esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Methodology  
### Simplification of Accumulators' Communication Port  
The gas-filled accumulator's structure is illustrated in Figure 1. The primary components of the accumulator include an annular space enveloping the propellant supply pipeline and some communication ports arranged on the pipeline. This annular space can be further divided into an upper unlage zone and a lower liquid zone. The internal pipeline is usually integrated together with the annular structure for the sake of design, processing, and testing requirements. The upper end of this structure is connected to the upstream propellant supply pipeline, and the lower end is linked to the pump inlet of the engine.  
This research is primarily focused on studying the influence of vertical flow within the supply pipeline on the horizontal oscillatory flow in the communication port. It can be simplified by replacing the annular liquid area following the communication port with a branch pipe. The reason for this substitution is that under specific Reynolds number conditions, such as 10\({}^{5}\), the increase in flow coefficient resulting from changes in the orifice aperture ratio within the range of 0.5\(\sim\)0.75 is less than 2%. This change is not considered as a significantly influential factor when compared to the vertical mainstream flow influence [13]. Through this approach, the problem is transformed into a situation similar to a tee pipe, where the main pipe and branch are connected by an orifice. Also, this study is conducted on a single communication port, isolated from the overall accumulator, without taking the coupling effect between communication ports into account.  
Considering the factors mentioned above, a simplified schematic model of the communication port is derived, as illustrated in Figure 2. Its characteristic dimensions include main-pipe diameter \(D\), communication port diameter \(D_{\rm P}\) and branch pipe diameter \(d\).  
The communication port operates under two distinct working conditions. When the static pressure in the mainstream exceeds than in the branch, it will cause the fluid to be forced into the branch, which is called \"charging\". Under this condition, the flow region includes one inlet boundary and two outlet boundaries; conversely, when the static pressure in the mainstream is lower than that in the branch, the fluid is directed into the mainstream, which is called \"discharging\". Under this condition, the system includes two inlet boundaries and one outlet boundary.  
The monitors' locations are determined by referencing the measurement locations from communication port flow simulations [12; 13] and those from orifice flow simulations and experiments [8; 15]. A slight friction loss is conveniently disregarded, with the distance  
Figure 1: Sectional view of the gas-filled accumulators. (**a**) Sectional view of Ares I Pogo accumulator, (**b**) Sectional view of Zenit’s accumulator [14].  
between Section 1 and the communication port denoted as \(D\) and the distance between section \(k\) and the communication port as \(d\).
# A Spatial Compounding Method for Non-Delayed Sequential Beamforming
## 3 Results
### In Vivo Imaging  
To test the robustness of the proposed compounding method in clinical circumstances, we acquired in vivo data from a 26-year-old volunteer using the Verasonics scanner. This data was obtained with appropriate ethical clearance and informed consent. During the experiment, the probe and the human object are kept still to eliminate large motion artifacts.  
Figure 7 shows the in vivo images. The image contains the transverse view of two vessels in the neck, including the jugular vein and the carotid artery. We choose the region inside the jugular vein to calculate CR and CNR values. The ROI is enclosed in the white circle in Figure 7. The background is depicted in the white rectangular which is within the tissue region in the thyroid gland. After compounding, the contrast for the jugular vein improves. The measured CR and CNR are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **DAS** & **NDSB** & **Comp1** & **Comp2** \\ \hline CR [dB] & \(-33.5\) & \(-34.7\) & \(-24.0\) & \(-23.8\) \\ CNR [dB] & \(-16.0\) & \(-16.8\) & \(-12.0\) & \(-10.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contrast-ratio and Contrast-to-noise ratio for Figure 7.  
Figure 6: B-mode images of experiment cyst phantom. (**a**) DAS image (**b**) NDSB image (**c**) Comp1 image (**d**) Comp2 image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **DAS** & **NDSB** & **Comp1** & **Comp2** \\ \hline CR [dB] & \(-33.5\) & \(-34.7\) & \(-24.0\) & \(-23.8\) \\ CNR [dB] & \(-16.0\) & \(-16.8\) & \(-12.0\) & \(-10.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contrast-ratio and Contrast-to-noise ratio for Figure 6.  
Figure 7: B-mode images of cross-section carotid artery and the jugular vein in 50-dB range. (**a**) DAS image (**b**) NDSB image (**c**) Comp1 image (**d**) Comp2 image.  
From Table 3, the proposed Comp2 gives the best CR and CNR performance, Comp1 also gives similar performance. Comp2 outperforms DAS by 9.7 dB and 5.2 dB in terms of CR and CNR. Compared with NDSB, the compounding method provides up to 10.9-dB and 6-dB improvement in CR and CNR, respectively.  
The longitudinal section beamformed images of the carotid artery are shown in Figure 8. Visually, the contrast within the carotid artery improves with the proposed method. The ROI is chosen within the carotid artery depicted in the white circle in Figure 8. The background region is within the white rectangular. The measured CR and CNR are shown in Table 4.  
From Table 4, the proposed Comp2 gives the best CR and CNR performance, Comp2 outperforms DAS by 6.4 dB and 1.4 dB in terms of CR and CNR. Compared with NDSB, the compounding method provides up to 8.0-dB and 4.5-dB improvement in CR and CNR, respectively.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 4 Performance Tradeoffs Analysis
### Array 2 Gain vs. Spatial Multiplexing Gain Tradeoff
Then the above expression (61) will be  
\[\frac{d\eta_{e,\ell}}{d\eta_{s,\ell}}=\frac{\left(1-\frac{\tau_{p}}{T_{coh}} \right)\sum\limits_{k=1}^{K}\frac{\frac{\eta_{n_{e,\ell}}}{\partial p_{d\ell} }\left(\frac{1}{t_{RF}}\sum\limits_{k=1}^{K}p_{d\ell}\left(1-\frac{\tau_{p}}{T _{coh}}\right)/2+P_{cct}\right)-\frac{\eta_{e,\ell}}{\epsilon_{RF}}\left(1- \frac{\tau_{p}}{T_{coh}}\right)/2}{\left(\frac{1}{t_{RF}}\sum\limits_{k=1}^{K }p_{d\ell}\left(1-\frac{\tau_{p}}{T_{coh}}\right)/2+P_{cct}\right)^{2}}dp_{d \ell k}}{\sum\limits_{k=1}^{K}\frac{\partial\eta_{e,\ell}}{\partial p_{d\ell} }dp_{d\ell k}}. \tag{62}\]  
To discuss the relation between energy and spectral efficiencies, if the spectral efficiency approaches zero, the SINR will be also zero. Expression (62) is inversely proportional to total power consumed under the effect of pilot overhead, and then the following is obtained  
\[\lim_{p_{d\ell}\to 0}\frac{d\eta_{e,\ell}}{d\eta_{s,\ell}}=\frac{\left(1- \frac{\tau_{p}}{T_{coh}}\right)}{P_{cct\ell}}. \tag{63}\]  
**Theorem 2:**_At high SNR regime, the channel capacity increases with \(M\) transmit, \(K\) received antennas and i.i.d Rayleigh faded gains between each antenna pair, would be increased by the factor min \(\left\{M,K,\frac{T_{coh}}{2}\right\}\) which is the total number of degree of freedom._  
Using the principle of Theorem 2 above, and for the massive MIMO multi-cell system under imperfect CSI with high SNR regime, the capacity is given by \[R_{rate}=\min\{M,K,\frac{T_{coh}}{2}\}\left(1-\frac{\tau}{T_{coh}}\right)\sum \limits_{k=1}^{K}\log(SINR), \tag{64}\]  
\(\min\left\{M,K,\frac{T_{coh}}{2}\right\}\) represents parallel spatial channels, while \(\frac{T_{coh}}{2}\) is for one data transmission phase, i.e., downlink or uplink data transmission [37]. **Corollary 1:**_As the number of antennas approaches infinity in massive MIMO, the spectral efficiency will increase, then SINR expression (\(\Upsilon_{\ell k}\)) will be written as_  
\[\lim\limits_{M\rightarrow\infty}\Upsilon_{\ell k}=\lim\limits_{M\to \infty}\frac{Mf_{MRT}p_{p}p_{d\ell k}\beta_{\ell k\ell}^{2}}{Mf_{MRT}p_{p} \sum\limits_{j\
eq\ell}^{L}p_{djk}\beta_{jk\ell}^{2}+\sigma_{n}^{2}}=\frac{p_ {d\ell k}\beta_{\ell k\ell}^{2}}{\sum\limits_{j\
eq\ell}^{L}p_{djk}\beta_{jk \ell}^{2}}, \tag{65}\]  
Expression (65) is the ratio between the direct gains (\(\beta^{2}{}_{\ell k\ell}\) large scale fading channel inside cell) and the cross gains (\(\beta^{2}{}_{jk\ell}\) interference channel between cells), where \(\sum\limits_{j\
eq\ell}^{L}p_{djk}\beta_{jk\ell}^{2}\) is the sum of squares interference coefficients between base stations from other cells and the user in another cell due to the use of non-orthogonal pilot sequences in other cells [38].
## 6 Holistic Research Based on Sensory and Consumer Studies
### Holistic Approaches and Food Marketing
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Concept** & **Description** & **Application in Food Marketing** & **Reference** \\ \hline \multirow{3}{*}{Sensory Marketing} & Integrates human senses (sight, sound, touch, taste, and smell) into marketing to evoke emotions and influence consumer behavior. & \(\bullet\) & Used to create emotional connections and memorable brand experiences; & \\  & & & Enhances consumer engagement and brand loyalty. & [162] \\ \hline \multirow{3}{*}{Crossmodal Correspondences} & Utilizes sound and shape symbolism to align sensory expectations with product attributes. & \(\bullet\) & Enhances product experiences by ensuring congruence between sensory cues (e.g., sound and shape) and product expectations; & \(\bullet\) & [163] \\  & & & & sound and shape) and product expectations; & [163] \\  & & & Influences consumer perceptions and preferences. & \\ \hline \multirow{3}{*}{Sensory Quality Signals} & Employ extrinsic sensory cues like sensory descriptions and labels to influence consumer choices. & \(\bullet\) & Particularly effective in the wine market, empowering consumer choice, increasing loyalty, and reducing food wastage; & \(\bullet\) & [164] \\  & & & & rapidly to other food products, such as fruits and vegetables. & \\ \hline \multirow{3}{*}{Integration of Sensory Analysis} & Combines sensory analysis with marketing to improve product development and market strategies. & \(\bullet\) & Helps in product positioning, market segmentation, and advertising strategies; Provides insights into consumer preferences and sensory evaluation. & [165,166] \\  & & & & \\ \hline \multirow{3}{*}{Holistic Food Development} & Proposes a comprehensive approach to functional food development, integrating technological and nutritional perspectives. & \(\bullet\) & Aims to create effective functional foods by balancing quality and functionality, supported by dedicated communication strategies to inform consumer needs and regulatory development. & [167] \\  & & & & straighteos to inform consumer needs and regulatory development. & \\ \hline \multirow{3}{*}{Culinary and Sensory Research} & Encourages collaboration between food scientists and culinary experts to enhance food offerings and consumer experiences. & \(\bullet\) & Integrates disciplines like cognitive psychology and linguistics; & \multirow{3}{*}{[160]} \\  & & & & Enrich sensory and consumer science, leading to innovative products and services. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Holistic sensory approaches and their application in food marketing. Table 2 shows that holistic sensory approaches to food marketing integrate sensory experiences to enhance consumer engagement and product differentiation. These strategies include sensory marketing, cross-modal correspondences, and sensory quality signals to create memorable and emotionally resonant consumer experiences.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 4 The Experiments
### Comparison with the Popular Trackers  
#### 4.2.1 Got-10k  
GOT-10k [38] proposed by the Chinese Academy of Sciences, is a large-scale tracking dataset comprising over 10,000 video sequences, 563 target categories, 87 sports modes (e.g., running, swimming, skiing, crawling) with 180 sequences, 84 target categories, and 32 sports modes in the test set. The average length of the test set sequence is 127 frames. Results are required to be submitted to the official platform for online evaluation, making it one of the mainstream datasets in the field of object tracking due to its relatively fair and accurate assessment process.  
To avoid evaluation errors caused by imbalances in the number of categories, GOT-10k introduces class-balanced metrics, namely mAO and mSR, as evaluation criteria. Typically, thresholds of 0.5 and 0.75 are used for mSR evaluation.  
The computation process for mAO and mSR is illustrated in Formula (8).  
\[\begin{split}\text{mAO}&=\frac{1}{c}\sum\limits_{c=1}^{C} \left(\frac{1}{|S_{c}|}\sum\limits_{i\in S_{c}}\text{AO}_{i}\right)\\ \text{mSR}&=\frac{1}{c}\sum\limits_{c=1}^{C}\left( \frac{1}{|S_{c}|}\sum\limits_{i\in S_{c}}\text{SR}_{i}\right)\end{split} \tag{8}\]  
Experimental results are shown in Table 1 for the GOT-10k. After fusing spatial scale information, our tracker achieves the highest accuracy while maintaining a real-time tracking speed of 28 fps. The mAO, mSR\({}_{0.5}\), and mSR\({}_{0.75}\) of SSTrack are 71.5%, 81.9%, and 65.5%, which are 2.6%, 2.2%, and 4% ahead of TransT using CNN as the backbone. Compared to SwinTrack, which uses Swin Transformer, it is 1.6%, 1.8%, and 1.6% ahead.  
In terms of speed, although Ocean has fast speed, its accuracy is not high enough. SwinTrack, which employs self-attention computation through sliding window partitioning, significantly improves tracking speed. In this paper, SSTrack introduces a spatial scale attention module based on the Swin Transformer. However, the tracking speed is somewhat reduced at the cost of improved accuracy. TransT requires complex self-attention computation, CSwinTT introduces Circular Shift Attention Mechanism, increasing computational complexity and resulting in lower speed and accuracy compared to our tracker.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 2 Two Plausible Calibration Concepts: The White- or the Black-Box
### Which Concept to Choose?  
The black box calibration concept has the advantages of being fast and relatively easy to implement. Its results include the uncertainties related to the adequacy of the wind model used by the reconstruction algorithm. However, the method has also weaknesses:  
1. multiple calibrated reference instruments (with certificates) are necessary to calibrate each of the reconstructed wind characteristics--e.g., cup anemometer for wind speed, vane for wind direction;
2. the assumptions formulated in the reconstruction algorithms may not be completely justified and strongly related to the characteristics of the calibration site--e.g., flow homogeneity in complex terrain;
3. the calibration procedure and setup is specific to the scanning trajectory of the lidar system and to each wind characteristic to calibrate (speed, direction, shear, etc.).  
The main advantages of the white box are the calibration of physically existing measurands--as opposed to model estimated wind characteristics--and a lower sensitivity to wind field reconstruction assumptions. The uncertainty evaluation of any reconstructed wind characteristics is theoretically permitted by the white box approach, for example propagating uncertainties with the GUM or Monte-Carlo methods [16].  
On the negative side, the calibration duration is longer for multi-beam lidars as formally the \(V_{LOS}\) along each LOS--i.e., each optical path--should be calibrated. For a scanning lidar system, only one optical path exists and thus only one LOS calibration is required. Combining the calibration of a single beam with an uncertainty assessment due to deviations between beams may also be considered to reduce the calibration duration. Furthermore, to implement calibration procedures for commercial lidar systems, the reconstruction algorithms will need to be provided (as a minimum, to the calibration laboratory). The veracity of the reconstructed wind characteristics must also be addressed. In other words, the underlying physics behind the reconstruction algorithm must be verified once for each parameter and type of lidar.  
Irrespective of the technology and design choices--e.g., continuous wave (CW) vs. pulsed systems; single-beam scanning vs. multi-beam step staring; homodyne vs. heterodyne--Doppler wind lidars all have in common that they measure LOS velocities. Thus, **the path towards a generic calibration procedure of nacelle lidars leads to the white box approach**, which we demonstrate hereafter.
# Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules
## 3 Proposed Self-Evolving FLC Design Method
### Online Stage
#### Criteria to Add Control Rules  
Two criteria are used to create a new fuzzy control rule.  
**Criterion 1**.: \(\left\|\Delta e_{c}(k)\right\|>\delta\)_, with \(\Delta e_{c}(k)=e_{c}(k)-e_{c}(k-1)\), where \(\delta\) is a threshold defined by the user._  
Criterion 1 is based on the variation of the estimated control error, which if it is larger than a threshold (\(\delta\)), gives an indication to add a control rule. In this paper, the threshold (\(\delta\)) is defined as a percentage of the range of the universe of discourse of the control variable \(\Delta u=u^{+}-u^{-}\), for example \(\delta=5\%\times\Delta u\). If Criterion 1 is met, the candidate input variable \(x_{j^{*}}\) is selected by (15) (Section 3.3.2) to which a new fuzzy control rule can be added. Then, a candidate fuzzy control rule \(R_{j^{*}}^{new}\) is defined, using the candidate membership function \(A_{j^{*}}^{new}\) as explained in Section 3.3.3. Then, Criterion 2 is used to decide if the candidate fuzzy control rule \(R_{j^{*}}^{new}\) should be added to the candidate input variable \(x_{j^{*}}\), ensuring a minimal distance between membership functions.  
**Criterion 2**.: \(\left|b_{j,j^{*}_{j}}(k)-b_{j,j^{*}_{j}}(k)\right|>\eta_{j}\)_, where \(b_{j,j^{*}_{j}}\) and \(b_{j,j^{*}_{j}}\) are the centers of \(A_{j^{*}}^{new}\) and of its nearest membership function (\(A_{j^{*}}\)) respectively, and \(\eta_{j}\) is a threshold defined by the user to define the minimal distance between two closest membership functions for input variable \(x_{j}\)._  
Criterion 2 is defined in order to avoid the learning of a complex controller structure, namely limiting an excessively fine partitioning of the input variables spaces (also avoiding overfitting cases and the creation of a large number of fuzzy control rules) and improving the controller interpretability. In this paper, the threshold (\(\eta_{j}\)) is defined based on the range of the universe of discourses of the input variables, for example, for input variable \(x_{j}\), \(\eta_{j}=\left|x_{j}^{+}-x_{j}^{-}\right|/K_{j}\), where \(K_{j}\) can be represented by the maximal number of membership functions for \(x_{j}\).
## 3 System Model and Problem Formulation
### Problem Formulation
\(P3\) and \(P4\) ensure the fairness of service between cells while minimizing the average delay of all data packets. \(C1\) requires each beam to occupy one or more contiguous subbands. \(C2\) indicates the constraint for the maximum transmitting power per subband of each beam for the protection of GEO terrestrial stations. \(C3\) ensures that the sum of the allocated power of all illuminated beams cannot exceed the total available power onboard. \(C4\) indicates that at most \(K\) beams can be lit in the same time slot. It is not difficult to find that the above formula is a non-convex and non-linear problem and it contains a binary variable \(\chi_{t}^{i}\). Since we divide the total available band into \(m\) equal-bandwidth subbands, the bandwidth allocation part involves integer programming. Therefore, this optimization problem is also NP-hard and we cannot obtain the optimal solution in polynomial time.
# Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation
## 2 Materials and Methods
### Echo Cancellation Approach I: Adaptive Filtering  
The internal structure of the LMS adaptive filter is shown in Figure 5, and adaptive algorithms are used to update the filter coefficients of the adaptive filter. LMS is a search algorithm in which weight coefficients \(w(n)\) of a filter are updated on the basis of the steepest descent, moving weight coefficients in proportion to the instantaneous gradient estimate of the mean square error [30]. The formula is as follows:  
\[y(n)=w^{T}(n)x_{N}(n) \tag{4}\]  
Figure 4: System architecture of ultrasonic through-metal communication.  
\[e(n)=d(n)-y(n) \tag{5}\]  
\[w(n+1)=w(n)+2\mu e(n)x_{N}(n) \tag{6}\]  
where \(y(n)\), \((\cdot)^{T}\), \(x^{T}(n)\), \(e(n)\), \(d(n)\) and \(\mu\) are the output signal, transposition operation, input signal, error signal, reference signal and step size, respectively. \(N\) denotes the filter order, which means the number of delay units in the filter, or the dimension of the filter's weight vector. It can be seen that the filter coefficient update is driven by the error signal which generates filter coefficients in a manner of minimizing the error signal.  
The PNLMS adaptive filter uses a variable step size parameter proportional to the filter taps' sparsity to adjust the convergence rate. It determines the active state based on tap sparsity, assigning larger step sizes to active taps for faster convergence and smaller step sizes to inactive taps for reduced steady-state error. Literature indicated that PNLMS had a much higher convergence rate than LMS. The PNLMS filter weight update formula is as follows :  
\[w(n+1)=w(n)+\frac{\mu G(n+1)}{\delta+x^{T}(n)G(n+1)x(n)}\cdot e(n)x(n) \tag{7}\]  
where \(G(n)\) is an N-dimensional diagonal gain matrix used to allocate step length weights to the filter's weight coefficients, and \(\delta\) is a regularization parameter.  
The fundamental principle of the RLS algorithm involves utilizing the available observation data to estimate the necessary filter coefficients and minimizing the mean square error of prediction in order to optimize the filtering effect. The formula is as follows:  
\[w(n)=w(n-1)+k(n)e(n) \tag{8}\]  
\[k(n)=\frac{P(n-1)x(n)}{\lambda+x^{T}(n)P(n-1)x(n)} \tag{9}\]  
\[P(n)=\frac{1}{\lambda}[P(n-1)-k(n)x^{T}(n)P(n-1)] \tag{10}\]  
where \(k(n)\), \(P(n)\) and \(\lambda\) are the gain vector, inverse matrix and forgetting factor. The further away from time n, the smaller the weight allocated to the error, with \(\lambda\) determining the magnitude of the weight between 0 and 1.