing with the region depicted by class 3 response. CF showed here an overall increasing tendency with two strong upturns in 2003 and 2008-2009 (Figure 3a). A GPP increment spurred in 2003 lasted only until 2005 (Figure 3b). No clear change trend was identified for SBD, which was characterized by intense year-to-year variability with the most abrupt shift towards the earlier vegetation onset in 2003 and 2011 (Figure 3c). Season length became greatly prolonged in 2003 and despite inter-annual variability, remained extended throughout the whole time of the analysis (Figure 3d). Further analyses revealed influence of biophysical factors on forest phenology and green biomass productivity within the 4 nNDII7 class 1 areas (Figure 5). Stands growing above 1400 m a.s.l. showed here not only the highest overall seasonal production, but also the strongest CF and GPP gains in 2003. The latter were coupled with sooner vegetation onset and longer SL. A gradual 2003-2010 GPP decrease was observed above 1400 m a.s.l. and contrasted with response of stands located at lower altitudes. Disparity in vegetation onset was the biggest for 2004 and 2006-2010.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 3 Achievable Rates, Outage Probabilities and Bit-Error-Rates Analysis
### Transmission between RS and UE
#### 3.2.2 Application of DF Coding Scheme  
During the first time slot, the TX transmits a superimposed signal according to NOMA principles to the relay. The achievable rates (\(R_{x_{1}}^{\mathrm{RS}}\) and \(R_{x_{2}}^{\mathrm{RS}}\)) for the relay to decode \(x_{1}\) and \(x_{2}\) are given [109] by  
\[R_{x_{1}}^{\mathrm{RS}}=B\mathrm{log}_{2}\bigg{(}1+\frac{\alpha_{1}\gamma| \mathbf{h}_{TX}|^{2}}{\alpha_{2}\gamma|\mathbf{h}_{TX}|^{2}+1}\bigg{)} \tag{22}\]  
and  
\[R_{x_{2}}^{\mathrm{RS}}=B\mathrm{log}_{2}\Big{(}1+\alpha_{2}\gamma|\mathbf{h}_ {TX}|^{2}\Big{)} \tag{23}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are power allocation factors associated with \(x_{1}\) and \(x_{2}\), respectively. UE\({}_{1}\)'s signal \(x_{1}\) and UE\({}_{2}\)'s signal \(x_{2}\) are the vector elements of \(\mathbf{x}\).  
Given that the relay can decode the signal from TX of the two NOMA users, the observable signal (\(y_{k}^{\mathrm{DF}}\)) at UE\({}_{1}\) and UE\({}_{2}\) in the second slot may be stated as  
\[y_{k}^{\mathrm{DF}}=\mathbf{h}_{R,k}\sqrt{\beta_{k}}\mathbf{x}+\eta_{k} \tag{24}\]  
where \(k=\mathrm{UE}\), \(\beta_{k}>0\) denotes the power allocation factor of the \(k\)th UE. Here, \(\beta_{1}+\beta_{2}=1\). Following the principle of NOMA scheme, the decoding order at the relay is \(x_{1}\to x_{2}\). The relay first detects \(x_{1}\) by treating \(x_{2}\) as noise. It then subtracts \(x_{1}\) from the \(y_{RS}\) in (16) to identify \(x_{2}\). By employing SIC, the SINR (\(\gamma_{1,2}^{\mathrm{DF}}\)) at UE\({}_{1}\) to detect \(x_{2}\) is given [109] by  
\[\gamma_{1,2}^{\mathrm{DF}}=\frac{\beta_{2}\gamma|\mathbf{h}_{R,1}|^{2}}{\beta _{1}\gamma|\mathbf{h}_{R,1}|^{2}+1} \tag{25}\]  
The SINR (\(\gamma_{R,1}^{\mathrm{DF}}\)) for UE\({}_{1}\) to detect its own signal is  
\[\gamma_{R,1}^{\mathrm{DF}}=\beta_{1}\gamma|\mathbf{h}_{R,1}|^{2} \tag{26}\]Similarly, the SINR (\(\gamma_{R,2}^{\rm DF}\)) at UE\({}_{2}\) is given [109] by  
\[\gamma_{R,2}^{\rm DF}=\frac{\beta_{2}\gamma|\mathbf{h}_{R,2}|^{2}}{\beta_{1}\gamma |\mathbf{h}_{R,2}|^{2}+1} \tag{27}\]
## 3 Results
### Typography and Typesetting
#### 3.6.3 Perceptual Span in Reading  
The perceptual span, traditionally thought to extend 14-15 characters rightward and 3-4 characters leftward of fixation, has been re-evaluated in a study involving 36 native English speakers aged 18-25 with normal or corrected vision [49]. Using 80 sentences displayed in Courier font per session, the study applied systematic letter manipulations up to four words left of fixation, replacing letters with visually similar or dissimilar alternatives (e.g., \"f\" - \"t\" vs. \"f\" - \"g\"). Results showed that the perceptual span extends further left than previously assumed, encompassing up to two words (11 characters) left of fixation. Reading times increased by 1042.68 ms (n \(-\) 1) and 188.97 ms (n \(-\) 2), fixation durations lengthened by 13.83 ms (n \(-\) 1) and 3.93 ms (n \(-\) 2), and fixations rose by 2.89 (n \(-\) 1) and 0.52 (n \(-\) 2), while comprehension accuracy remained above 96%. These findings highlight the influence of typography on reading fluency and challenge existing models like E-Z Reader and SWIFT to account for expanded leftward perceptual spans.  
Supporting these conclusions, the study by [PERSON] et al. found that the perceptual span extends 10-14 characters to the right of fixation, with word identification limited to 8 characters for both 60 hearing and 42 deaf readers [47]. Deaf readers demonstrated significantly faster reading rates (320.54 words-per-minute (WPM)) than hearing readers (253.71 WPM, \(p<0.001\)), emphasising the importance of optimising visual spans for readability.  
Another study explored how shifting word characters left or right affects fixation duration and gaze direction [50]. The results revealed a tendency to skim the latter half of words after reading the initial portion. Similarly, context integration studies showed that antecedents in text increased go-past times at sentence-initial nouns (t(5749) = 2.20, \(p=0.3\)), while spillover words were read faster (t(5729) = \(-\)3.96, \(p<0.01\)), reflecting smoother integration. Typographical cues like bold or italicised antecedents improved accessibility and reduced initial reading times by making key elements more visually salient [51].  
In another study, pupil dilation was measured while reading at varying speeds using RSVP (Rapid Serial Visual Presentation), where words are displayed sequentially on a screen [52]. Grey backgrounds produced linear increases in pupil dilation as text speed rose, indicating no specific background effect on workload. However, a linear relationship between pupil dilation and perceived workload was only distinguishable at speeds between 200 and 450 WPM. The study, conducted using VR headsets, is limited by participant unfamiliarity with RSVP and potential differences in measurement accuracy across eye-tracking devices.
## 6 Simulation Test and Results Analysis
### Experimental Test of IBOA
#### 6.1.3 Non-Parametric Test  
In order to better test the performance of the algorithm, a series of test methods were used to evaluate the advantages and disadvantages of the algorithm. Among them, the statistical method is considered to be an effective method, including a parametric test and a non-parametric test. For the comparison of various algorithms, the hypothesis estimation method is not considered to be conducive to the evaluation of the advantages and disadvantages of the algorithm. The newly developed algorithm's statistical performance was verified by employing a non-parametric statistical test.  
In this non-parametric test, each algorithm is executed for 30 independent runs, with 20 populations, for 50 iterations. A non-parametric Wilcoxon test was used to evaluate the differences between paired samples.The \(p\)-values achieved in the test are shown in Table 4, indicating that the IBOA algorithm is mathematically significantly superior.  
Unlike the Wilcoxon rank test, the Friedman test is used to obtain a ranking for multiple algorithms. For the above test functions, IBOA's performance was better than the other algorithms. This non-parametric test is used to rank the algorithms. According to the box diagram shown in Figure 6, the test results show that the mean rank for IBOA is the minimum among all the algorithms.
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
###### Abstract  
In this paper, we present a tiny networked mobile platform, termed Tiny-Web-Thing (_T-Wing_), which allows the sharing of data-intensive content among objects in cyber physical systems. The object includes mobile platforms like a smartphone, and Internet of Things (IoT) platforms for Human-to-Human (H2H), Human-to-Machine (H2M), Machine-to-Human (M2H), and Machine-to-Machine (M2M) communications. _T-Wing_ makes it possible to host rich web content directly on their objects, which nearby objects can access instantaneously. Using a new mechanism that allows the Wi-Fi interface of the object to be turned on purely on-demand, _T-Wing_ achieves very high energy efficiency. We have implemented _T-Wing_ on an embedded board, and present evaluation results from our testbed. From the evaluation result of _T-Wing_, we compare our system against alternative approaches to implement this functionality using only the cellular or Wi-Fi (but not both), and show that in typical usage, _T-Wing_ consumes less than 15\(\times\) the energy and is faster by an order of magnitude.  
energy-efficiency; mobile communication; proximity sensing; mobile platform; content hosting +
Footnote †: journal: Sensors
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 2 Related Work
### Hybrid Approaches for Market Prediction  
Recent studies have explored hybrid approaches that combine various data sources and models to improve market prediction. [PERSON] et al. (2022) [PERSON] et al. (2022) introduced a multi-modal framework using Non-Stationary Markov Chains (NMCs), BERT for sentiment analysis, and LSTM to understand temporal dependencies better. Likewise, [PERSON] and [PERSON] (2023) [PERSON] et al. (2023) proposed a machine learning method leveraging order book details to highlight how detailed datasets can enhance predictive accuracy. [PERSON] and [PERSON] (2024) [PERSON] and [PERSON] (2024) demonstrated the effectiveness of hybrid frameworks by integrating VIX, GARCH, and LSTM to forecast S&P 500 volatility, showcasing how ensemble methods can improve predictive precision in financial markets.  
[PERSON] et al. (2024) [PERSON] et al. (2024) [PERSON] et al. (2024) expanded the functionality of LSTM networks by incorporating genetic algorithms to optimize hyperparameters, resulting in significantly improved performance for forecasting unemployment rates. This method underscores the benefits of combining optimization techniques with recurrent models to enhance accuracy in time series prediction. Similarly, [PERSON] and [PERSON] (2020) [PERSON] and [PERSON] (2020) demonstrated the effectiveness of combining CEEMDAN and LSTM with exogenous features to improve financial time series forecasting, further highlighting the versatility of LSTM-based approaches.  
[PERSON] et al. (2020) [PERSON] et al. (2020) proposed an innovative hybrid approach that combined Data Envelopment Analysis (DEA) with machine learning techniques to assess eco-efficiency. Their study highlighted the effectiveness of integrating optimization models with data-driven approaches for identifying patterns and enhancing decision-making in resource-intensive industries. Following this work, [PERSON] et al. (2021) [PERSON] et al. (2021) explored various optimization strategies such as DEA to boost productivity and sustainability using hybrid machine learning models.  
In the meantime, [PERSON] and colleagues (2019) [PERSON] and colleagues (2019) highlighted the importance of hyperparameter optimization by utilizing Genetic Algorithms (GAs) to enhance Long Short-Term Memory (LSTM) model performance. This approach allowed for automated selection of key parameters, surpassing traditional methods in effectiveness.  
Despite significant advancements in these models, they frequently fall short of providing a unified method for integrating various dimensions of financial data. This includes essential elements like ontological classifications and macroeconomic indicators, which are critical for navigating dynamic market conditions.
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## 3 A Three Step MUSIC-Taylor-Based Localization Approach
### Step 2, Taylor-Series Based Enhancement
The first-order partial derivatives of the object function in (13) with respect to \(\Delta\theta\), \(\Delta\phi\), and \(\Delta\tau\) can be expressed as:  
\[F^{\prime}_{\Delta\theta}(\mathbf{\Delta})= \frac{\partial}{\partial\Delta\theta}\Big{(}S(\tilde{\mathbf{p}}_{0},\mathbf{\Delta}_{0})^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}S(\tilde{\mathbf{p}}_{0},\mathbf{\Delta}_{0})\Big{)} \tag{14}\] \[= 2\big{(}\mathbf{A}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{A}\Delta \theta+\mathbf{s}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{A}+\mathbf{T}^{\mathrm{H}} \mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{A}\Delta\tau+\mathbf{E}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{ \mathrm{H}}\mathbf{A}\Delta\phi\big{)},\] \[F^{\prime}_{\Delta\phi}(\mathbf{\Delta})= \frac{\partial}{\partial\Delta\phi}\Big{(}S(\tilde{\mathbf{p}}_{0}, \mathbf{\Delta}_{0})^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}S(\tilde{\mathbf{p}}_{0}, \mathbf{\Delta}_{0})\Big{)}\] (15) \[= 2\big{(}\mathbf{E}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{E}\Delta \phi+\mathbf{s}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{E}+\mathbf{T}^{\mathrm{H}} \mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{E}\Delta\tau+\mathbf{A}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{ \mathrm{H}}\mathbf{E}\Delta\theta\big{)},\] \[F^{\prime}_{\Delta\tau}(\mathbf{\Delta})= \frac{\partial}{\partial\Delta\tau}\Big{(}S(\tilde{\mathbf{p}}_{0}, \mathbf{\Delta}_{0})^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{S}(\tilde{\mathbf{p}}_{0},\mathbf{\Delta}_{0})\Big{)}\] (16) \[= 2\big{(}\mathbf{T}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{T}\Delta \tau+\mathbf{s}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{T}+\mathbf{A}^{\mathrm{H}} \mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{T}\Delta\theta+\mathbf{E}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{ \mathrm{H}}\mathbf{T}\Delta\phi\big{)}.\]  
\(\Delta\theta\), \(\Delta\phi\), and \(\Delta\tau\) can be solved by combining the linear functions (14)-(16), which can be written in determinant forms as,  
\[ \begin{bmatrix}\mathbf{A}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{A}&\mathbf{T}^{ \mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{A}&\mathbf{E}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{ \mathrm{H}}\mathbf{A}\\ \mathbf{A}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{E}&\mathbf{E}^{\mathrm{H}}\mathbf{V}\mathbf{ V}^{\mathrm{H}}\mathbf{E}&\mathbf{T}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{E}\\ \mathbf{A}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{T}&\mathbf{E}^{\mathrm{H}}\mathbf{V}\mathbf{ V}^{\mathrm{H}}\mathbf{T}&\mathbf{T}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{T}\end{bmatrix} \begin{bmatrix}\Delta\theta\\ \Delta\phi\\ \Delta\tau\end{bmatrix} =- \begin{bmatrix}\mathbf{s}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{ \mathrm{H}}\mathbf{A}\\ \mathbf{s}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{E}\\ \mathbf{s}^{\mathrm{H}}\mathbf{V}\mathbf{V}^{\mathrm{H}}\mathbf{T}\end{bmatrix} .