esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To reach this goal we formulate a disk condition that says that the output mesh should have a topological disk formed by triangles around each vertex. This disk condition drives the refinement, that is, we go on refining protecting balls or surface meshes if this disk condition fails. If this refinement terminates, the disk condition necessarily holds for the output mesh. Then, by PL topology, the output Delaunay mesh restricted to each manifold surface patch is a manifold. Furthermore, the input incidence structure among different elements in the PSC is maintained. The output may not be homeomorphic to the input since a small feature such as a small handle may not be detected at the length scale the algorithm is asked to operate. However, a homeomorphic meshing is guaranteed when the supplied scale is sufficiently small. Actually, in practice, the disk condition usually suffices to provide a homeomorphism. One of our main tasks is to prove that the refinement always terminates. To this end we use the following result. If the protecting balls are sufficiently small and satisfy some _separation_ properties (conditions C1-C3 in Section 2. ), then the disk condition holds if the restricted Delaunay triangles are sufficiently small. Therefore, the failure of the disk condition signals either balls do not satisfy separation properties, or are large, or triangles are large. The way we compute the protecting balls, failure of separation properties also implies that they are large. In essence, if the disk condition fails, either a ball or a triangle is too large. The algorithm refines the larger of the two and hence guarantees that neither a protecting ball nor a triangle gets arbitrarily small ensuring termination. One may observe that, this strategy does not allow adaptive mesh sizing. However, one may regulate the input scale to produce meshes at different levels of resolution. Perhaps the most important ingredient in this approach is to maintain a set of protecting balls with the separation properties. It turns out that it is difficult to ensure one of these properties exactly (condition C3) when the balls are large. Instead we maintain a more relaxed condition which implies the desired property when the balls are sufficiently small. We have implemented our algorithm in this paper. In an earlier attempt we tried to mesh with the disk condition but pre-computed the balls with a small radius chosen heuristically [15]. The code failed in cases where the pre-selected size of the balls was wrong. The approach in this paper allows the refinement algorithm to determine the balls automatically instead of pre-computing them. We report experimental results for our protection algorithm and meshing in Section 5. The code and a video explaining the experimental results have been released [16].
# Covert Communications in Active-RIS-Aided NOMA Systems: Element Grouping or Not?
## 5 Conclusions  
In this paper, we proposed the w/o-EG and w-EG schemes to enhance the covert communication performance of an ARIS-aided uplink NOMA system. To reveal the impacts of element grouping on the covert communication performance of an ARIS-aided NOMA system, we formulated the covert communication rate maximization problem by jointly optimizing the NOMA transmit power, ARIS beamforming, and receive beamforming. To tackle the complicated non-concave objective function and highly coupled non-convex constraints, the original optimization problem was decoupled into the sub-problems of the NOMA transmit power allocation, ARIS beamforming, and receive beamforming, respectively. A closed-form expression for the optimal NOMA transmit power was derived. An AGM and penalized Dinkelbach transform based AO algorithm was proposed to tackle the mixed-integer non-linear programming for the element grouping-assisted covert communication rate maximization problem. The numerical results showed that while element grouping improved the covert communication performance, a decreased performance was observed compared to the scheme without employing element grouping. The simulation results also revealed that the number of elements in the grouping affected the trade-off in covert communication performance between the w/o-EG and w-EG schemes.  
**Author Contributions:** Methodology, X.K., X.P. and H.L.; Validation, X.K. and F.L.; Formal analysis, X.K.; Resources, M.Z. and X.P.; Data curation, F.L. and M.Z.; Writing--original draft, X.K., H.L. and H.Y.; Writing--review & editing, X.K., H.L. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant 62071202.  
**Data Availability Statement:** Data available in a publicly accessible repository.  
**Conflicts of Interest:** The authors claim that there are no conflicts of interest.
## 5 Application of Information Transmission  
Other than in the civilian field, silent information transmission has received increasing attention in special fields where stealth is a necessity. For example, near-field frogmen need to exchange information with their colleagues, as well as remote-operated vehicles or bases in the sea without being detected (Figure 9a). As is well known, Morse code is an effective way to transmit information, and it has been used for well over a century. Different Latin alphabets are represented by different configurations of \"dot\" and \"dash\" symbols (Figure 9b). Based on the contact-induced ionic current-generating system and Morse code, we proposed a wireless self-powered device that can silently transmit information in underwater conditions via a modulating decoder to translate the ionic current signals into intelligible letters. The device comprises a signal modulator, an LCD display, a portable lithium battery, a copper working electrode and a copper sensing electrode. In addition, an aluminum sheet and a platinum sheet are secured onto the surface of the gloves. It is therefore possible to generate the ionic current using only three fingers on one hand.  
Figure 9: _Cont._The working principle of the system is as follows. Firstly, we define the positive ionic current pulse generated by contacting the aluminum sheet as the \"dash\" and the negative ionic current pulse generated by the platinum sheet as the \"dot\", as shown in Figure 9c. For example, the letters S and O are generated by consecutively contacting the platinum sheet and the aluminum sheet three times with the copper working electrode, respectively. Then, messages such as \"SOS\" and \"UP\" (Figure 9d) are expressed by contacting the aluminum or the platinum sheets representing \"dash\" or \"dot\" in appropriate sequence and displayed on the screen of the decoder, respectively (Figure 9e). Other information, such as \"GO\" and \"HELP\", can also be conveyed by simply contacting the two fingers with metal sheets in underwater conditions using the same process. Therefore, silent information transmission can be achieved between vehicles and scuba divers.
## 3 Results
### Fraction of Vegetation Cover  
The performance of the top five fCover prediction models, using data derived from both the PlanetScope and MicaSense sensors, and the scatter plot of the best model are shown in Figure 5. The PlanetScope model, using the Enhanced Vegetation Index (EVI), showed a correlation of R\({}^{2}\) = 0.68 with the measured fCover values. The standard deviation between the different treatments was relatively small, indicating limited variability in the predictions. The MicaSense model, using the Normalized Difference Vegetation Index (NDVI), showed a higher correlation with measured fCover values (R\({}^{2}\) = 0.82, RMSE = 8.3%). The standard deviation in PlanetScope between the different treatments indicates limited sensitivity to the variations induced by different agronomic practices, such as fertilization with phosphorus and nitrogen. This lack of variability in PlanetScope predictions is probably due to its coarser spatial resolution, which may not capture the heterogeneity present in the experimental plots. The MicaSense model also showed greater variability in both measured and predicted values, as indicated by the larger standard deviations across the treatments. This increased variability likely to reflect the higher spatial resolution of the MicaSense sensor, which is better able to capture the finer scale variations in crop cover caused by different treatments. The improved agreement and variability in MicaSense  
Figure 4: Planetscope NDVI time series per each experiment.  
predictions suggests that it may be more effective for monitoring fCover in heterogeneous experimental conditions.  
Time series analysis of fCover, based on PlanetScope Enhanced Vegetation Index (EVI), shows the seasonal dynamics of vegetation cover across the different experimental treatments (Supplementary Material: Figure S4). The control treatment (plot 00, see Table 1), which did not receive fertilization, consistently shows the lowest fCover values throughout the growing season, indicating minimal vegetative growth. In contrast, the phosphorus and nitrogen fertilized treatments show varying degrees of increased vegetation cover. For example, treatments with P(MAP) + N(Urea) (plot 11) and P(Str + C) + N(Urea) (plot 42) show higher fCover values, especially during the peak of the growing season.
## 3 Results
### Mechanism for Data Collection  
For data collection, various sources can be used to determine the factors with the greatest influence on the implementation and use of eCommerce. Data sources are varied and depend on the type of analysis to be carried out; for example, social networks are current media that generate large volumes of data [36]. However, the extraction process requires more knowledge and trained personnel for the implementation of the analysis structure [37].  
Companies that start an eCommerce integration process can choose to generate their own data sources, with surveys being among the fastest mechanisms for the generation of data and the identification of the degree of customer satisfaction. In this work, a survey model was developed that included questions related to the factors that affected the consumer in the use of digital channels and eCommerce, for example:  
* Consumer experience with online stores;
* Accessibility;  
Figure 2: Flowchart of methodology for the analysis of the indicators that affect the use of eCommerce.  
* Catalogs;
* Compliance;
* Privacy;
* Compensation;
* Contact;
* Medium;
* Satisfaction;
* Loyalty;
* Repurchase intention;
* Shipment;
* Perceived value.  
In addition to these factors, it was necessary to identify information that allows companies to profile consumers in terms of gender, age, academic level, experience in online shopping, number of online purchases in each period, etc. These factors were added as variables in the analysis to help companies to segment their population according to their line of business.  
Surveys seek to determine the level of acceptance of a brand, product, or service. Generally, survey questions are designed to elicit as much real information from consumers as possible, so they need to be clear, direct, and objective. The response model was presented by levels of acceptance or rejection; in this way, the respondent did not fall in a state of annoyance or discomfort that punished the veracity of the answers. Below are the questions used in the survey, where 1 means \"Totally disagree\" and 5 means \"Totally agree\":  
* It is easy to find what I need on the website [1; 2; 3; 4; 5];
* It is easy to access any section of the website [1; 2; 3; 4; 5];
* The website allows me to complete a transaction quickly [1; 2; 3; 4; 5];
* The information on the website is well organized [1; 2; 3; 4; 5];
* Pages download quickly [1; 2; 3; 4; 5];
* The website is easy to use [1; 2; 3; 4; 5].
# A Multi-Level Optimization Method for Elastic Constants Identification of Composite Laminates
## 5 Experimental Study
The load-strain relation of the laminate was constructed to determine the strains for any given load and the elastic constants of the constituent material. The measured axial, lateral, and 45\({}^{\circ}\) strains of the laminates for axial load \(F_{x}\) = 0.5 kN are  
\[ \begin{array}{l}\varepsilon_{x}{}^{*}(\mu\varepsilon)=\ 208.57\ (0.91\%)\\ \varepsilon_{y}{}^{*}(\mu\varepsilon)=\ -149.89\ (1.26\%)\\ \varepsilon_{45}{}^{*}(\mu\varepsilon)=\ 28.86\ (1.64\%)\\ \end{array}  \tag{22}\]  
For comparison, the \"actual\" elastic constants were used in Equations (16)-(18) to calculate the \"actual\" strains. The absolute percentage differences between the \"actual\" and measured strains, as shown in the parentheses, are given in the above equation. It is noted that the deviations of the measured strains from the \"actual\" ones are likely due to the existence of uncertainties in material properties, fabrication of specimens, strain measurement, etc. These uncertainties and their effects should be studied in detail in the future. The measured strains were then used in the proposed multi-level optimization method to identify the elastic constants. The identified elastic constants and the percentage differences between the \"actual\" and identified elastic constants are given as  
\[ \begin{array}{l}E_{1}(GPa)=140.10(1.15\%)\\ E_{2}(GPa)=8.093(0.086\%)\\ v_{12}=0.302(0.026\%)\\ G_{12}(GPa)=5.185(0.29\%)\\ \end{array}  \tag{23}\]  
It is noted that, even though the accuracy of the measured strains was affected by uncertainties, the proposed multi-level method could still produce excellent results with absolute percentage differences less than or equal to 1.15%. Furthermore, it is worthy to note that, if the constraint term of \((\varepsilon_{45}^{*}-\varepsilon_{45})^{2}\) is not included in the objective function of Equation (19), the percentage differences between the \"actual\" and identified elastic constants might exceed 20%. Therefore, the strain constraint term contributed by making the elastic constants refrain from having large deviations from the actual values. The excellent results obtained for the [0\({}^{\circ}\)/\(\pm\)45\({}^{\circ}\)]\({}_{\mathrm{s}}\) laminate might lead to the possibility of having a new testing procedure for elastic constants identification in the future. However, the uncertainties involved in laminate fabrication and strain measurement should be characterized and their effects on the elastic constants analyzed before this new testing procedure can be standardized.
# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 2 Materials and Methods
### Feature Extraction
#### 2.4.1 Power Spectral Density  
Power spectral density (PSD) is a spectral analysis method extensively employed in EEG signal processing, which can depict the energy distribution across various frequency components within the signal [24]. The PSD is a widely employed method in EEG signal processing that elucidates the energy distribution across various frequency components of a signal. This analysis allows for the extraction of salient neural activity features from EEG signals, enabling researchers to identify prominent frequency components. This information is instrumental for investigating the relationships between EEG signals and neural activity, thereby improving the identification and classification capabilities of BCI systems.  
In the context of MBPPE, the Welch method is employed for PSD computation, demonstrating superior performance in PSD estimation [24]. To ensure frequency resolution, the original signal is divided into \(N\) overlapping segments of specified length to ensure precise frequency resolution. To mitigate the impact of abrupt changes at the signal boundaries on the spectral analysis, we apply a window function to each segment, which effectively smooths the signal. The Welch method, by averaging the spectra of these overlapping windows, excels in reducing noise and random fluctuations, thus boosting the stability of the PSD estimation. Additionally, we apply a logarithmic transformation to the Welch method's output, refining data visualization by compressing the signal's dynamic range.
# FollowMe: One Social Importance-Based Collaborative Scheme in MONs
## 5 Application  
In this section, we integrate FollowMe with the classical opportunity routing algorithms PRoPHET [20] and SimBet [21]. Here, we continue using node \(x,y\) as examples. Suppose node \(x\) encounters node \(y\), node \(x\) sends its signal to node \(y\), and the latter then runs FollowMe to determine its action. If both of them are cooperative, then node \(x\) runs the corresponding protocol to determine whether it forwards packets to node \(y\). The specific process is shown in Algorithm 2.  
```
1:for each node \(x,y\in V\)do
2:if\(x\) and \(y\) are within the communication range and \(x\) is the sender of the packet then
3: run FollowMe
4:if both of them want to receive and forward packets then
5:for any packets to be sent to \(y\)do
6:if\(y\) is the destination node of the packet then
7:\(y\) receives and stores the packet in the buffer
8:elseif\(y\) is the more appropriate carrier for the packet than \(x\)then
9:\(y\) receives the packet and stores it in the buffer
10:endif
11:endfor
12:endif
13:endif
14:endfor
```  
**Algorithm 2**Process of applying FollowMe to opportunity routing algorithm.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 3 Soundfield Recording and Reproduction
### 3D Soundfield Recording
#### 3.2.2 Plannar Microphone Array  
Planar microphone arrays are widely used for applications such as beamforming and circular harmonic analysis of 2D spatial sound. However, generally speaking, due to the limitation of the 2D geometry, a single planar array cannot capture full 3D spatial sound.  
A special planar microphone array configuration that is capable of recording 3D spatial sound was proposed by [PERSON] et al. [92], as shown in Figure 3c. In this configuration, the combined use of omni-directional and vertically placed first order (cardioid or differential) microphones enables detection of the acoustic particle velocity in the vertical direction, which can be used to solve for the spatial soundfield components that are normally \"invisible\" to planar microphone arrays. It is shown that this planar microphone array offers the same capability as a spherical microphone array of the same radius, in terms of spatial sound recording.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation
### Wi-Fi Wireless Network
#### 8.1.5 Connecting the Two Networks  
The initial design proposes the establishment of a dedicated point-to-point link between the two tanks using high-gain directional antennas. However, the distance between the two tanks is considerable, nearly 7 km, causing the Earth's curvature to significantly obstruct the Fresnel zone, compromising the integrity of the connection. To solve this issue, a taller structure is needed to have a clear Fresnel zone.  
However, considering the positioning of the wells shown in Figure 18, it is notable that well w-658, which is connected to the secondary tank, and well T/1/235/0/96, connected to the main tank, are aligned with each other. This alignment presents an opportunity for these wells to act as a bridge between the two networks.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sector/Link**} & **Distance** & **Antenna Type** & **Antenna Type** & **TX Power** & **Antenna** & **RSSI** & **Fresnel** & \multirow{2}{*}{**Status**} \\  & **(km)** & **(Base)** & **(Remote Node)** & **(dBm)** & **(dBm)** & **Margin** & **(dB)** & **(\%)** & \\ \hline Secondary & & & & & & & & \\ Tank & & ANT795-6M & & & & & & \\ \hline w-658 & 0.89 & ANT795-6M & ANT793-8 DK & 13 & 5 & 10.4 & \(\sim\)80 & Feasible \\ \hline ww7 & 0.96 & ANT795-6M & ANT793-8 DK & 11 & 5 & 10.4 & \(\sim\)80 & Feasible \\ \hline ww3 & 4.02 & ANT795-6M & ANT793-8 DK & 17 & 5 & 10 & \(\sim\)80 & Feasible \\ \hline \hline \end{tabular}
\end{table}
Table 7: Wi-Fi network Link Parameters for Secondary Tank.  
Figure 17: Transmit Power vs. RSSI for wells connected to the secondary tank at various distances.  
By utilizing directional antennas with a Tx power of 10 dB and an antenna height of only 5 m, an RSSI margin of 10.9 dB is achieved, which exceeds the required threshold Table 8 shows the final link parameters. This approach eliminates the need for a taller structure and strategically uses the water network structure.
## 4 Performance Evaluation
### Tcn-Ts-SW Forecasting
The reason behind choosing a low epoch number is that since we are considering a real time application, the execution time would be the highest priority in designing the this structure in return, the accuracy is compromised. For further validation, the accuracy of the proposed system is evaluated by the RMSE, MAE and \(R^{2}\) score metrices summarized in Table 4. The window size variation of \(\Delta=300\) display the best setup for a single stage sliding window technique is observed in the data ratio of 0.6. However, it is observed to have a negative \(R^{2}\) score and this event can be traced back to the data presented on this model. The nature of the time-series subjected to our proposed model; TCN-TS-SW, is an irregular trend that does not include any seasonality or uniformity. It consist of gross outliers that could contaminate the fairness while computing the \(R^{2}\) resulting a negative value on the final output. Additionally, the decremented of accuracy is caused by uncertain noises and dynamic variance of input data. Figure 6: _Cont_. Meanwhile for \(\Delta=400\) can be seen on the Figure 7 the alongside comparison of the actual data versus the predicted one. The forecasting module of the proposed TNN-TS-SW performs better compared to the \(\Delta=300\) since there are relatively more training dataset involve during the training procedure. For further validation, the accuracy of the proposed system is evaluated by the RMSE, MAE and \(R^{2}\) score metrices summarized in Table 4. It shows that the best setup for a single stage sliding window technique is observed in a starting ratio of 0.6 and a forecast horizon of 161 elements. However, it also have a low \(R^{2}\) score similar to the result from the first scenario hence, it also implicate a huge variance between the actual and predicted value hence, the result of having a negative \(R^{2}\) value is due to the absence of constant term on the (4) as further explained on [40,41]. Figure 6: The plotted result between the actual and predicted values. The network has a \(\Delta=300\) with a training/validation ratio of 70/30. The x-axis represents the time of the execution, and the y-axis represents the voltage (Volts) that represents the raw readings from the temperature sensor. Figure 7: The plotted result between the actual and predicted values. The network has a \(\Delta=400\) with a training/validation ratio of 70/30. The x-axis represents the time of the execution, and the y-axis represents the voltage (Volts) that represents the raw readings from the temperature sensor.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design
#### 3.1.3 Independent Variables  
Study 1 explored the impact of different feedback modes on street-crossing decision-making. The experiment included three independent variables, focusing on the effects of haptic and visual feedback. The specific independent variables are as follows:  
* Feedback modality (two levels): haptic feedback and visual feedback.
* Feedback frequency (four levels): 0.5 Hz, 1.0 Hz, 2.0 Hz, and 4.0 Hz, as shown in Figure 2.
* Feedback size (three levels): small, medium, and large, as shown in Figure 3.  
Figure 1: Overview of haptic and visual feedback systems.