esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy and Security in Cognitive Cities: A Systematic Review
## 4 Discussion
### A Unified View  
The results of the backward search have revealed that the cognitive city paradigm emerges from the combination of a variety of fields and its development involves multiple technologies and paradigms, namely: cognitive computing, IoT, big data, cloud and fog computing, ML, natural language processing, multi-agent systems, etc. Although addressing the security and privacy issues of the underlying technologies is a starting point to understand their implications in the cognitive city arena, approaching these challenges from a technology basis only could be a mistake, because of the unexpected risks that arise as different technologies interact in different scenarios. Instead, we sustain that a holistic approach that takes into account the unique characteristics of cognitive cities is preferable. We advocate for a global Security by Default framework, especially designed for the cognitive city,  
1. that is built upon a formal description of the entities that constitute a cognitive city and their relations. This would require the creation of a cognitive city ontology.
2. that is based on a multi-layered model of the cognitive city, from the technological and agent layers, to the social interaction and city-level layers.
3. that encompasses all the stages in the life-cycle of cognitive products, processes, and services for the cognitive city, from the design and build phases, to the operation, monitoring, repairing, and disposal stages.
4. that takes into account the potential privacy and security risks that can arise in every phase and architecture layer.  
* that includes a new specific threat model for cognitive cities, which should include goals, threat actors, attack vectors, and fault trees.
* that includes metrics and risk assessment models to evaluate privacy and security at several levels (per individual, per building, per system, per district, etc.). These metrics should take into account the perspectives of several stakeholders, such as cognitive systems' manufacturers, infrastructures' providers, cities' councils, and citizens' fellowships, and balance their needs and requirements by using multi-criteria decision-making methods [79].  
Moreover, in a globalized world, an internationally-accepted certification framework (led by certification bodies) on the basis of real-life urban scenarios could be created, and standards might be developed. This certification process would be required along the life-cycle of any cognitive system, and would complement law enforcement by transnational regulations.
## 5 Results
### Effect of Integer Ambiguity-Fixing  
Considering the rather large orbit bias in the along-track direction of the GEO satellites and their poor tracking geometry, the double-differenced ambiguities of GEOs might not be fixed. Therefore, besides fixing ambiguities of all satellites, we also carried out ambiguity fixing of IGSOs and MEOs only to avoid any possible negative effect of GEOs.  
On average there are approximately one to two ambiguities for each station-satellite pair for GEOs over the three-day session while IGSO or MEO has two to three times more. For both scenarios, the fixing percentages are almost the same of about 80% after two iterations.  
For the scenario where all ambiguities are considered in the fixing procedure, satellite orbits become slightly worse than the free solutions in terms of the overlapping RMS. Unfortunately, we have not found any hint about the cause of this degradation and this topic thus remains under investigation. The major reason could be the poor tracking geometry that results in a large orbit bias in the along-track direction, up to several meters. Such orbit bias may contaminate ambiguities from this satellite to various stations but in a different way due to the different station locations. Consequently, the bias cannot be removed in the double-differenced ambiguities.  
In the second scenario where only ambiguities of IGSO and MEO satellites are considered, ambiguity fixing shows a positive contribution from the overlapping RMS listed in Table 8 with that of the free solution for comparison. Compared RMS of the free and fixed solutions, 3D-RMS of IGSOs and MEOs are improved by 30% and 6%, respectively. The largest improvement occurs in the along-track direction.
## 3 Network Overview  
### GAN Model  
The GAN model includes a generative model G and a discriminative model D, and the composition of G and D can be a convolutional neural network (CNN) or a Multi-Layer Perceptron (MLP) network, as shown in Figure 1.  
The generative model G gets a generated sample \(X_{fake}=G(z)\) by inputting a random signal \(z\) that match a certain distribution, or an additional control variable \(c\). The discriminative model D obtains a probability value by distinguishing a generated sample from a real one, and the loss function of the G and D model are as follows:  
\[L_{D}=E_{z}[log(1-D(X_{fake}))]+E_{X}[logD(X_{real})] \tag{1}\]  
\[L_{G}=E_{z}[log(1-D(X_{fake}))] \tag{2}\]  
Here, D is trained to maximize \(L_{D}\) while G is trained to minimize \(L_{G}\). In the physical sense, the Equation (1) is that the D model learns to distinguish between real samples and fake samples, and Equation (2) aims that a generated fake sample by the G model is more and more difficult to distinguish. At last, these two models reach a balance.  
Figure 1: GAN model.  
In fact, the GAN may often occur mode collapse, which means the generative samples does not match with the characteristics of the training set, even far away. In order to achieve the balance of G and D, the loss function for the two models should keep balance until stable. [PERSON], [PERSON] et al. put forward the algorithm that the loss is based on the ordinary distance measurement of Kullback-Leibler (KL) divergence and Jensen-Shanno (JS) divergence [42]. Yet, this algorithm has some defects in the GAN application. Firstly, there are different loss degrees for the two models in training, which results in unbalance of them. Secondly, the feature space of the training samples and the generated samples may not coincide, so that the loss of the generator is close to zero, while the discriminator model can accurately identify the real and fake samples each time. All lead that the system cannot continue to train. In [44], the engineering method is used to improve the GAN. At the same time, the Wasserstein GAN (WGAN) algorithm is proposed in [45]. the Wasserstein distance takes the place of KL divergence, which makes models basically stable. Considering that the ultimate goal is to generate non-coincident models as much as possible, the WGAN algorithm still has a large loss when it is not coincident. In short, the OCC model of this paper is based on the Deep Convolutional GAN (DCGAN) [44].
## 3 Research Method
### Validity and Reliability Verification
#### 3.3.1 Validity Test of Evaluation Indicator System  
Assessing whether the index system can accurately reflect the objective reality requires the use of the validity test. Based on statistical theory, this study addressed the issues of subjective evaluation uncertainty and redundancy or lack of indicators by introducing the validity coefficient \(\beta\) as a tool for quantifying deviations and measuring the fit between the index system and the real scenario using expert scoring data. Compared to costly field measurements and machine learning approaches that rely heavily on data quality, validity tests achieve a balance between scientific rigor and engineering feasibility, demonstrating distinct methodological advantages. For the 10 key indicators established in the pre-screening, the validity coefficient test is used to conduct empirical research, and the specific modeling process is as follows:  
1. Data collection and standardization: Let the evaluation index system be \(Z=\{z_{1},\,z_{2},\,z_{3},\,\ldots,\,z_{n}\}\) containing a total of n indicators. Invite M experts to participate in independent scoring. According to the relative importance of the indicators, the experts need to assign scores to the indicators within the \([0,1]\) standardization interval, forming the scoring matrix \(X=\{x_{1j},\,x_{2j},\,x_{3j},\,\ldots,\,x_{nj}\}\), where \(x_{ij}\) indicates the scoring value of the jth expert for the indicator \(z_{i}\).
2. Calculation of the validity coefficient \(\beta_{i}\) of the single indicator: Define the validity coefficient for indicator \(z_{i}\) as: \[\beta_{i}=\sum_{j=1}^{s}\left|\frac{\overline{x_{i}}-x_{ij}}{MV}\right|\] (6) where \(V\) is the theoretical optimal value and \(x_{i}\) is the average score value of the evaluation indicator \(z_{i}\), which can be calculated using Equation (7): \[\overline{x_{i}}=\sum_{j=1}^{s}x_{ij}/M\] (7)
3. Global validity coefficient synthesis: Integrate the validity coefficients of each index through the arithmetic average method to get the overall validity coefficient \(\beta\) of the evaluation system: \[\beta=\sum_{j=1}^{n}\beta_{j}/n\] (8)
4. Validity determination criteria: The validity coefficient \(\beta\) is often used as a core parameter to measure the scientificity of the evaluation system, and when the \(\beta\) value is lower than the empirical threshold (routinely set at 0.15), the consensus of the expert group on the evaluation dimensions is significantly enhanced, and the validity of the indicator system in reflecting the objective reality is also enhanced.
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
###### Abstract  
The timing group delay parameter (TGD) or differential code bias parameter (DCB) is an important factor that affects the performance of GNSS basic services; therefore, TGD and DCB must be taken seriously. Moreover, the TGD parameter is modulated in the navigation message, taking into account the impact of TGD on the performance of the basic service. International GNSS Monitoring and Assessment System (iGMAS) provides the broadcast ephemeris with TGD parameter and the Chinese Academy of Science (CAS) provides DCB products. In this paper, the current available BDS-3 TGD and DCB parameters are firstly described in detail, and the relationship of TGD and DCB for BDS-3 is figured out. Then, correction models of BDS-3 TGD and DCB in standard point positioning (SPP) or precise point positioning (PPP) are given, which can be applied in various situations. For the effects of TGD and DCB in the SPP and PPP solution processes, all the signals from BDS-3 were researched, and the validity of TGD and DCB has been further verified. The experimental results show that the accuracy of B1I, B1C and B2a single-frequency SPP with TGD or DCB correction was improved by approximately 12-60%. TGD will not be considered for B3I single-frequency, because the broadcast satellite clock offset is based on the B3I as the reference signal. The positioning accuracy of B1I/B3I and B1C/B2a dual-frequency SPP showed that the improvement range for horizontal components is 60.2% to 74.4%, and the vertical components improved by about 50% after the modification of TGD and DCB. In addition, most of the uncorrected code biases are mostly absorbed into the receiver clock bias and other parameters for PPP, resulting in longer convergence time. The convergence time can be max increased by up to 50% when the DCB parameters are corrected. Consequently, the positioning accuracy can reach the centimeter level after convergence, but it is critical for PPP convergence time and receiver clock bias that the TGD and DCB correction be considered seriously.  
BDS-3; standard point positioning; precise point positioning; time group delay; differential code bias +
Footnote †: journal: Applied Sciences
# Endophytic Yeasts for the Biocontrol of _Phycetema vagabunda_ in Apples
## 2 Materials and Methods
### Selection and Identification of Yeasts as a Potential Biocontrol of P. vagabunda  
Nine isolates of the most frequently isolated yeast were evaluated as biocontrol agents against _P. vagabunda_. Apples were wounded in the equatorial axis (3 mm diameter and 3 mm deep) using a sterile pipette tip and inoculated with 20 \(\mu\)L of a yeast suspension (1 \(\times\) 10\({}^{9}\) cells mL\({}^{-1}\)). After 24 h, 20 \(\mu\)L of a _P. vagabunda_ suspension (5 \(\times\) 10\({}^{5}\) conidia mL\({}^{-1}\)) was inoculated [8]. In the control treatment, the yeast cell suspension was substituted for sterile distilled water. After 20 days at 20 \({}^{\circ}\)C, rot incidence and severity were recorded according to [PERSON] et al. [40].  
Yeast strains were identified by phylogenetic analyses of the D1/D2 domain of the 26S LSU of rRNA using the primers NL1 (5\({}^{\prime}\)-GCA TAT CAA TAA GCG GAGGAA AAG-3\({}^{\prime}\)) and NL4 (5\({}^{\prime}\)-GGT CCG TGT TTC AAGACG G-3\({}^{\prime}\)) in MEGA version 11. DNA sequences were aligned, together with sequences of homologous regions of closely related species retrieved from the GenBank. Evolutionary distances were computed using the Jukes-Cantor method, and phylogenetic trees were obtained by neighbor-joining. All positions containing alignment gaps and missing data were eliminated only in pairwise sequence comparisons. The stability of clades was assessed with 1000 bootstrap replications.  
Strain identification was confirmed by sequencing the 5.8S-ITS rDNA region using the primers ITS1 (5\({}^{\prime}\)-TCCGTAGGTGAACCTGCGGG-3\({}^{\prime}\)) and ITS4 (5\({}^{\prime}\)-TCCTCCGCTTATGATGC-3\({}^{\prime}\)) in Macrogen Inc. (Seoul, Korea). These sequences were then compared with those published in the GenBank database with the BLAST program [19].
## 3 Theory and Methodology
### The Proposed Tsallis-Entropy-Based Method
#### 3.2.2 The Proposed Cauchy-TB Method
where \(S\) represents the total set of pixels within the image, while \(\mathcal{F}(s)\) denotes the gray-level intensity of a pixel \(s\) belonging to \(S\). The sets \(S_{o,t}\) and \(S_{b,t}\) represent the sets of object and background pixels defined by threshold \(t\). Assume that the object and background pixel distributions follow Cauchy distributions, denoted by \(\mathrm{C}(\theta_{0}(t),x0,o(t))\) and \(\mathrm{C}(\theta b(t),x0,b(t))\), respectively, where \(\theta_{0}(t)\), \(\theta b(t)\), \(x0,o(t)\), and \(x0,b(t)\) are the scale and location parameters for object and background pixels, respectively, formulated as  
\[p_{o,t}(i)=\frac{1}{\pi}\bigg{[}\frac{\theta_{o}}{(x-x_{0,o})^{2}+\theta_{o}^{ 2}}\bigg{]} \tag{14}\]  
\[p_{o,b}(i)=\frac{1}{\pi}\bigg{[}\frac{\theta_{b}}{(x-x_{0,b})^{2}+\theta_{b}^{ 2}}\bigg{]} \tag{15}\]  
At threshold \(t\), the prior probability of a pixel being part of the object is \(\gamma(t)\), with \(1-\gamma(t)\) indicating the probability of belonging to the background. \(\gamma(t)\) is estimated as follows:  
\[\gamma(t)=\frac{|S_{o,t}|}{|S|} \tag{16}\]  
where \(|\mathrm{X}|\) represents the number of elements in the set X. Therefore, applying [PERSON] rule, we can formulate the posterior probability for a pixel of intensity i being part of the object or background:  
\[P_{t}[s\in S_{o}|\mathcal{F}(s)=i]=\frac{\gamma(t)p_{o,t}(i)}{p_{t}(i)} \tag{17}\]  
\[P_{t}[s\in S_{b}|\mathcal{F}(s)=i]=\frac{(1-\gamma(t))p_{b,t}(i)}{p_{t}(i)} \tag{18}\]  
where \(p_{t}(i)\) is calculated by  
\[p_{t}(i)=\gamma(t)p_{o,t}(i)+(1-\gamma(t))p_{b,t}(i) \tag{19}\]  
Therefore, the Tsallis entropy expressions for both object and background classes presented in Equations (8) and (9) are reformulated  
\[p_{b,t}(i) =P[\mathcal{F}(s)=i|s\in S_{b,t}]\]0  
\[p_{b,t}(i) =P[\mathcal{F}(s)=i|s\in S_{b,t}]\]1  
The information measure function is then updated and expressed as  
\[p_{b,t}(i) =P[\mathcal{F}(s)=i|s\in S_{b,t}]\]2  
and the optimal threshold \(t_{opt}\) can be estimated using common optimization algorithms.
###### Abstract  
This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 participants), teams collaborated over Zoom (version 5.16.6) to set up a virtual Mars station using custom simulation software (Mars Star City, version 4.0). Participants were divided into 14 experimental teams, which were shown the virtual mirror, and 14 control teams without it. Team performance was measured by the improvement in the Mars simulation output quality. Our analysis using correlation, multi-level regression, and machine learning revealed that fewer interruptions but an increasing number over time correlated with higher performance. Higher vocal arousal and happiness also enhanced performance. We confirmed that female presence in teams boosts performance. SHAP values indicated that high variability in happiness, head movement, and positive facial valence--an \"emotional rolleroaster\"--positively predicted team performance. The experimental group outperformed the control group, suggesting that virtual mirroring improves virtual teamwork and that interrupting each other more while speaking less, leads to better results.  
virtual teamwork; real-time feedback; team performance; emotion recognition; videoconferencing; machine learning +
Footnote †: journal: Applied Sciences  
1 Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, MA 02142, USA 1  
2
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 5 Performance Analysis and Simulation
### Average Delay of Mapping  
The average delay of mapping is very important to the mapping resolution system, because it defines the time required between sending a mapping parse request and receiving the mapping item later. An important goal of the mapping resolution service design is to keep mapping delay at an acceptable level even though the number of tags is very large.  
In Figure 6, the x-axis represents the size of the mapping resolution system, and the y-axis represents the average mapping delay. Figure 6a gives a comparison of the simulation results of average mapping delay in small networks. It can be seen from the figure that the mapping system proposed in this paper has a lower average mapping resolution delay than LISP-DHT [18], DHT-MAP [2], and Lchord [19]. The average decreased by 18%, 13%, and 8%, respectively. This proves that when the network size is small, the mapping system proposed in this paper is effective in reducing the mapping resolution delay. Figure 6b gives the average mapping delay comparison of different mapping systems under large-scale network conditions. It can be seen from the figure that the mapping system proposed in this paper has a lower average mapping resolution delay than LISP-DHT, DHT-MAP, and Lchord. The average decreased by 32%, 23%, and 13%, respectively. Especially, with the increasing scale of the mapping system, the advantage of the mapping system proposed in this paper is more obvious. This shows that when the network size is large, the mapping system proposed in this paper is more effective in reducing the mapping resolution delay.
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
###### Abstract  
During every Atlantic hurricane season, storms represent a constant risk to Texan coastal communities and other communities along the Atlantic coast of the United States. A storm surge refers to the abnormal rise of sea water level due to hurricanes and storms; traditionally, hurricane storm surge predictions are generated using complex numerical models that require high amounts of computing power to be run, which grow proportionally with the extent of the area covered by the model. In this work, a machine-learning-based storm surge forecasting model for the Lower Laguna Madre is implemented. The model considers gridded forecasted weather data on winds and atmospheric pressure over the Gulf of Mexico, as well as previous sea levels obtained from a Laguna Madre ocean circulation numerical model. Using architectures such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) combined, the resulting model is capable of identifying upcoming hurricanes and predicting storm surges, as well as normal conditions in several locations along the Lower Laguna Madre. Overall, the model is able to predict storm surge peaks with an average difference of 0.04 m when compared with a numerical model and an average RMSE of 0.08 for normal conditions and 0.09 for storm surge conditions.  
machine learning; storm surge; hurricane; forecasting; CNN; LSTM Article
# Color Simulation of Multilayered Thin Films Using Python
## 2 Materials and Methods
### Theoretical Background
#### 2.1.3 Color Simulation  
To convert a reflectance spectrum to a color (i.e., sRGB value), the spectrum is first mapped to the CIE 1931 XYZ color space using formulas based on color matching functions [26]:  
\[N=\int\overline{y}(\lambda)I(\lambda)d\lambda \tag{28}\]  
\[X=\frac{1}{N}\int\overline{x}(\lambda)S(\lambda)I(\lambda)d\lambda \tag{29}\]  
\[Y=\frac{1}{N}\int\overline{y}(\lambda)S(\lambda)I(\lambda)d\lambda \tag{30}\]  
\[Z=\frac{1}{N}\int\overline{z}(\lambda)S(\lambda)I(\lambda)d\lambda \tag{31}\]  
where \(\lambda\) is the wavelength and \(\overline{x}\), \(\overline{y}\), and \(\overline{z}\) are the color-matching functions, which are numerical descriptions of the chromatic and tristimulus response of cone cells [27]. The parameters \(S(\lambda)\) and \(I(\lambda)\) are the spectral reflectance and standard illuminant, respectively. In this study, we employed a CIE 1931 2\({}^{\circ}\) standard observer and D65 white reference as the color matching functions and the reference illuminant, respectively [28; 29]. The CIE 1931 2\({}^{\circ}\) standard observer quantifies human color perception. Due to the distribution of cones in the human eye, the tristimulus values of colors vary depending on the observer'sfield of view. The standard observer model was defined based on stimuli subtending a 2\({}^{\circ}\) visual angle, corresponding to the foveal region of the retina. The D65 white reference is the standard daylight illuminant, positioned using CIE values. The values \(X\), \(Y\), and \(Z\) can be converted to either CIE \(xy\) values or sRGB values. For CIE \(xy\) values,  
\[x=\frac{X}{X+Y+Z} \tag{32}\]  
\[y=\frac{Y}{X+Y+Z} \tag{33}\]  
where \(x\) and \(y\) are the coordinates of the CIE 1931 color space chromaticity diagram, which is widely used to specify colors in practice. CIE XYZ coordinates are linearly transformed to sRGB coordinates using the following conversion matrix [30]:  
\[\begin{bmatrix}r\\ g\\ b\end{bmatrix}=\begin{bmatrix}3.24045&-1.53714&-0.49853\\ -0.96927&1.87601&0.04156\\ 0.05564&-0.20403&1.05723\\ \end{bmatrix}\begin{bmatrix}X\\ Y\\ Z\end{bmatrix} \tag{34}\]  
where \(r\), \(g\), and \(b\) are linear RGB. The sRGB color space defines its values in a nonlinear form that corresponds to human visual perception. However, the conversion from XYZ to RGB derived above produces linear RGB values. To obtain sRGB values, a gamma-compressed transformation is used [30]:  
\[V=\begin{cases}12.92v&v\leq\)0.0031308\\ 1.055v^{1/2.4}-0.055&\text{otherwise}\end{cases} \tag{35}\]  
where \(v\in\{r,g,b\}\) and \(V\in\{R,G,B\}\). Since the values obtained using Equation (35) are in the nominal range (0-1), we multiply each component by 255 to represent conventional sRGB values in the range of 0-255.