esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
## 3 Assumptions and Requirements
### The Network Model  
Similar to other WSNs [9], nodes in smart home consist of the sink node (only one sink node in the sensor network) and sensor nodes \(N=\{N_{1},N_{2},N_{3},N_{4}\ldots N_{n}\}\). As shown in Figure 3, the smart home provider is a reliable service provider who can collect and analyze data from many homes. The sink node has a high enough computing power to take on complicated operations. The sensor node is the smallest unit in the WSN, and has a limited computing power and limited battery capacity. Sensor nodes can apply encryption algorithms to encrypt collected data [10], before sending it to the sink node. We assume that the encryption algorithms used are safe enough and that the attacker cannot acquire the original data through analyzing the cipher texts. We also assume that the sink node is actively powered, while being equipped with tamper-resistant hardware [11]. Consequently, it is reasonable to assume that the adversary cannot compromise the sink node.  
Figure 2: Traffic status of the sensors.  
We assume that the dataset collected at the sink node is \(D=\{X,y\}\) and \(X=\{X_{1},X_{2},X_{3}\cdots,X_{n}\}\), where \(X_{i}\) refers to the set of sensor data from sensor node \(N_{i}\). A datum of \(X_{i}\) is denoted as \(X_{i}^{(j)}=\{x_{1},x_{2},x_{3},x_{4}\}\), in which \(x_{1}\) is the sensor data, \(x_{2}\) is the traffic status, \(x_{3}\) is the timestamp of transmitting the sensor data, and \(x_{4}\) is the flag to indicate whether the message is real or fake. Label \(y\) in \(D\) can only be a binary value {0,1}. The minimum unit of the collected datasets is \(\left\{X_{j}^{(i)},y^{(i)}\right\}\), which means that the \(i^{th}\) sensor data comes from sensor node \(N_{j}\) with label \(y^{(i)}\).
## 4 Results
### Scenario Analysis  
In this analysis, the weights of the criteria were recalculated based on three scenarios denoted as Scenario A, B, and C. In Scenario A, it was assumed that all criteria had the same weights, while it was assumed in scenario B that \"Compliance with the schedule\" (C2) is not an important project management success indicator and was hence excluded as a criterion. Scenario C was similar to scenario B but it was \"Compliance with the budget\" (C1) that was excluded as a criterion from the analysis. The results of the scenario analysis indicated a change in the rank of enabling elements, as shown in Table 19 and Figure 4. DT2 (Industry 4.0 Enabling Technologies) remained the most effective enabling element of digital infrastructure in scenario A as it was in the original scenario. On the other hand, DF2 (Digitally Competent Employees) has ranked first in scenario B and scenario C. It was also noteworthy that the average CC\({}_{\text{i}}\) for digital pillars has not changed significantly in the different proposed scenarios. This indicates that despite the different weights of the criteria, the digital infrastructure enabling elements associated with all digital pillars have had consistent, variating effects on project management success with respect to all criteria. Therefore, the ranking of enabling elements has had minor changes from one scenario to another. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alternative**} & \multirow{2}{*}{**Enabling Element**} & \multirow{2}{*}{\begin{tabular}{c} **Normalized** \\ **Weight Based** \\ **on CC\({}_{\text{i}}\)** \\ \end{tabular} } & \multirow{2}{*}{**Average CC\({}_{\text{i}}\)**} & \multirow{2}{*}{\begin{tabular}{c} **Digital Pillar** \\ **Rank Based on** \\ **Average CC\({}_{\text{i}}\)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Digital Pillar** \\ **Relative** \\ **Weight** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Digital Pillar** \\ **Based on Digital Pillar** \\ **Weight** \\ \end{tabular} } \\ \hline
**DT1** &
\begin{tabular}{c} Digital \\ Transformation \\ \end{tabular} & 0.221388 & & & & & & & & \\ \hline
**DT2** &
\begin{tabular}{c} Industry 4.0 Enabling \\ Technologies \\ \end{tabular} & 0.246722 & 0.85246 & 1 & 0.468111 & 1 \\ \hline
**DF1** &
\begin{tabular}{c} Digital Teams \\ \end{tabular} & 0.121255 & & & & & & & \\ \hline
**DF2** &
\begin{tabular}{c} Digitally Competent \\ Employees \\ \end{tabular} & 0.234661 & 0.648147 & 2 & 0.355917 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: \(Cont.\)
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 3 Related Work
### Location Privacy Preserving Schemes  
Location privacy is characterized as the context within which an entity's identity remains unrelated to its actions, location and unique characteristics [39]. VANETs allow each vehicle to deliver a beacon message containing the required details, including the speed and direction of the GPS. The Administrator, TA and the authorized third-party provider are required to provide this detail. The beacon message is continuously demanded from the network, according to which every vehicle sends its ID on a continuous basis. This is further most often closely related to the driver of the vehicle or the owner that a  
Figure 4: Privacy preserving schemes and trust management models.  
breach of location could further help the enemy harm the driver, owner of the vehicle. The identity of the vehicle must be obscured or isolated from its location for the safety of the driver. Therefore, many algorithms, schemes have been proposed [40]. The section also addresses recent privacy legislation for locations and presents a detailed overview of their functionality. To solve the location privacy issues different mechanisms are used such as K-anonymity, Group signature, Pseudonyms, Silent period and Mixed-zone.  
Anonymization strategies also modify pseudonym mappings, interrupting the creation of continuous time series that could be the subject of statistical analysis. Unfortunately, the fast replacement of pseudonyms directly sacrifices ease of use and functionality. Other services, such as personalized refueling and dietary guidelines, calculated based on historical personality specific data, can have a negative effect. In view of the application of interest, these trade-offs involve careful choice of technologies. Some popular privacy preservation strategies are discussed below.
## 3 Background and Problem Statement
### Production Process of Cement Clinker  
Most modern cement plants use a dry process to produce cement by first heating crushed materials including clay, slate, blast furnace slag, etc., and then cooling and grinding the resultant substance to create the fine powder of the clinker. Such dry processes are more thermally efficient than the traditional wet process and thus are more environmentally friendly. Cement clinker results from pyroprocessing, and there are several main steps in such a calcination process of clinker production, including:  
1. Preparation and pre-treatment of raw materials, where quarried limestone is crushed along with clay, slate, blast furnace slag and other components, to a proper size of no more than 3 inches;
2. Pre-heating, where the crushed raw materials are heated in the multi-stage pre-heater and then fed into a rotary kiln to raise their temperature to approximately 1370 \({}^{\circ}\)C (about 2500 \({}^{\circ}\)F); see 1 in Figure 1;
3. Cal calcination and sintering in rotary kiln, where as the raw materials travel along the rotary kiln towards the firing zone, the raw materials lose moisture and other components and form a rock-like substance called clinker that are mineral lumps roughly one inch in diameter; see 1 in Figure 1;
4. Cooling the grate cooler, where the hot cement clinker leaves the rotary kiln, enters into the grate cooler to get cooled/quenched rapidly to prevent further reactions and preserve the desired mineralogical composition, and the heat is recycled to the pre-heater to improve efficiency, save financial cost, and reduce the environmental impact of the process; see 1 in Figure 1;
5. Grinding in ball mills, where the cool clinker is milled to form a fine powder, which is the final product known as cement;
6. Post-treatment, where additional cement components, e.g., calcium sulphate to control the setting time, are added;
7. Packaging and transportation, where the finished cement products are bagged, moved via conveyor, and finally shipped.  
The various steps in the calcination process are closely related and mutually constrained. They have a tight coupling and together build a complete clinker calcination thermodynamic system, Figure 1 shows a simplified schematic view of such calcination and sintering process and the corresponding cement plant devices. The operating conditions in the whole aforementioned process must be carefully controlled to obtain the desirable cement properties.
# Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
## 3 Results & Discussion  
### Planar Array Modeling Results  
The methodology developed was to examine a planar array with \(8\times 8\) and \(6\times 6\) elements with \(\lambda/2\) interelement distance. Equation (3) was used to generate a beam pattern of reference radiation pattern without any failed elements in an array. Figure 4 shows an example of a reference radiation pattern for \(8\times 8\) elements.  
Figure 3: Failed elements in a planar array with non-uniform inter-element distance.  
Next, the failed radiation beam pattern is generated with Equation (8) by designating the failed element's weight as zero. It is assumed that the failed element(s) do not contribute to the radiation pattern. In this analysis, the probability of faulty elements is fixed to a maximum of 25 elements (varies on case basis) in an 8 \(\times\) 8 array and 12 elements for a 6 \(\times\) 6 array. Figure 5a shows 25 elements failed in a random location radiation pattern and its failed radiation pattern in Figure 5b.  
Figure 4: Radiation Pattern of an 8 \(\times\) 8 planar array (reference radiation pattern) in a mesh grid representation.
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 3 Intelligent Detection Method
### Machine Learning Algorithms for Intelligent Fault Detection and Evaluation Metrics  
In this study, three algorithms (Support Vector Machine, Logistic Regression and Gradient Boosting Decision Tree) based on different classification mechanisms were selected as classifiers to detect and diagnose electrical connection faults.  
The performance of machine-learning-based fault detection is evaluated in terms of precision (P), recall (R), F-score (F) and accuracy (AC), while the confusion matrix can show the number of samples of different fault modes correctly or wrongly classified in detail. The Receiver Operating Characteristic (ROC) curve depicts the classification performance using different discrimination thresholds. The closer the node locates to (0.1), the better the performance of the classifier. The Area Under Curve (AUC) is the magnitude of the area under the ROC curve, and the steeper the ROC curve, the higher the AUC value and the better the performance of a given classifier. Further, the processing time for classification was analyzed to predict the time cost for each trained algorithm.
# Mental Workload Classification Method Based on EEG Independent Component Features
## 4 Discussion about Classifiers  
Method 1 presented in this paper uses the SVM classifier algorithm. In this section, we further discussion the influence of different classifier algorithms on the classification accuracy. The LDA, BPNN, and KNN classifiers are used in Method 1, separately, in order to compare with the SVM classifier. The classification accuracies of LDA, BPNN, KNN, and SVM are shown in Figure 9.  
From Figure 9, we can observe that:  
1. The accuracy range of SVM is between 61% and 94%, and its average accuracy is 79.8%;
2. The accuracy range of LDA is between 66% and 93%, and average accuracy is 76.6%;
3. The accuracy range of BPNN is between 51% and 85%, and average accuracy is 71.8%;
4. The accuracy range of KNN is between 53% and 90%, and average accuracy is 69.7%.  
The ranges of classification accuracies of SVM and LDA are very close, but the average accuracy of SVM is higher than that of LDA. So, we select SVM as the classification method for different EEG processing methods. On the other hand, the classification accuracy of the SVM classifier is the highest.  
It is normal that the classification accuracy of different subjects is different, but the accuracies of sub2, sub6 and sub7 are generally low. Through our research, we find that the low classification accuracy of individual subjects is related to their own mental states. Some people have no significant  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Subject** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline sub 1 & 51\% & 61\% & 67\% & 69\% & 64\% & 63\% \\ \hline sub 2 & 51\% & 52\% & 52\% & 54\% & 52\% & 55\% \\ \hline sub 3 & 40\% & 42\% & 58\% & 56\% & 63\% & 66\% \\ \hline sub 4 & 57\% & 58\% & 64\% & 75\% & 83\% & 86\% \\ \hline sub 5 & 56\% & 64\% & 72\% & 76\% & 77\% & 79\% \\ \hline sub 6 & 41\% & 49\% & 52\% & 56\% & 54\% & 58\% \\ \hline sub 7 & 50\% & 54\% & 48\% & 53\% & 52\% & 57\% \\ \hline sub 8 & 50\% & 60\% & 70\% & 77\% & 77\% & 77\% \\ \hline sub 9 & 53\% & 52\% & 56\% & 60\% & 59\% & 61\% \\ \hline sub 10 & 62\% & 68\% & 71\% & 68\% & 75\% & 75\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mental workload classification accuracy (\(\%\)) with different numbers of ICs in Method 1.  
Figure 9: Classification accuracy comparison of different classifiers.  
change in mental state during the three load tasks, so that their mental workloads are not clearly distinguished. We will focus on this issue in future studies.
## Appendix B Proof of Lemma 3  
Let \(Z\triangleq\frac{aX}{Y+1}\) and \(Y\triangleq bY_{1}+cY_{2}\), where \(X\sim\mathcal{X}_{2d_{1}}^{2}\), \(Y_{1}\sim\mathcal{X}_{2d_{2}}^{2}\), and \(Y_{2}\sim\text{Exp}\left(1\right)\). First, the PDF of \(Y\) is derived as  
\[f_{Y}\left(y\right) =\int_{0}^{\infty}f_{Y|Y_{1}}\left(y|y_{1}\right)f_{Y_{1}}\left(y _{1}\right)dy_{1}\] \[=\int_{0}^{y/b}\frac{1}{c}e^{-\left(\frac{y-y_{1}}{c}\right)} \frac{y_{1}^{d_{2}-1}e^{-y_{1}}}{\Gamma\left(d_{2}\right)}dy_{1}\] \[=\frac{e^{-\frac{y}{c}}}{c\Gamma\left(d_{2}\right)}\int_{0}^{y/b }y_{1}^{d_{2}-1}e^{-\left(1-\frac{b}{c}\right)y_{1}}dy_{1}\] \[\overset{(a)}{=}\frac{e^{-\frac{y}{c}}}{c\Gamma\left(d_{2}\right) }\left(1-\frac{b}{c}\right)^{-d_{2}}\gamma\left(d_{\prime}\left(\frac{1}{b}- \frac{1}{c}\right)y\right)\] \[\overset{(b)}{=}\frac{e^{-\frac{y}{c}}}{c}\left(1-\frac{b}{c} \right)^{-d_{2}}\left(1-e^{-\left(\frac{1}{b}-\frac{1}{c}\right)y}\sum_{i=0}^ {d_{2}-1}\frac{\left(\frac{1}{b}-\frac{1}{c}\right)^{i}y^{i}}{i!}\right), \tag{20}\]  
where (a) follows the equality \(\int_{0}^{z}y^{d-1}e^{-ay}dy=\alpha^{-d}\gamma\left(d,\alpha z\right)\) and (b) comes from the relationship between the lower and upper incomplete gamma functions \(\gamma\left(d,x\right)=\Gamma\left(d\right)-\Gamma\left(d,x\right)\). Then, from the PDF of \(Y\), the PDF of \(Z\) is expressed as  
\[f_{Z}\left(z\right) =\int_{0}^{\infty}f_{Z|Y}\left(z|y\right)f_{Y}\left(y\right)dy\] \[=\frac{\left(1-\frac{b}{c}\right)^{-d_{2}}\left(\frac{z}{a} \right)^{d_{1}-1}e^{-\frac{z}{a}}}{ac\Gamma\left(d_{1}\right)}\int_{0}^{\infty }\left(y+1\right)^{d_{1}}e^{-\left(\frac{z}{a}+\frac{1}{c}\right)y}\] \[\quad\times\left(1-e^{-Ay}\sum_{i=0}^{d_{2}-1}\frac{\left(\frac{ 1}{b}-\frac{1}{c}\right)^{i}y^{i}}{i!}\right)dy. \tag{21}\]  
Meanwhile, we have  
\[\int_{0}^{\infty}\left(y+1\right)^{d_{1}}e^{-\left(\frac{z}{a}+\frac{1}{c} \right)y}dy=\sum_{l=0}^{d_{1}}\frac{d_{1}!}{\left(d_{1}-l\right)!}\left(\frac{ z}{a}+\frac{1}{c}\right)^{-\left(l+1\right)} \tag{22}\]  
and  
\[\int_{0}^{\infty}\left(y+1\right)^{d_{1}}e^{-\left(\frac{z}{a}+ \frac{1}{c}\right)y}\left(e^{-Ay}\sum_{i=0}^{d_{2}-1}\frac{A^{i}y^{i}}{i!} \right)dy\] \[\quad=\sum_{i=0}^{d_{2}-1}\sum_{p=0}^{d_{1}}\frac{d_{1}\left(p+i \right)!A^{i}}{\left(d_{1}-p\right)!p!i!}\left(\frac{z}{a}+\frac{1}{c}+A\right) ^{-\left(p+i+1\right)}. \tag{23}\]  
Therefore, by substituting (22) and (23) into (21), (22) is obtained. Further, the MGF of (22) is straightforwardly obtained in the same way as Lemma 1 in Appendix A.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 2 Photoacoustic Signal Generation  
The photoacoustic (PA) effect is the formation of acoustic signals following the light absorption of the medium. For example, the PA signal can be generated when high intensity light impinges on transparent liquid, such as water. The laser-based PA transmitter uses a high-power laser to convert optical energy into acoustic energy at the air-water interface and provides a standoff method for transmitting acoustic signals from air into the water. Based on the energy density and irradiance imparted to the medium, this energy-transfer mechanism can be subdivided into a linear and a nonlinear domain. In the linear process, the light is exponentially attenuated by the medium, resulting in local temperature variations that give rise to volume expansion and contraction. These fluctuations in the density generate a propagating pressure wave. High intensity light, like laser pulses, only heats the medium and the physical state of the medium does not change in the linear PA process. Therefore, the generated acoustic power is proportional to the laser power [39]. On the other hand, in nonlinear PA the physical state of the medium changes during energy transfer, and the acoustic pressure is nonlinearly related to the laser power. The optical breakdown leads to plasma generation at locations where the breakdown threshold is exceeded in a nonlinear PA process. This plasma formation is associated with breakdown and cavitation bubble expansion-collapse shockwaves to generate acoustic signals. The generation of Linear PA is relatively simpler and heavily studied in medical research [40], but the nonlinear PA is a more intricate process. Therefore, details on the breakdown threshold, plasma size-shape, and shockwaves are discussed in the balance of this section.
# Understanding Neutrophil Dynamics during COVID-19 Infection
## 4 Numerical Results
### Basin of Attraction  
We next determine the basins of attraction for the no-inflammation and chronic-inflammation equilibrium states. For simplicity, we assumed that all parameters and initial conditions are given in Tables 1 and 2 (severe case), fixed \(\alpha_{c}=\alpha_{c}^{critical}=3.67\) and varied activated neutrophils and anti-inflammatory mediators initial conditions \((g_{0},n_{0})\in\{0.1<g_{0}<1,0\leq n_{0}\leq 20\}\). Under these assumptions, we find a large basin of attraction for the chronic-inflammation equilibrium state \(S_{3}=(\tilde{n}_{3},\tilde{n}_{3},\tilde{m}_{3},\tilde{c}_{3},\tilde{g}_{3}, \tilde{c}_{3})\) where \(n_{0}>2\) and \(g_{0}>1.2\) and a small basin of attraction for the no-inflammation equilibrium state \(S_{1}=(\tilde{n}_{1},\tilde{a}_{1},\tilde{m}_{1},\tilde{c}_{1},\tilde{g}_{1})=( 0,0,0,\tilde{g}_{S}/\gamma_{g},s_{c}/\gamma_{e})\) for small \(g_{0}\) and \(n_{0}\) values (see Figure 7A). This means that for low initial anti-inflammatory markers, the inflammation is resolved when few active neutrophils are recruited and inflammation persists otherwise. We further investigate how the basin of attraction for the no-inflammation equilibrium \(S_{1}\) changes when we fix \(g_{0}=0.1\) pg/mL and vary both the initial activated neutrophils number \(0\leq n_{0}\leq 20\) and the macrophage phagocytosis rate \(10^{-5}<\phi<0.1\). We find that for high macrophage phagocytosis rate \(\phi\), the no-inflammation equilibrum state is an attracting set for all \(0\leq n_{0}\leq 20\), while for low macrophage phagocytosis rate \(\phi\) the no-inflammation equilibrium state is an attracting set only when the initial activated neutrophil level is small (see Figure 7B).  
Figure 6: **The \(\phi\) bifurcation diagram.** Stable (solid black curves) and unstable (dashed black curves) population equilibria for varied \(\phi\) for (**A**) total neutrophils \(\tilde{n}+\tilde{a}\), (**B**) IL-10 \(\tilde{g}\), and (**C**) N8R \((\tilde{n}+\tilde{a})/\varepsilon\). The green and red lines are the \(\phi\) values from the mild and severe cases, \(\phi^{m}\) and \(\phi^{s}\).  
Figure 7: **Basin of attraction.** Attracting asymptotic solutions \(\tilde{n}+\tilde{a}\) for (**A**) different initial condition \(n_{0}\) and \(g_{0}\) (**B**) \(g_{0}=0.1\), different initial condition \(n_{0}\) and changing parameter \(\phi\). We fix \(\alpha_{c}=3.67\) neutrophils/(macrophage \(\times\) day) and all other parameters are as in Tables 1 and 2 (severe case). Note that \(g_{0}\) and \(\phi\) are plotted on log scale.
# Improved Duplication-Transfer-Loss Reconciliation with
## 3 Materials and Methods
### Assigning Event Costs for \(\mathbb{TL}\) and \(\mathbb{TX}\)  
Based on analyses of simulated and biological datasets, DTL reconciliation often uses default event costs of 1, 2, and 3 for losses, duplications, and transfers, respectively, [5; 16; 17]. For \(\mathbb{TL}\) events, it makes sense to use a cost equal to \(P_{\Theta}+P_{loss}\) since a \(\mathbb{TL}\) event implies one transfer and at least one loss. For \(\mathbb{TX}\) events, a cost equal to that of a transfer event may be appropriate, since each \(\mathbb{TX}\) event implies one transfer but not necessarily any other events (besides extinction or non-sampling). However, depending on the specific dataset being analyzed, e.g., based on the approximate age of the root of the species tree or on the extent of incomplete taxon sampling, it may make sense to assign a slightly higher cost for \(\mathbb{TX}\) events, such as a cost equal to \(P_{\Theta}+P_{loss}\).  
Theoretically, observe that if \(P_{\mathbb{TX}}=P_{\Theta}\) then, for every optimal DTLx reconciliation that invokes a transfer event, there exists a distinct and equally optimal DTLx reconciliation in which that transfer event is substituted by an equivalent \(\mathbb{TX}\) event. Thus, assigning \(P_{\mathbb{TX}}\) to be strictly greater than \(P_{\Theta}\) may make it easier to distinguish between transfer and \(\mathbb{TX}\) events.