esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Local Quantum Depletion and Generalized Gross-Pitaevskii Equation  
Assuming a slowly-varying order parameter, such that the gradient term can be neglected, but also a small quantum depletion, from Equation (10), the chemical potential \(\mu\) can be approximated as:  
\[\mu\simeq g|\psi_{0}(\mathbf{r})|^{2}+U(\mathbf{r}). \tag{25}\]  
It is then straightforward to derive the elementary excitations:  
\[E_{\mathbf{k}}(\mathbf{r})=\sqrt{\frac{\hbar^{2}k^{2}}{2m}\left(\frac{\hbar^{2 }k^{2}}{2m}+2g|\psi_{0}(\mathbf{r})|^{2}\right)}\, \tag{26}\]and the real quasi-particle amplitudes:  
\[u_{\mathbf{k}}(\mathbf{r}) = \frac{1}{\sqrt{2}}\left(\frac{\frac{\hbar^{2}k^{2}}{2m}+g|\psi_{0}( \mathbf{r})|^{2}}{E_{\mathbf{k}}(\mathbf{r})}+1\right)^{1/2} \tag{27}\] \[v_{\mathbf{k}}(\mathbf{r}) = -\frac{1}{\sqrt{2}}\left(\frac{\frac{\hbar^{2}k^{2}}{2m}+g|\psi_{ 0}(\mathbf{r})|^{2}}{E_{\mathbf{k}}(\mathbf{r})}-1\right)^{1/2}. \tag{28}\]  
We can now insert Equation (28) into (23), and after integration over the linear momenta, we obtain:  
\[\tilde{n}(\mathbf{r})=\frac{\sqrt{2}}{12\pi^{2}}\left(\frac{2 mg}{\hbar^{2}} \right)^{3/2}|\psi_{0}(\mathbf{r})|^{3}. \tag{29}\]  
This is the local version of the familiar Bogoliubov term for the quantum depletion, originally obtained for a uniform bosonic system, i.e., with \(U(\mathbf{r})=0\). For the local anomalous average density, after dimensional regularization [12], we find instead:  
\[\tilde{m}(\mathbf{r})=3\ \tilde{n}(\mathbf{r}). \tag{30}\]  
Finally, inserting this expression into Equation (10), we get:  
\[\begin{split}\mu\ \psi_{0}(\mathbf{r})&=\Big{[}- \frac{\hbar^{2}}{2m}\
abla^{2}+U(\mathbf{r})+g|\psi_{0}(\mathbf{r})|^{2}+\frac {\sqrt{2}}{6\pi^{2}}\left(\frac{2m}{\hbar^{2}}\right)^{3/2}g^{5/2}|\psi_{0}( \mathbf{r})|^{3}\Big{]}\psi_{0}(\mathbf{r})\\ &+\frac{\sqrt{2}}{4\pi^{2}}\left(\frac{2 mg}{\hbar^{2}}\right)^{3/ 2}g^{5/2}|\psi_{0}(\mathbf{r})|^{3}\psi_{0}^{5}(\mathbf{r})\.\end{split} \tag{31}\]  
This is a modified Gross-Pitaevskii equation containing beyond-mean-field corrections due the the presence of local quantum depletion and anomalous average density. The chemical potential \(\mu\) of Equation (31) is fixed by the normalization condition:  
\[N=\int d^{3}\mathbf{r}\left[|\psi_{0}(\mathbf{r})|^{2}+\tilde{n}(\mathbf{r}) \right]=\int d^{3}\mathbf{r}\left[|\psi_{0}(\mathbf{r})|^{2}+\frac{\sqrt{2}} {12\pi^{2}}\left(\frac{2 mg}{\hbar^{2}}\right)^{3/2}|\psi_{0}(\mathbf{r})|^{3}\right] \tag{32}\]  
with \(N\) the total number of bosons.  
It is important to stress that, in the case of a uniform and real Bose-Einstein condensate, Equation (31) gives:  
\[\mu=g\eta_{0}+2g\tilde{n}+3g\tilde{n}=gn+4g\tilde{n}=gn+gn_{0}\frac{32}{3 \sqrt{\pi}}\sqrt{n_{0}a_{\mathrm{s}}^{3}}\, \tag{33}\]  
which is the chemical potential with the familiar beyond-mean-field Lee-Huang-Yang correction [6] under the assumption of small quantum depletion. Clearly, one does not obtain this zero-temperature result neglecting the anomalous average density \(\tilde{m}\).
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 2 Related Work  
### Notations  
In this paper, tensors, matrices, vectors and scalars are denoted by \(\mathcal{H}\), \(\mathbf{H}\), \(\mathbf{h}\), \(h\). For instance, the HSI tensor is represented by \(\mathcal{H}\in\mathbb{R}^{h\times\text{w}\times L}\), where \(h,w,L\) correspond to height, width and the number of spectral band respectively. HSI tensor reshapes as two-dimensional matrix \(\mathbf{H}\in\mathbb{R}^{n\times L}\), where \(n\) represents \(h\times w\). The prior spectral vector represents \(\mathbf{d}\in\mathbb{R}^{L\times 1}\).
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 5 Conclusions
### Conclusions  
When conducting deep excavation projects, it is essential to perform numerical simulation analyses to ensure construction safety. For neighboring buildings, the distance from the excavation site must be carefully considered, as the distance factor significantly impacts the deformation of the diaphragm wall. During excavation, it is possible to estimate the impact on neighboring buildings to avoid causing damage and ensure safety.  
When simulating neighboring buildings, the load factor must be carefully included as a key consideration. The load significantly affects the maximum deformation of the wall and contributes to surface settlement. This indicates that the load must be effectively considered in deep excavation simulations to accurately estimate the potential impact on neighboring buildings.  
The depth factor is often overlooked in industry simulations, but the research results showed that it has a significant impact on the maximum lateral deformation of the diaphragm wall. When the depth of the neighboring building reached 0.5 He, the maximum lateral displacement of the wall tended to converge. At 0 He, the wall exhibited the maximum deformation. Therefore, when simulating the load of neighboring buildings, appropriately simulating the basement depth can avoid overestimating the maximum lateral deformation.  
Among the three simulation models--MC, HS, and HSS--the results indicate that the MC model significantly underestimated, the HS model tended to overestimate, and the HSS model provided intermediate results. The HSS model offered the most reasonable results for analyzing the lateral displacement phenomena of diaphragm walls.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 4 Validation and Experimental Results
### Comparison against Sibyl Framework
#### 4.2.3 Results  
The results of all the experiments executions are shown in Appendix A. As can be seen from Table A1, the number of PDUs (BGP UPDATES) obtained with our simulations in ns-3 DCE exactly match the number obtained with the Sibyl emulations for most of the scenarios. This exact match between the results in ns-3 DCE and the emulation platform strongly validates the accuracy of our simulation platform. In particular, this shows that with the proposed platform, we can execute the exact same BGP algorithm that runs in the Sibyl emulation approach.  
Regarding the scenarios where there are differences, we should note that there are some Sibyl scenarios that present more than one result. This is due to the fact that the emulations are not deterministic, and depend, for example, on the host machine resource usage. For the cases where we have more than one result from Sibyl, the results obtained in our simulations are between these values or very close to them. These differences also demonstrate one of the main advantages of the simulation against the emulation given that in the simulation the results are deterministic and reproducible. The exact same result can be obtained independently of the underlying hardware or software where it is run.  
In Table 1, we select some specific results. In particular, we show two cases with a significant difference between our experiments and the Sibyl framework results. If we consider the couple of scenarios painted in blue in Table 1, the result in scenario 10_10_1_node-failure-spine for Sibyl is roughly half the result obtained in ns-3 DCE;we argue that this is an outlier in Sibyl, as we will further show in Section 4.3.1. Regarding 12_12_1_node-failure-leaf scenario, note that the result for Sibyl is smaller, but in the same order than the one of ns-3 DCE; here, we argue that the vector-distance nature of BGP and its well known characteristic of \"path hunting\" is responsible for this difference, as we further explain in Section 4.3.2.  
Note that we can also compare the results of the simulations with and without the zebra daemon running. As explained in Section 5, we executed the experiments disabling zebra in order to reduce the resource consumption of the simulation. In most of the scenarios considered, this change does not affect the number of BGP updates exchanged in the experiment. Nevertheless, in some cases there exists a small difference (actually, in 8 out of 250 experiments, 3.2%). Most of the misalignment in the results are experienced in node-failure-leaf scenarios, due to the very reason we mentioned above: the path-vector nature of BGP may cause extra UPDATE messages to be exchanged, as explained in Section 4.3.2.  
In the following section, we also compare the obtained results against a theoretical model of the behavior of BGP.
###### Abstract
In [26], the original time-frequency watermarking scheme is realized with a lead-independent beat-to-beat adaptive data container design. The authors demonstrated six wavelets, six encoding bit depth values, and two watermark content types to catch the necessary conditions for the watermarked ECG to fit International Electrotechnical Commission (IEC) performance requirements. [PERSON] et al. [27] announced a wavelet-based watermarking procedure for the patient information hidden in the ECG as a QR image. First, they converted the 1D ECG signal into a 2D ECG image using the Pan-Tompkins algorithm and used a wavelet transform to decompose the 2D ECG image. Then, they decomposed the wavelet detail coefficient and the QR image using QR decomposition for incorporating the data. The concepts proposed in [28] are single-sample quantification, ECG watermarking, and threshold-based compression, which reduce data size while ensuring patient data confidentiality and authenticity. Since patients' confidential data including patient name, patient birthdate, date of medical treatment, and medical history have the right to personal privacy, the hospital and related personnel must respect and protect patients' confidential data during network transmission or telemedicine in the hospital to prevent leaked, stolen, or even misappropriated data. Accordingly, we design a useful information-hiding technique to embed patients' confidential data into ECG signals in this study. In other words, we propose a new technique to hide patients' confidential data in ECG signals. Since ECG has high accuracy requirements, especially PQRST waves [29], we rewrite the signal-to-noise ratio (SNR) and the low-frequency amplitude embedding rule as a performance index and constraint so as to obtain an optimized model for embedding sensitive patients' confidential data including patient name, patient birthdate, date of medical treatment, and medical history attached with patient bed into ECGs. The optimization model is processed by simulated annealing (SA) algorithms, and the results are applied to incorporate the confidential patient data for satisfying better signal-to-noise ratio (SNR), root mean square error (RMSE), and similarity. Consequently, the distortion of PQRST complexes and ECG amplitude is very low, so that the embedded confidential data can fit the requirements of physiological diagnostics. Through network transmission, the ECG with confidential data embedded can be received at the other end and the confidential data can be extracted without the original ECG. In addition, the embedded confidential data shows a good robustness against common attacks. The rest of this study is as follows. Section 2 gives a sketch of some preliminary work, including discrete wavelet transform (DWT) and simulated annealing (SA). Section 3 presents the proposed method. Section 4 displays the experimental results. Finally, conclusions are shown in Section 5.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Application Challenges
#### 5.1.1 Robustness and Adaptability  
One of the major concern in the deployment of CPSs to monitor water supply systems facilities is the robustness of the sensing device. In a water piping network application where the sensors are exposed to severe temperature and harsh environment, the long-term durability of the sensing device is a challenge. For example, using a CPS to monitor the water quality in a water pipe network installed in a harsh underground environment, flora aggregate [111; 112] may be found around the sensors, which depletes the sensing power and the operational performance of the sensing device. In extreme cases, the flora aggregate may not be easily removed, and consequently, the long-term effectiveness of the sensing device in such applications is a major concern. The effectiveness of the system thus depends on the application environments. In most cases, underwater and underground applications are a harsh environment for sensing devices. For instance, in CPS-based underground pipe leak monitoring using ground penetration radar (GPR) sensing technology, such a system is considerably affected by the type of soil in which the pipe is buried. GPR sensing technology is a technique that utilizes electromagnetic wave propagation and scattering to track a physical phenomenon. So in clay soils, corrosion products of iron pipes increase the radio frequency signal attenuation and reduce reflection [4]. Consequently, the iron pipes are masked from the GPR signal. For more information on GPR operational principle, the reader is referred to [4]. In underwater pipe leak monitoring, acoustic emission sensors are used as the sensing device. However, acoustic signals are significantly affected by background noise from the environment and those due to a pipe burst. The scheme also relies on the materials of the monitored pipe. It has been acknowledged that AE signals are well attenuated in plastic pipes [113]. Therefore, the sensing device must be robust enough to cater for these limitations. In addition, as mentioned earlier, the pattern of water demand is heavily influenced by consumer demand uncertainties and seasonal changes. As such, a CPS adopted for water quality monitoring or water supply system management must be robust and adapt quickly to responses due to water demand variations.
## 2 Materials and Methods
### Instruments and Method  
Chromotographic separations were performed on a LC20 Chromatography Enclosure System (Dionex Corporation, Sunnyvale, CA, USA) composed of a GP50 gradient pump, a Rheodyne injection valve with a 25-\(\upmu\)L injection loop, and an ED50 electrochemical detector. The flowthrough amperometric detection cell is made from a 1.0-mm diameter glassy carbon working electrode (GCE) and a standard combination pH-Ag \(|\) AgCl reference electrode, set to Ag mode; the titanium body of the cell served as the counter electrode. Separations were performed using a C8 column LiChrospher(r) 60 RP-select B (25 cm \(\times\) 4 mm, 5 \(\upmu\)m I.D. (Merck KGaA, Darmstadt Germany) coupled with the guard column LiChroCART LiChrospher 60 RP-select B (4 mm \(\times\) 4 mm, 5 \(\upmu\)m I.D., Merck) at a flowrate of 1.0 mL/min by isocratic elution with 20 mM of acetic acid/acetonitrile (55:45 \(v/v\)). The pH was adjusted to 7.0 with acetic glacial acid or sodium hydroxide, as necessary. The reservoir bottles were closed and pressurized with pure nitrogen to 0.8 MPa. A three-step waveform PAD was employed which consists of three potentials and four-time parameters. The optimized applied potentials and pulse durations were the following: E\({}_{\text{OX}}\) = +2.0 V (t\({}_{\text{OX}}\) = 50 ms), E\({}_{\text{DET}}\) = +1.2 V (t\({}_{\text{DEL}}\) = 100 ms, and t\({}_{\text{INT}}\) = 800 ms), and E\({}_{\text{RED}}\) = \(-2.0\) V, (t\({}_{\text{RED}}\) = 50 ms). The system was interfaced, via proprietary network chromatographic software (PeakNet(tm), Dionex Corporation, Sunnyvale, CA, USA), to a personal computer, for instrumentation control, data acquisition, and processing.  
Cyclic voltammetry (CV) studies were carried out by a potentiostat CHInstruments Electrochemical Analyzer and CHI620A software version 2.07 (Austin, TX, USA). Batch apparatus consisted of a conventional three electrode cell, equipped with a Pt wire counter electrode, an Ag/AgCl (saturated KCl) reference electrode, and a glassy carbon (3 mm diameter) as the working electrode. Prior to use, the surface of the GCE was polished with emery paper and alumina powder, then, ultrasonicated. The supporting electrolyte (20 mM acetic acid/acetonitrile (55:45 \(v/v\)) at pH 7) was deaerated by nitrogen bubbling for 10 min.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Block Interleaver  
A basic type of interleaver is described through a finite permutation over the numbers in Equation (7), where \(L\) is a period  
\[Z_{T}=\{0,1,\ldots,L-1\} \tag{7}\]  
An interleaver is described through the finite set of Equation (8)  
\[\begin{pmatrix}0&1&\ldots&L-1\\ \pi_{0}&\pi_{1}&\ldots&\pi_{L-1}\end{pmatrix} \tag{8}\]  
where the range of the \(\pi_{i}\) numbers corresponds to the number of elements in the set of Equation (9). This type of interleaver is denominated block permutation interleaver.  
\[Z_{L}=\{\pi_{0},\pi_{1},\pi_{2},\ldots,\pi_{L-1}\} \tag{9}\]  
A permutation matrix provides an alternate description for a block permutation interleaver. For example, some interleavers can be described by symmetric 3 \(\times\) 3 generator matrices, such as:  
\[G=\begin{pmatrix}0&1&0\\ 1&0&0\\ 0&0&1\end{pmatrix} \tag{10}\]  
However, other interleavers are described by symmetric matrices where the nonzero terms in the matrix are monomials in the variable \(D^{n}\), where \(n\) is an integer; in this way, the multiplexed interleaver has as its generator a symmetric diagonal matrix such as the one below:  
\[G=\begin{pmatrix}1&0&0\\ 0&D&0\\ 0&0&D^{2}\end{pmatrix} \tag{11}\]
## 3 Collocation and Evaluation Methodology
### Collocation and Evaluation Methodology
The side effect of multiple grid matching is the possibility of duplicate matches. Therefore, a step to remove duplicates is also required. Finally, it must be verified that the distance between these matching pixels are indeed less than 2 km. This multiple grid matching algorithm can reduce the computation time complexity to O(N). Thus, this approach can be affordable with limited computational resources. Figure 1: Schematic diagram depicting how the Advanced Geosynchronous Radiation Imager (AGRI) pixel (the blue dot) is collocated with Moderate Resolution Imaging (MODIS) pixels (the green dot and the red dot) by using multiple grid matching methodology. These three dots represent the central position of each pixel. First, by using the grid of large size (red grid), the AGRI pixel (the blue dot) and one MODIS pixel (the green dot) can fall into the same cell (the red cell) and become collocated. However another MODIS pixel (the red dot), which is closer to the AGRI pixel than the green one, is missed by this large red grid. Then, by using the grid of small size (black grid), this MODIS pixel, which was missed last time, and the AGRI pixel can fall into a new box, the black box, and they are collocated with each other. In addition, the MODIS scanner may view a particular MODIS FOV at an angle that departs significantly from the nadir, which may lead to increased uncertainty of the comparison. Therefore, the MODIS samples with sensor viewing zenith angle (VZA) more than 45\({}^{\circ}\) were filtered out. Only MODIS samples with VZA less than 45\({}^{\circ}\) can be used for a comparison with the AGRI samples.
# Effects of Bulk Flow Pulsations on Film Cooling with Two Sister Holes
###### Abstract  
In a triple-hole system comprising a primary hole and two sister holes, when the sister holes are positioned slightly downstream of the main hole under steady flow conditions, their jets generate an anti-counter-rotating vortex pair. Vortex interactions between the jets increase the effectiveness of adiabatic film cooling. In this study, a series of large-eddy simulations were conducted to understand how pulsations in the main flow affect film cooling in a triple hole. To understand the effects of pulsations on film cooling performance is important for better cooling design of the gas turbine engines. The numerical simulations were carried out on a flat plate geometry with a triple cylindrical hole system at 35\({}^{\circ}\) injection angle. The pulsations were approximately sinusoidal, and their effect on film cooling was investigated at several frequencies (2, 16, and 32 Hz) and Strouhal numbers (Sr = 0.1005, 0.8043, and 1.6085) at an average blowing ratio of 0.5. The results for the triple-hole system were compared with those for a single hole for the same amount of cooling air and the same cross-sectional area of the holes. Increasing the Strouhal number of the main flow decreased \(\eta\) in both systems. However, at each Strouhal number, \(\eta\) was higher in the triple hole. Furthermore, the triple-hole system was found to be better for film cooling than a single-hole system for higher values of the pulsation Strouhal number. Contours of time-averaged film cooling effectiveness and dimensionless temperature, instantaneous film cooling effectiveness contours on a test plate, mean velocity magnitude contours in the hole, and Q-contours for the triple holes under the application of pulsations to the flow were investigated.  
large-eddy simulation; turbine blade cooling system; adiabatic film cooling effectiveness; bulk flow pulsation; triple cooling hole configuration
# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 4 The Proposed Methodology
### Practical Classification Algorithm for Real-Time Gesture-To-Word Decoding  
The proposed SPP based Compact DCNN will give proper results for hand gestures in real-time. However, to the best of our knowledge, a practical video to gesture decoding algorithm with SPP based DCNN has not yet been properly researched. In this subsection, we have devised a novel practical approach to decode the hand gesture to the consecutive algorithm in real-time. Algorithm 1 has shown the approach for decoding gesture(s) video in real-time.  
```
0: Hand gesture video or image frames set
0: Decoded alphabets instruction set \(\begin{aligned} \textbf{BEGIN}\end{aligned}\)
```
0: Process the video for the given frames per second (fps)
0: Transform the images as 2D array \((M,N)\) where \(M\) is fps and the N will be the instruction/word length (images)
0: Apply the Proposed Compact Spatial Pyramid Pooling Deep  Convolutional Neural Network to transform each images
0: Column wise Majority Selection
0: Return array of \(n\)-sized output instruction  END ```  
**Algorithm 1** Hand Gesture Decoding Algorithm  
The ASL sign language has 29 distinct classes denoting all of the English alphabet A to Z, along with _space_, _del_, and _nothing_ characters gesture for additional support. The gestures set can be mathematically computed as a permutation problem with Equation (5),  
\[P(n,r)~{}=~{}\frac{n!}{(n-r)!} \tag{5}\]  
Here, \(n\) is the total class of 29 gestures and \(r\) is the selected sets. Giving the input into Equation (5) gives a total of 570,024 usable gestures for various instructions. Another important step of the gesture is the frame per second (fps) processing. The modern video camera has the standard of 30 fps in real life. Accordingly, the input video containing a 4 instruction set will have 120 frames to decode. It transforms the images into a \(2D\) array of \((M,N)\) dimensions and then applies the proposed compact SPP-DCNN to transform each image into a corresponding gesture alphabet label. At this part of the algorithm, the majority member selection was done based on the median operator applied in the predicted array, as shown in Equation (6)  
\[\begin{aligned} \text{Majority}(x_{1},x_{2},\dots,x_{n})~{}=~{} \left\lfloor\frac{1}{2}+\frac{(\sum_{i=1}^{n}x_{i})-1/2}{n}\right\rfloor\end{aligned} \tag{6}\]  
Here, \(x_{1},x_{2},\dots,x_{n}\) denotes the decoded label in each form of the array. Now, as a simple example of 5 fps gesture of \(\langle A,B,C,D\rangle\) can produce \([0,0,0,1]\) for the first row of data with the SPP-DCNN with 95% accuracy. However, the proposed majority member selection will change the result into the correct gesture based on the majority member in that row. This practical approach makes this proposed algorithm extremely effective for practical gesture decoding. A detailed accuracy analysis from this proposed algorithm has been discussed in Section 7 for more clarification.