esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A numerical study, using a simplified and validated 2D numerical model, looking at the impact of the PCM thermal conductivity on the system equivalent power is presented in this work. The results show that with a substantial thermal conductivity increase of 100% (doubling the thermal conductivity), in the range of some of the best results presented when using nanoparticles, for example, the overall system power only increases by approximately 30% at best. From the power vs. time results, it can be concluded that this increase in the PCM thermal conductivity barely affects the overall system discharge history. Increases in the PCM thermal conductivity of at least 500% to 1000% are required to substantially change the discharging power history, with an ultimate power increase up to 400% for a 5000% increase in thermal conductivity.  
It is important to keep in mind that the numerical model used for this analysis is simplified, using a 2D approach to capture the important behaviors of a more complex 3D system. Although the validation shows the similarities in the captured behaviors, it is not perfect, so additional processes might play different roles as the PCM thermal conductivity is increased. It is equally important to keep in mind that this analysis is based on a given system, with a given geometry and operating conditions; the impact of the PCM conductivity could be different for other systems. As mentioned in the introduction, the answer to the  
Figure 11: System equivalent power over time as a function of the PCM: (**a**) solid thermal conductivity from \(k_{s}\) to 10\(k_{s}\) (\(k_{l}\) constant) and (**b**) liquid thermal conductivity from \(k_{l}\) to 10\(k_{l}\) (\(k_{s}\) constant) for simulation with 12 fins, 9 LPM, and \(T_{l}=65\,^{\circ}\mathrm{C}\) to \(T_{f}=21\,^{\circ}\mathrm{C}\).  
simple question related to the impact of the PCM thermal conductivity requires complex work because of the dependence of the heat transfer rates during phase change on much more than that one PCM properties. Additional analysis from the community should look at the impact of the PCM thermal conductivity on other systems, as well as the impact that modifying the thermal conductivity might have on other PCM thermophysical properties like the latent heat, density, and specific heat.  
Conceptualization, D.G.; methodology, M.B. and D.G.; validation, M.B.; formal analysis, M.B.; investigation, M.B.; writing--original draft preparation, M.B.; writing--review and editing, D.G.; visualization, M.B.; supervision, D.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science and Engineering Research Council (NSERC) of Canada through an Undergraduate Student Research Award (USRA) awarded to M.B.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 5 Simulation and Analysis
### The Impact of Spread-Spectrum Sequence Depth on Position-Detection Precision
Additionally, an analysis of the performance of the QD when detecting the position of the light spot under the same received optical power is presented in Table 4.  
Table 4 presents the accuracy of position detection under three different spread-spectrum gain settings, as well as the experimental and theoretical improvements in the precision of position detection under each spread-spectrum gain. When the received optical power of the received signal remains constant, it is evident that the position-detection precision achieved by the direct detection system \(\sigma_{x}\) is significantly lower than that of the spread-spectrum detection system. Furthermore, as the spreading gain increases, the improvement in the position-detection precision achieved by the system becomes more pronounced. The gain in SNR achieved via the use of spread-spectrum technology effectively enhances the system's detection precision. When the received optical power at the QD is \(-\)37 dBm, the \(\sigma_{x}\) for spreading depths of 1023, 127, and 31 is 0.983 \(\upmu\)m, 2.876 \(\upmu\)m, and 7.275 \(\upmu\)m, respectively. This represents improvements of 14.96 dB, 10.29 dB, and 6.26 dB compared to the position-detection precision achieved by the direct method, which is 30.811 \(\upmu\)m. When comparing the theoretical values provided in the table, it is evident that the spreading gain obtained by the spread spectrum is slightly lower than the theoretical calculation due to the influence of the phase-locking performance of the digital phase-locked loop. Additionally, \(\sigma_{x}\) decreases as the depth of the modulation PRN increases, resulting in an increase in the position-detection precision; this aligns with the results of the theoretical simulation analysis.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
### Contributions and Outline
#### 1.2.4 Distributed Signaling  
The centralized routing model described above is complemented by a distributed signaling process where a signaling tree rooted at the sink/gateway is used to: (1) transport the nodes' information to the sink for topology discovery, path finding and routing table computation; and (2) disseminate the network provisioning information from the gateway to the nodes for routing table configuration. The signaling tree is built using an energy-frugal distributed algorithm.  
The works most closely related to ours are found in [11; 12; 13; 14]. The work in [14] considers a node's service differentiation model similar to ours, but uses a flat topology, which is based on node weight differentiation during path selection. This differs from our model, which is based on hierarchical routing using service-aware clustering. While considering a centralized clustering scheme similar to ours, the works in [11; 12; 13] are not based on a service awareness/differentiation model similar to ours.  
The remainder of this paper is organized as follows: Section 2 describes the energy-efficient IoT model, formulates the service-aware clustering (SAC) problem, presents its algorithmic solution and derives formulas for the optimal number of clusters' computation. The experimental results are presented in Section 3, while Section 4 contains our conclusions and draws some directions for future work.
# Experimental Study on Chromaticity Control in Visible Light Communication Systems
## 5 Conclusions  
This article investigates the control chromaticity in light sources for visible light communication systems. LED white light sources with adjustable color temperatures were obtained through simulation and experiments. When the error rate of each color temperature is below 3.8 \(\times\) 10\({}^{-3}\), the modulation bandwidth of the system reaches 11.7 MHz, the longest communication distance is 5 m, and the illumination intensity is above 400 lx. Thus, both the communication and illumination functions of visible light are achieved, and the following conclusions are obtained:  
1. The light source with different color temperatures can be obtained by changing the ratio of red, green, and blue LEDs. The general color index of the light source with a color temperature between 5500 K and 6500 K is the best. The influence of color temperature on the illumination intensity of the receiving surface at the same distance can be ignored; the distance has a significant impact on the illumination of the receiving surface, as the distance increases. The light intensity rapidly decreases.
2. The optical power under different color temperatures and distances is measured, and it is found that the color temperature has a small impact on the receiving power value, while the distance has a greater impact. With the increase in distance, the optical power value decreases in the exponential decay trend.
3. Based on communication experiments under different color temperatures, it was concluded that the communication performance of the system is better in the high color temperature range and significantly better than in the low color temperature range. As the distance increases, the communication error rate under different color temperatures continues to increase.  
**Author Contributions:** Conceptualization, X.K.; methodology, X.K. and X.W.; software, X.W.; validation, J.L., X.W. and X.K.; formal analysis, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W.; visualization, H.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Funding was received from the following: The Key Industrial Innovation Chain Project of Shaanxi Province [grant number 2017 ZDCXL-GY-06-01]; the General Project of National Natural Science Foundation of China [grant number 61377080]; the Xi'an Science and Technology Plan (22 GXFW0115).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Binary-Tree-Fed Mixnet: An Efficient Symmetric Encryption Solution
## 1 Introduction
The purpose of this work has been overcoming the drawbacks of ESEBM and improving its throughput. In this regard, the new scheme presented here, called binary-tree-fed mixnets or BTFM, does not require any identifiers and eliminates the need of searching in tables. The outcome is a faster mixnet without overloading messages with superfluous headers. Additionally, it can stand with a higher number of adversaries. The major contribution is a starting phase where keys are established by means of a binary graph. Then, the production phase is faster than in any other mixnet. This new idea of working with binary graphs solves the problem of how to share keys anonymously without using asymmetric encryption and is a new tool to apply independently in other contexts. In summary, the main contributions of this paper are  
1. A new tool for sharing keys anonymously without asymmetric encryption. Users can send keys or messages to a server while preserving their privacy. This is achieved using a binary graph where only permutations and XOR operations are required. 2. The development of a new mixnet scheme, BTFM, which is based solely on symmetric encryption. Its main attributes are improvements in performance and security. 3. The discovery of some vulnerabilities in a previous symmetric encryption mixnet scheme, ESEBM. 4. A security study of BTFM, where the minimum threshold of adversaries necessary to violate privacy is determined. 5. The comparison of BTFM with previous schemes, carried out through a series of experiments. The paper is structured as follows. The next section is devoted to ESEBM, describes it, and shows some vulnerabilities. Then, BTFM is detailed in Section 3. The security analysis follows in Section 4. Section 5 shows and discusses the results of some experiments. The last section presents our conclusions.
# A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients
## 2 Methods  
### Machine Learning Model  
The primary aim of this research was to build a generative model for simulating the clinical trajectories of patients who went on to experience an ischemic stroke. A model is generative if new samples can be drawn from it; generative modeling for clinical data involves randomly generating patient data with identical statistics as the real patient data, in addition to simulating the evolution of these trajectories over time. Patient trajectoriesfor a condition are inherently random due to unobservable factors, such as the patient's environment and history. As such, accurate forecasting of progression of clinical measures should be able to account for this random nature.  
To address this randomness, we utilized a variational autoencoder (VAE) architecture for our disease progression model [15]. VAEs encode input data as a probability distribution over a generative latent space, which is then used to regenerate the original data. By sampling a latent vector from this distribution and decoding it, we created a random reconstruction close to the original input data.  
VAEs maximize a lower bound to the marginal log-likelihood of observing the input data given the model parameters, called the evidence lower bound [16]. This allows for tractable optimization of the model's distribution without having to compute integrals and/or costly normalization constants [16]. In this way VAEs are more efficient and flexible than restricted Boltzmann machines (RBMs).  
Our particular variation of the base VAE architecture was a \(\beta\)-VAE [16]. The \(\beta\)-VAE was an extension of a VAE in which regularization of the conditional latent distribution was controlled with a specified weight, \(\beta\). Setting \(\beta=1\) yielded the original formulation of the VAE, while larger values of \(\beta\) encouraged the generative latent variables to be more independent of one another. This led to a more \"disentangled\" representation of the data which better represented the influencing variables and enhanced the fidelity of modeling.  
Additionally, in terms of the effectiveness of the data science methods, the novel use of a variational autoencoder (as opposed to other generative models such as generative adversarial networks (GANs or RBMs), allows for stable learning and efficient sampling of digital twins from patient electronic health records (EHRs). In addition, the VAE's disentangled latent space could allow for development of more sophisticated models which incorporate not only forecasting but prediction using the latent space's distilled information in further work. This is in contrast to GANs, which do not learn a posterior distribution over a latent space and therefore cannot easily be incorporated directly into a predictive pipeline. RBMs do have a latent space, however the inherent learning and sampling mechanisms are costly, and the model architecture is inflexible as compared to VAEs.
## 2 Materials and Methods
### Procedure
Figure 4: Reflectance according to the spectral region and wavelengths calculated on the study plots by means of a ASD FieldSpec 4 Hi-Res NG Spectroradiometer, Malvern Panalytical Ltd, Cambridge, UK (n = 50 per land use: potatoes, bare soil and pasture). Source: Current research. Figure 3: UAV flight over the study plots of pasture, potatoes, and bare soil. (**a**) UAV hexacopter on the pasture study plot, (**b**) potatoes study site, (**c**) Topcon submetric device on bare soil study site. The climatological information for the month prior to the sampling for the field experiment was collected at the EPM meteorological station located 450 m from the study plots, considering rainfall, temperature, and wind as influent parameters. Optical Planet Scope 3m resolution images in four bands (R, G, B and NIR) were obtained for the same week as the ground measurements were taken. The images used were divided by bands and subsequently multiplied by the reflectance coefficient to convert the Digital number radiance, rescaled into an 8-bit digital number (DN) with a range between 0 and 255, into Top of Atmosphere (TOA) Reflectance. The vegetation indices were computed using both the UAV and the planet scope images. According to the literature, the NDVI is defined as Equation (1), NDWI (Equation (2)), SAVI (Equation (3)) and PDI (perpendicular drought index) (Equation (4))  
\[\text{NDVI}=(\text{NIR}-\text{R})/(\text{NIR}+\text{R}) \tag{1}\]  
\[\text{NDWI}=(\text{GREEN}-\text{NIR})/(\text{GREEN}+\text{NIR}) \tag{2}\]  
\[\text{SAVI}=[(\text{NIR}-\text{R})/(\text{NIR}+\text{R}+\text{L})]\ (1+\text{L}) \tag{3}\]  
\[\text{PDI}=1/\surd(\text{M}^{2}+1)(\text{R}-(\text{M}\times\text{NIR})) \tag{4}\]  
To determine the PDI, a soil line was built by means of red and NIR reflectivity correlation of pixels on bare soil, where red was the independent variable and NIR the dependent variable [30]. This drought index was compiled using spatial characteristics of the soil moisture in red and NIR feature spaces to assess soil moisture stress. M is the slope of the soil line in the red-NIR spectral feature space, forming one edge of the triangle in the NIR-red spectral space represented by the soil line (Figure 5). After extracting the pixel information from the spectral vegetation indices calculated from the UAV and satellite images, a regression analysis was carried out using the obtained field data.
# Suitability of Active Noise Barriers for Construction Sites
## 4 Results
### Target Area: Shadow Zone of the Facade  
More studies have simulated the effect of noise cancelation by the barrier on the facade of a tall building nearby a construction site. In this subsection, the shadow zone on the facade is considered as the target area to control the noise of construction activities. The height of the shadow zone on the facade depends on the distance of the barrier to the building and the distance of the noise source from the barrier (\(d_{pr}\)). In this study, the height of the shadow zone on the facade changes from 7 m for the noise source at \(d_{pr}=7\) m to the whole facade for \(d_{pr}=1\) m. Table 2 demonstrates the most suitable configurations of the transducers when the shadow zone of the facade is considered as the control target area.  
Figure 5 illustrates the average reduction in the shadow zone on the facade (\(\overline{IL}_{F,s}\)) for different positions of the secondary sources and error sensors described in Figure 4. In this figure, the target area is the shadow zone on the facade.  
Figure 4: \(\overline{IL}_{s}\) (dB) for different positions of **(i)** secondary sources and **(ii)** error microphones, in the control zone. The distance of the noise source from the barrier (\(d_{pr}\)) is **(a)**: 1 m, **(b)** 2.5 m, **(c)** 4 m, **(d)** 5.5 m, and **(e)** 7 m. The barrier is presented by the black bar, and \({}^{*}\) and \(\blacksquare\) represent the best positions of the control sources and error microphones, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(d_{pr}\) (m)} & \multirow{2}{*}{(\(X_{s}\), \(Z_{s}\)) (m)} & \multirow{2}{*}{(\(X_{e}\), \(Z_{e}\)) (m)} & & \multicolumn{2}{c}{\(\overline{IL}_{s}\) (dB)} & \multicolumn{2}{c}{\(\overline{IL}_{F.s}\) (dB)} \\ \cline{5-8}  & & & **ANB** & **PNB** & **ANB** & **PNB** & **ANB** & **PNB** \\ \hline
1 & (\(-\)0.5, 0) & (0.2, 2.3) & 11.8 & 14.9 & 22.0 & 10.8 & 22.0 & 10.8 \\
2.5 & (\(-\)0.5,1.9) & (0.5, 0.5) & 11.4 & 13.2 & 12.1 & 9.6 & 0.6 & 8.7 \\
4 & (\(-\)0.5,1.8) & (0.3, 0.3) & 10.3 & 12.3 & 9.7 & 9.5 & \(-\)0.5 & 8.3 \\
5.5 & (\(-\)0.5,2.3) & (0.5, 0.2) & 10.7 & 11.8 & 9.4 & 9.2 & 1.1 & 8.3 \\
7 & (\(-\)0.5,2.3) & (0.3,0.4) & 8.3 & 11.4 & 8.9 & 9.1 & 0.1 & 8.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The best positions of the secondary sources and error microphones in the control zone, when the target zone is the shadow zone of the facade. Variables are similar to Table 1.  
Figure 5: _Cont._Figure 5: \(\overline{IL}_{FS}\) (dB) for different positions of (**i**) secondary sources and (**ii**) error microphones in the control zone. The distance of the noise source (\(d_{pr}\)) is (**a**) 1 m, (**b**) 2.5 m, (**c**) 4 m, (**d**) 5.5 m, and (**e**) 7 m. * and **e** represent the best positions of the control sources and error microphones, respectively.
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 3 Preliminaries
### Grid Structure  
Before users request location-sharing services, the location-sharing system usually specifies a query area (e.g., a city or an administrative district). Therefore, the grid structure [23] can be used to divide a query area into a certain number of grid cells. By using the method of grid structure, a query area can be designed to a uniform structure. The uniform structure can be a square or a rectangle covering the entire query area, and consists of the grid cells with equal size. The size of each grid cell can be defined as \(S\times S\). Suppose that the lower-left and upper-right coordinate of a query area are denoted as \((x_{a},y_{a})\) and \((x_{b},y_{b})\), respectively, then the query area can be divided to \(n_{x}\times n_{y}\) grid cells with equal size and represented by Equation (1):  
\[\textit{Query area}\longleftarrow((x_{a},y_{a}),(x_{b},y_{b}),n_{x},n_{y}). \tag{1}\]  
To unify the grid structure, the lower-left and upper-right coordinates can be converted by Equation (2):  
\[\begin{cases}x_{c}&=~{}x_{a}-x_{a};\\ y_{c}&=~{}y_{a}-y_{a};\\ x_{d}&=~{}x_{b}-x_{a};\\ y_{d}&=~{}y_{b}-y_{a};\end{cases} \tag{2}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(ID\) & A user’s identifier. \\ \(PID\) & A user’s pseudo-identifier (pseudo-ID). \\ \(GID\) & A grid cell’s identifier. \\ \(GFID\) & A grid cell’s fake-identifier (fake-ID). \\ \(BID\) & A base station’s identifier. \\ \((x,y)\) & A location coordinate. \\ \(df\) & Threshold distance for friends. \\ \(ds\) & Threshold distance for strangers. \\ \(dis(i,j)\) & The distance comparison function to compute Euclidean distance from \(i\) to \(j\). \\ \(l\) & A user’s query distance. \\ \(ts\) & A time-stamp. \\ \(SecK_{\textit{B\&L}}\) & Location server’s secret key, shared with base stations. \\ \(SecK_{\textit{B\&S}}\) & Social network server’s secret key, shared with base stations. \\ \(SecS_{\textit{U}}\) & A user’s session key, shared with all his friends. \\ \(seq\) & A sequence number, generated by base stations. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of notations.  
Then, \(n_{x}~{}=~{}(x_{d}-x_{c})/S\) and \(n_{y}=(y_{d}-y_{c})/S\) can be calculated.  
In the uniform grid structure, each grid cell can be represented by a unique identifier. Herein, the identifier \(GID\) of each grid cell is represented by \((r_{i},c_{i})\), where \(r_{i}\) denotes the row identifier of Y-axis and \(c_{i}\) denotes the column identifier of X-axis. Figure 1a shows an example of determining the identifier of grid cell \(GID_{a}\) that base station \(B\) locates in. The identifier can be calculated by Equation (3) and the identifier of \(GID_{a}\) can be identified as (5, 5).  
\[\begin{cases}r_{b}~{}=~{}\frac{y_{b}-y_{a}}{(y_{d})/n_{y}}\\ c_{b}~{}=~{}\frac{x_{b}-x_{d}}{(x_{d})/n_{x}}\end{cases} \tag{3}\]