esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Attribute Evaluation System for Product Form Design
### Decision-Making for the Product Form Design Scheme Based on Expert Knowledge
\begin{aligned} S_{i}=\sum\limits_{j=1}^{\text{b}}w_{j}. \frac{\left(\text{PIS}_{x}^{+}-\text{c}_{\bar{j}}\right)}{\left(\text{PIS}_{x }^{+}-\text{NIS}_{x}^{-}\right)}\\ R_{i}=\text{max}_{j}\left[w_{j}.\frac{\left(\text{PIS}_{x}^{+}-\text{c}_ {\bar{j}}\right)}{\left(\text{PIS}_{x}^{+}-\text{NIS}_{x}^{-}\right)}\right] \\ Q_{i}=v\frac{\left(S_{i}-\text{min}S_{i}\right)}{\left(\text{max}S_{i}- \text{min}S_{i}\right)}+(1-\beta)\frac{\left(R_{i}-\text{min}R_{i}\right)}{ \left(\text{max}R_{i}-\text{min}R_{i}\right)}\end{aligned} \right. \tag{12}\]  
In Equation (12), \(\beta\) is the decision coefficient, generally set at 0.5 [42]. Use \(Q_{i}\) to measure the proximity to the ideal solution to determine the optimal scheme, with smaller values indicating better schemes. 2 Calculate the osculating value. \[NIS_{x}^{*}=2\cdot NIS_{x}^{-}-PIS_{x}^{+} \tag{13}\]  
\[\left\{ \begin{aligned} D_{i}^{+}=\sqrt{\sum\limits_{j=1}^{\text{b} }w_{j}.\left(c_{ij}-PIS_{x}^{+}\right)^{2}}\\ D_{i}^{*}=\sqrt{\sum\limits_{j=1}^{\text{b}}w_{j}.\left(c_{ij}- NIS_{x}^{*}\right)^{2}}\end{aligned} \right. \tag{14}\]  
\[OV_{i}=\frac{D_{i}^{+}}{D^{+}}-\frac{D_{i}^{*}}{D^{*}} \tag{15}\]  
In Equations (13)-(15), \(NIS_{x}^{*}\) represents the virtual worst-case ideal solution, \(D_{i}^{+}\) and \(D_{i}^{*}\) are, respectively, the weighted Euclidean distances of \(PIS_{x}^{+}\) and \(NIS_{x}^{*}\), and \(OV_{i}\) represents the osculating value of the product form scheme, with smaller values indicating a better performance of the scheme. 3 Aggregate evaluation value. \[\sigma_{i}=\lambda Q_{i}^{*}+(1-\lambda)OV_{i}^{*} \tag{16}\]  
In Equation (16), \(Q_{i}^{*}\) and \(OV_{i}^{*}\) are the normalized values of the benefit ratio and the osculating value, respectively. \(\lambda\) is the allocation coefficient, generally set at \(\lambda=0.5\).
## 2 Results
### Phylogenetic Analysis of the TPS-c and TPS-e/f Subgroups  
To elucidate the phylogenetic relationships and functional diversification within the TPS-c and TPS-e/f subgroups, conserved across the angiosperms yet demonstrating remarkable functional diversity, we employed a robust analytical framework. By conducting a comprehensive multiple sequence alignment of TPS genes from 115 angiosperm species,  
Figure 2: Syntenic network analysis of TPS gene family evolution. (**A–E**) Circos plots showing syntenic relationships within TPS subgroups: (**A**) TPS-a (orange), (**B**) TPS-b (yellow), (**C**) TPS-c (purple), (**D**) TPS-e/f (green), (**E**) TPS-g (blue). Each node represents a TPS gene, with inner ring color indicating subgroup and outer ring color denoting species group (super-asterids: light blue; super-rosids: light orange; basal eudicots: light purple; monocots: light green; _L. chinense_ and _A. trichopoda_: light gray). Node sizes reflect the number of synteny connections per node, with detailed col-linear TPS gene pair information available in Table S5.  
we constructed maximum-likelihood phylogenetic trees to dissect their evolutionary trajectories. The phylogenetic delineation, presented in Figure 3, Table S2, and Figure S2, clearly segmented these genes into three distinct clades: TPS-c, TPS-e, and TPS-f.  
Our results reveal a stable representation of TPS-c genes across diverse plant lineages, encompassing 21 super-asterids, 61 super-rosids, five basal-eudicots, 26 monocots, one basal-angiogenesis, and one magnolipid. Conversely, the TPS-e and TPS-f subgroups, traditionally considered a single entity due to overlapping functional attributes and structural homologies, exhibited discernible phylogenetic bifurcation. Notably, TPS-e diverged into two subclades: one enriched with monocot genes, indicative of an expansion specific to this clade post the monocot-dicot evolutionary split, while the other was devoid of them. These findings accentuate the intricate evolutionary dynamics of the TPS gene family, reflecting both the retention of ancestral traits and the diversification essential for ecological adaptations.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions
### Non Optimized and Energy Optimized Protocols  
This portion analyzes the energy optimized and non optimized versions of the selected protocols. Plots and their discussion are presented as follows.
## 2 Related Work
The state-of-the-art quality metrics designed for stereoscopic perspective or omnidirectional video are often very efficient and competitive in their application. However, as stated above, such applications are much simpler than the immersive video, where a user can freely navigate within the 3D scene but changing his or her viewing angle, as well as viewing position. Such a characteristic of the immersive video system introduces artifacts and distortions which do not occur in simpler applications, and which are induced by view rendering. On the other hand, the quality metrics designed for immersive video often require the use of depth maps [34], being less versatile (e.g., they cannot be used for the assessment of virtual views presented to the final user of the immersive video system). A more practical approach is based on adapting more straightforward metrics to characteristics of immersive video [4,35,36]. The objective quality metric, which provides the highest correlation with MOS (mean opinion score) in immersive video applications, is IV-PSNR, proposed by the authors of this paper in [4]. The IV-PSNR metric was designed to properly handle and assess distortions typical to immersive video. As written in [4], IV-PSNR provides reliable results despite not utilizing any temporal information and being based on PSNR, which is simple and widely used but certainly not the best objective quality metric.
## 6 Advanced Considerations: Environmental Factors in Underwater VLC
### Pressure  
The other critical factor that can influence the performance of VLC systems in underwater environments is pressure since it increases with depth. This increased pressure might change the density and refractive index of water, which will affect the way light propagates and is absorbed in water. According to [8], pressure increases with depth, which may alter the physical properties of water and the behavior of light, potentially affecting VLC performance. Ref. [56] does comment that the physical properties of water and light transmission change under pressure, most especially at any real water depth, but it does not comment on the specific effects.  
Pressure thus plays its role in applications involving deep seas, where the increased pressure may change the optical properties of water. Similarly, [PERSON]. [60; 62] provide a related focus on the depth-related pressure being responsible for changes in density and refractive index that could interfere with VLC system performance. In the same way, [PERSON]. [63] gives an example to illustrate how the pressure would affect changes in refractive index and absorption characteristics, leading to the alteration in the propagating light and thus signal quality. Ref. [64] discussed how the increase in pressure with depth may affect some of the physical properties of water and, further, that of light passing through the water, which again affects signal attenuation and degrades communication performance.
## 4 Analysis and Findings of Research Questions
#### 4.1.2. Smartphone User Activities Based on Service Modality
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modality** & **Sensors and Method** & **Activities** \\ \hline \multirow{7}{*}{Services} & \multirow{7}{*}{Telephony Services, Telecommunications} & \multirow{7}{*}{Tapping on a malicious link sent through email, SMS, or an “in-app” advertisement in another app such as a game [95; 12; 97; 94].} \\  & & & Adoption of BYOD policies [96]. \\  & & & Using mobile advertising to communicate with users and collect data from mobile devices [97]. \\  & & & The user uses voice access technologies in mobile devices and voice assistants [98]. \\  & & & Modern vehicles relate to the world around them through Wi-Fi connections or 3G/4G networks [85]. \\  & & & Considering mobile devices as a trusted companion [99]. \\  & & & The user’s touch actions (tap, scroll, hold, and zoom) and PINs permit a remote website to discover client-side user activity [100]. \\  & & & The user visits a website controlled by an attacker [95]. \\ \multirow{2}{*}{Physical} & \multirow{2}{*}{Touch, Gesture, Entry} & \multirow{2}{*}{The utilization of smartwatch and wearable accessories [101].} \\  & & & People regularly carry significant data assets on mobile devices [96]. \\  & & & Downloading suspicious email attachments [92; 94]. \\  & & & Uploading location-based data to social media [92; 94]. \\  & & & Browsing the latest downloads and interests using device location analysis [75]. \\  & & & Poor coding and validation of input fields [102; 103]. \\ Software & \multirow{2}{*}{System Software, Application Software} & \multirow{2}{*}{Updating software on a mobile device [87; 89].} \\  & & & Jailbreaking and rooting mobile devices [87; 89]. \\  & & & Utilizing mobile applications known as wallets [104]. \\  & & & Storing personal passwords, social security information, private pictures, and bank account information on a mobile device [87; 89; 96; 105]. \\ Applications & User Application, Third-party Application & Checking permissions that the application requires [87; 89]. \\  & & & Downloading apps from untrusted third-party websites [12; 87; 92; 12]. \\  & & & Installing any malware attached to an email [12]. \\  & & & Sharing of PIN, password, or pattern information [92]. \\  & & & Downloading and utilizing third-party app stores, dealing with illegally modified free copies of premium programs [106]. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._
## 3 Results and Discussion
### Spectrometer  
The amount of water leaking from the pipes gradually increased with the increasing amount of water passed through the pipe. This leaking water was absorbed by the soil surrounding the pipes in the experimental setup and, hence, increased the soil moisture content. As a result, the reflectance recorded by the spectrometer was expected to decrease with the increase in time. This expectation was met based on the results of the experiment. The reflectance obtained by the spectrometer at wavelengths higher than 0.70 \(\upmu\)m significantly decreased when compared to the reading taken at time zero and the reading taken after 60 min.  
A sample reflection/transmission plot like those shown in Figure 16 demonstrates the changes against different wavelengths. The results obtained for pipe 2 (pipe with a crack) and pipe 3 (pipe with a hole) for all three pipe materials (PPR, PVC and PE) at 2% moisture content are shown in Figure 17. Based on the results, the spectral reflectance decreased at wavelengths greater than 0.70 \(\upmu\)m with the moisture content increased. These findings agree with findings in literature which suggests that the decrease in reflectance with the increase in moisture content is more noticeable in the infrared bands [23]. The reflectance obtained at wavelengths less than 0.70 \(\upmu\)m was observed to decrease; however, the decrease was inconsistent, making it difficult to draw any conclusion. Furthermore, the spectrometer was not able to consistently identify leaks in the pipes when the moisture content was 5% and 10%.  
Figure 16: Spectrometer reflectance obtained from the PE pipe with a crack (right) at 2% moisture content.  
Figure 15: IR images obtained on PPR, PVC and PE setup with 10% soil moisture content.  
The spectrometer could identify leaks from the crack and hole in all pipe material at 2% sand moisture content. However, the spectrometer was unable to detect the leak from the joint due to the accumulation of water on the surface of the soil that resulted in ponding. As a result, it was not possible to use the instrument. Nevertheless, the leak in the fourth pipe was detected by just visual inspection as the water from the faulty joint had reached the surface.
## 8 Wireless Body Area Networks (WBANs) and Wearable (WBANs)
To improve the efficiency of WBAN, it is important to minimize the number of nodes in the network. Adding more devices and path redundancy for solving node failure and network problems cannot be a practical option in WBAN systems. WBANs receive and transmit a large amount of data constantly. Data processing must be hierarchical and efficient. WBANs in a medical area consist of wearable and implantable sensor nodes that can sense biological information from the human body, and transmit it over a short distance wirelessly, to a control device worn on the patient body. The sensor electronics must be miniaturized, low-power, and detect medical signals such as pulse rate, electroencephalography, electrocardiograms, pressure, and temperature. The gathered data from the control devices are then transmitted to remote destinations in a wireless body area network for diagnostic and therapeutic purposes, by including other wireless networks for long-range transmission. If the Wireless WBAN, WWBAN, is part of the telemedicine system, the medical system can alert medical personnel when life-threatening events occur. In addition, patients may benefit from continuous long-term monitoring as a part of a diagnostic procedure. Patients and physician may achieve optimal maintenance of a chronic condition, and can monitor the recovery period after the acute event or surgical procedure. The collected medical data may be a good indicator of cardiac recovery of patients after a heart surgery. Many people are using WBAN devices, such as wearable heart rate monitors, respiration rate monitor, and pedometers, for medical reasons or as part of a fitness regime. WBAN may be attached to cotton shirts to measure respiratory activity, electrocardiograms, electromyograms, and body posture. The recorded data and patient data may be stored and analyzed by employing cloud storage and cloud computing services.
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 5 Performance Evaluation
### Prediction Accuracy
Figure 6: Service cache similarity at each MECS with different \(\eta\)s. Each subfigure shows the distribution of the service cache similarity in each MECS as a box plot. In Figure 6, we observe that our method improves the similarity of the service cache regardless of the MECS position in the grid and the cache size. When \(\eta=8\), compared with the LSTM model, our method achieves an 11.26% increase in the cache similarity. Specifically, the average \(a_{m}(t)\) is 54.71% when the LSTM method is used. The average \(a_{m}(t)\) increases to 60.87% when our method replaces the LSTM method. The standard deviation of \(a_{m}(t)\) is 5.99% when our method is used, while it is 5.74% when the LSTM method is used. When \(\eta=16\), the proposed method improves the average service cache similarity by 13.52%. When the LSTM method is used, the average \(a_{m}(t)\) is 54.45%. Our method increases the average \(a_{m}(t)\) to 61.81%. The standard deviation of \(a_{m}(t)\) is 6.56% when our method is used, and it is 5.09% when the LSTM method is used.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 3 Attention Mechanism in MDE Methods  
### Overview of MDE Framework with Attention  
Most of the MDE methods start out with an encoder-decoder architecture in which encoders extract hierarchical features while decoders are employed to fuse multi-scale features to the yield depth map. Although combinations of backbone and attention modules vary a lot, we try to figure out predominant architectures for attention-based MDE methods. On the encoder end, features are mainly extracted through CNN, Transformer, and hybrid methods of both; furthermore, the hybrid methods of CNN and Transformer can be divided into serial and parallel. Therefore, according to the architecture of encoder, we summarize four typical prototypes of CNN-based, Transformer-based, and CNN-Transformer-based MDE frameworks, which are shown in Figures 6-9, respectively. Figure 6 shows a typical architecture of CNN-based MDE. ResNets [30] are often used as a backbone to extract features. In such structures, attention modules can be placed at any stage of encoder and decoder, even at the skip connections, to model long-range dependency by spatial or channel attentions. In Figure 7, we outline a Transformer-based MDE method, in which images are firstly embedded as images such as maps, and then fed into Transformer blocks. The attention mechanism exists in the Transformer blocks or locates any stage of decoder and skip connections. Figure 8 illustrates a serial scheme of the CNN-Transformer-based MDE framework. Unlike the Transformer-based methods, the input images are firstly projected into feature maps by convolutional operations, and features are then fed into Transformer blocks. Figure 9 presents another option of a CNN-Transformer-based MDE framework, in which CNN and Transformer blocks are arranged in parallel. The RGB images are inputted into two branches separately, and fusion blocks are utilized to integrate global and local information.  
Figure 5: Scaled dot product attention mechanism of Transformer (image: courtesy to [PERSON] et al. [13]).  
Figure 8: Serial scheme of CNN–Transformer-based supervised MDE.  
Figure 6: Possible combinations of CNN-based supervised MDEs.  
Figure 7: Possible combinations of Transformer-based supervised MDEs.  
Notably, the components of the schemes mentioned above may not simultaneously appear in one framework. We just report the possible combinations in one figure. In addition, we only describe supervised MDE methods in Figures 6-9 and the inputs and outputs of each block in these figures are two-dimensional images. For the self-supervised MDE, an extra camera pose estimator is needed to obtain rotation and shift matrices, and photometric error is calculated by the original image and the wrapped image is used to train the networks.
# Smart IoMT Framework for Supporting UAV Systems with AI
## 5 Discussion
### Future Directions  
Many MAC protocols have been suggested for UAV-based connectivity for WSNs, but none of them have been approved as a standard procedure. One of the major reasons is that these protocols are designed for a specific objective and do not provide high throughput, low-power communication, synchronization, scalability, and other features. The CSMA/CA protocol enables fast throughput with little synchronization cost, but it needs alternate methods to prevent collisions between numerous cores in disaster zones when several BANs must relay data urgently.  
UAVs often follow a variety of paths and may fly or hover continuously. This has an impact mostly on the coverage zones that the UAVs can monitor and detect for data collection. Whenever the UAV is stationary, the problem is identical to that of WSN coverage; however, once the UAV is moving, numerous UAVs may be necessary to cover a certain region for data gathering. Coverage difficulties must be addressed as part of smart healthcare systems, with an emphasis on maximizing and improving coverage in affected areas.  
Localization methods are critical for monitoring and tracking UAVs as they transmit health data. They could also monitor the whereabouts of people that require rapid medical attention. The Global Positioning System (GPS) is used to accurately localize UAVs, although it has communication challenges owing to bad weather, which could result in poor accuracy and signal reception. Because UAVs must connect to BANs in emergency zones or during severe weather, further research is needed to establish localization methods in this approach.  
Routing protocols are critical for allowing several UAVs to transmit BAN data by determining the best and quickest way. Path scheduling is among the primary issues with UAVs and to determine an ideal travel trajectory for UAVs, and alternative techniques are required, particularly in disaster situations. UAVs could be able to adapt to position-based navigation. However, because of the movement of UAVs, it frequently fails to find an ideal path, necessitating additional research in this area.
## 2 System Description
### Direction Finding of ASK and MSK Modulated Signals
Let \(b(t-\tau_{1}(t))=b_{k}\); that is, the current symbol is \(b_{k}\), then  
\[s_{1}(t) =\cos[2\pi f_{c}(t-\tau_{0}(t))+\phi_{\text{MSK}}(t-\tau_{1}(t))], \tag{22}\]0  
This approximation holds when the path delay \(\tau_{d}\) is much smaller than the symbol period \(T\). For common MSK signals, \(T\) typically ranges between 4-10 ms. In this system, the maximum path delay \(\tau_{d}\) is approximately 17.3 \(\upmu\)s, which satisfies the approximation condition. At this time,  
\[s_{1}(t) =\cos[2\pi f_{c}(t-\tau_{0}(t))+\phi_{\text{MSK}}(t-\tau_{1}(t))], \tag{22}\]1  
Since \(\frac{\pi}{2T}\cdot b_{k}\) is much smaller than \(2\pi f_{c}\), and \(b(t-\tau_{d})=\pm 1\), over a certain period of integration, if the number of positive and negative terms of \(b_{k}\) is roughly equal and \(\Delta\phi\) is not extremely small, this term can be neglected. Therefore,  
\[s_{1}(t) =\cos[2\pi f_{c}(t-\tau_{0}(t))+\phi_{\text{MSK}}(t-\tau_{1}(t))], \tag{22}\]2  
To more effectively eliminate the influence of term \(\frac{\pi}{2T}\cdot b_{k}\), i.e., to remove the MSK phase modulation and better track the phase variation of the carrier frequency, we set  
\[ \begin{split}\phi_{0}(t)&\equiv-2\pi f_{c}\tau_{0} (t),\\ \phi_{1}(t)&\equiv-\frac{\pi\tau_{1}(t)}{2T}.\end{split}  \tag{29}\]  
The MSK signal with delay received by the receiver can be expressed as follows:  
\[ \begin{split} x(t)&=\cos[2\pi f_{c}t+\phi_{0}(t)+ \phi_{\text{MSK}}(t-\tau_{1}(t))]\\ &=\cos[2\pi f_{c}t+\phi_{0}(t)+b(t-\tau_{1}(t))\bigg{(}\frac{\pi t }{2T}+\phi_{1}(t)\bigg{)}+\theta(t-\tau_{1}(t))\bigg{]}.\end{split}  \tag{30}\]  
After performing a series of operations such as downconversion and filtering on the wideband signal, we obtain  
\[z(t)=A(t)e^{j\phi(t)}=A(t)\exp[j\phi_{0}+j\phi_{\text{MSK}}(t-\tau)]. \tag{31}\]  
By squaring the signal \(z(t)\), since \(2\theta(t-\tau_{1}(t))=0\) or \(2\pi\), the \(\theta(t-\tau_{1}(t))\) term can be eliminated, resulting in  
\[\angle[z(t)]^{2}=2\phi_{0}(t)\pm\frac{\pi t}{T}\pm 2\phi_{1}. \tag{32}\]  
In this equation, the signs of the latter two terms are determined by the instantaneous symbol of \(b(t-\tau_{1}(t))\). Let  
\[\phi_{\pm}=2\phi_{0}\pm 2\phi_{1}, \tag{33}\]  
Assuming that \(\phi_