esa-sceva/satcom-chunk-collection huggingface dataset:
# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 4 Simulation Setup and Analysis of Results
#### (A) Average delay and average power consumption
Before letting the sensor nodes move to other locations, the LMM collects several pieces of information, including the moving location, the distance from the original location to the current location, and the distance from the current location to the destination. Figure 9 presents the moving times of the sensor nodes using the LMM and the other mobility models at different velocities. The LMM outperforms the other mobility models; it is scalable because the locations and the distance between the moving sensor nodes are easily determined, and the motion only marginally increases the time required to change positions. **(C) Drop Rate using the LMM, CMM, RWMM, and WMM**  
Network congestion and a lack of network coverage are key factors that affect the drop rate of packets. These two issues are directly or indirectly dependent on the performance of the mobility models. Congestion and lack of coverage prevent packets from successfully arriving at the base station. Figure 8: Times taken by the sensor node to reach different positions using the LMM, CMM, RWMM, and WMM at a fixed velocity of 10 m/s. Figure 9: Times taken by the sensor node to reach different positions using the LMM, CMM, RWMM, and WMM at different velocities. The coverage problem includes monitoring a set of goals in the intended area. The sensor nodes collect the information from events within their communication range, and forward it to the base station. The base station may not be able to receive transmitted packets due to such coverage problems. Figure 10 compares the packet drop rate of the LMM with those of the CMM, RWMM, and WMM. The LMM outperforms the other mobility models in walking patterns; it drops a maximum of 0.7% of the packets, whereas the other mobility models have dropped rates as high as 2.45%. This result confirms the high potential of the LMM as a mobility model for walking patterns.
## Appendix A Equations
### Multinomial Multivariable Logistic Regression with a Logit Parameter Estimation  
In multinomial logistics regression, the probability of the set of C classes predicted is calculated, and the class possibilities are associated, which means the sum of all class possibilities is 1. Equation (12) shows the mathematical description of the multinomial logistics regression function to calculate a class probability.  
\[p_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{C}e^{z_{j}}} \tag{12}\]  
where:  
\(p_{i}\): is the probability of class I.  
\(z_{i}\): is denoted by the Equation (13).  
\[z_{i}=\alpha_{i1}x_{1}+\alpha_{i2}x_{2}+\alpha_{i3}x_{3}\...+\ \alpha_{in}x_{n}+\beta_{i} \tag{13}\]  
where:  
\(x_{1}\), \(x_{2}\), \(x_{3}\)... \(x_{n}\) represent the independent variables and n is the number of independent variables.  
Also, we took a reference class to estimate the parameter in odd ratios, Equation (12) shows the transformed Logit function used to estimate parameters.  
\[y=Logit(x)=\log\frac{p_{ij}}{p_{ir}} \tag{14}\]  
where:\(p_{ij}\): is the probability of sample i belonging to class j.  
\(p_{ir}\): is the probability that the same sample belongs to the reference class r.  
\(y\): is the multinomial predicted variable.
## 2 Related Work and Contributions
### Existing Application of RSSI in Technology Recognition  
RSSI is readily available on many commercial radio chipsets; hence, several pioneering studies in technology recognition on small-scale devices make use of RSSI due to practical advantages. The work in [27] proposes to identify technologies with different medium access control (MAC) mechanisms by observing the transmission and idle period in the time series of RSSI. Two types of classifiers are presented and experimentally validated to identify sub-categories of Wi-Fi standards. The authors of [28] use support vector machines as a machine learning algorithm to identify three generic types of MAC mechanisms, by observing the transmission interval duration and the received power for each operating MAC. Building upon the observation of the transmission interval, the authors of [29] introduce the peak-to-average power ratio and hardware-specific features. For instance, the presence of a microwave oven is identified when the RSSI of the chip CC2420 drops below the noise floor, which is likely caused by saturation in the receiver chain. The intrinsic periodicity extracted by a simplified version of the spectral correlation function proposed by [30], and the characteristics of the spectrum trace obtained by a commercial Wi-Fi card proposed by [31] are used in combination with features of RSSI series (such as idle and transmission time) to distinguish technologies in the 2.4-GHz ISM band. Although each of these works has its own strength in certain application scenarios, the common interdependency on features from the time domain of RSSI series, such as packet duration and inter-packet gap, implies that they lack the capability to distinguish streaming technologies, such as LTE and DVB-T. In addition, particular approaches such as [28] relying on complex machine learning algorithms are unsuitable to implement on constrained devices.
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
## 3 Generation of CBFM Waveforms for Joint Radar-Communications
Note that the communication receiver can also act as the bistatic radar receiver. The details of the communication receiver and bistatic/monostatic radar configurations are given in subsequent sections. Figure 4 shows the architecture of the joint radar-communication system transmitter. Here, the digital information is encoded into the chaotic system that is further modulated on to frequency to generate the CBFM waveform. This modulation step serves several purposes. Mainly, it increases the bandwidth of the transmitted signal, which is necessary for high-resolution radar imaging. Secondly, it provides an additional security blanket without revealing what type of information is encoded. Here, the security is through obfuscation and not any standard type of encryption. Lastly, it is immune to noise up to a certain degree of freedom. The selection of the chaotic state variable depends on the conditional Lyapunov exponent of the response oscillator (discussed in the next section). Thus, there may be some systems where state variable \(x(t)\) may not serve the purpose of synchronization, but other state variables will do.
## 2 Materials and Methods
### Preparation of (NH4)4[Co(C6H5O7)2]  
Cobalt citrate, (NH4)4[Co(C6H5O7)2], was synthesized [11] with a slight modification. In brief, Co(NO3)2.6H2O (0.30 g) and citric acid (0.42 g) were placed in a 25 mL round bottom flask and dissolved in 5 mL of water. The reaction mixture was stirred at room temperature until both reactants had completely dissolved. Then, the pH of the reaction mixture was adjusted to \(\sim\)8 with ammonia. Subsequently, the clear reaction solution was filtered, and ethanol was added. After a few days at 4 \({}^{\circ}\)C, dark pink crystals grew out of the solution. The crystalline material was collected by filtration and dried in vacuo. The yield of the reaction was 0.45 g (-61%). Physicochemical characterization of the crystalline product was consistent with the X-ray crystal structure and molecular formulation of the derived material. For the purpose of the work described herein, the compound (NH4)4[Co(C6H5O7)2] (MW 509.3 g/mol) was employed in crystalline powder form, referred to hereafter as cobalt citrate (Co-cit). Solutions of Co-cit (1 mM) were prepared by dissolving 10.2 mg of the compound in 20 mL ultrapure water at room temperature. The solution was then sterilized by filtering through a 0.45 \(\upmu\)m pore size syringe filter (Filters Fiorini, France) into a sterile glass bottle.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Limitations  
This review focuses on 616 publications related to discrete event simulation from 2010 to 2022. It is important to note that the included publications were sourced on the PubMed, Scopus, and WOS databases. Consequently, articles from the gray literature, which is commonly found in the healthcare sector due to privacy concerns, were not included in this analysis.  
Furthermore, this review provides a broad comparison of the benefits of DES software in the healthcare sector and the reported results by authors during the 12-year period. However, it is worth mentioning that this study did not consider the implementation costs associated with the models.  
One limitation of this study is the potential generalization of the findings from the specific distributions used to other healthcare procedures. It is important to acknowledge that certain statistics and results may not be applicable or hold true for all medical departments. Each department may have unique operational challenges and factors that were not extensively examined in this study.
## 2 Preliminaries
### Forward and Backward Privacy of DSSE
#### 2.3.2 Backward Privacy  
When searching on keyword \(w\) is made, backward privacy [2; 5] guarantees that the deleted document identifiers containing \(w\) are not revealed.  
Now, we introduce a leakage function defined in backward privacy. The record information revealed in a list of queries issued so far is represented as the above leakage function \(\mathcal{L}\). The list of queries is denoted as \(Q\), where  
\[Q=\{(u,\text{op},\text{in})\ or\ (u,w)\}.\]  
There are two types of query: search query and update query. \((u,w)\) is a search query, where \(u\) refers to the timestamp and \(w\) refers to the search keyword. Note that the timestamp \(u\) starts from 0 and increases with the incoming queries. (\(u\), op, in) is an update query, where op \(\in\{\text{add},\text{del}\}\) and in refers to the input \((w,\text{ind})\).  
The search pattern [3], \(sp(w)\), is one of the common leakage function, composed of timestamps of all search queries on keyword \(w\), which allows an adversary to identify identical search queries. The formal definition of \(sp(w)\) is defined as follows:  
\[sp(w)=\{u|(u,w)\in Q\}.\]  
\(TimeDB(w)\), for a keyword \(w\), leaks the list of all documents matching a search keyword \(w\) and the timestamp of their insertion, except for documents that are deleted. The formal definition is as follows:  
\[TimeDB(w)=\{(u,\text{ind})|(u,add,(w,\text{ind}))\in Q,\] \[\text{and}\ \forall u^{\prime},(u^{\prime},del,(w,\text{ind})) \
otin Q\},\]  
Note that \(u^{\prime}\) is the timestamp that comes after the \(u\). \(Updates(w)\) refers to the list of timestamps of all updates on keyword \(w\). Formally, \(Updates(w)\) is defined as :  
\[Updates(w)=\{u|(u,add,(w,\text{ind})\text{ or }(u,del,\] \[(w,\text{ind}))\in Q\}.\]  
Lastly, the leakage function \(DelHist(w)\) is defined as follows:  
\[DelHist(w)=\{(u^{add},u^{del})|\exists\text{ind }s.t.\ (u^{del},del,(w,\] \[\text{ind}))\in Q,\text{and}\ (u^{add},add,(w,\text{ind}))\in Q\}.\]  
[PERSON] et al. [5] introduced the formal definitions for three different types of backward privacy ordered from the most to least secure notions, which are defined as follows:  
* Type-I: _It leaks the document identifiers matching the keyword \(w\), timestamps of their insertion \((TimeDB(w))\), and total number of updates on \(w\)._
* Type-II: _It leaks the document identifiers matching the keyword \(w\), \(TimeDB(w)\), and the list of timestamps of the entire updates on keyword \(w\)\((Updates(w))\)._
* Type-III: _It leaks the document identifiers matching the keyword \(w\), \(TimeDB(w)\), \(Updates(w)\), and deletion history \((DelHist(w))\) that reveals which deletion update canceled which insertion update._
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Column Generation
#### 3.3.2 Pricing Acyclic Paths  
Many paths which are generated in the pricing do not share much resemblance with tours in the underlying graph \(D\): On the one hand, certain paths only contain few vertices and lead almost immediately back to \(s_{\theta}\). We will address this problem later using the propagation of lower bounds. On the other hand, paths frequently contain cycles with respect to \(D\), that is, they contain two different copies \(v_{\theta}\), \(v_{\varphi^{\prime}}\) of the same vertex \(v\
eq s\) with different values \(\theta\
eq\theta^{\prime}\). Specifically, a path  
\[P=(a_{1}=(u_{1},v_{1},\theta_{1}),\ldots,a_{k}=(u_{k},v_{k},\theta_{k}))\]  
in \(D^{\mathcal{T}}\) forms a \(k\)-cycle if \(u_{1}=v_{k}\). A path of at least \(k\) arcs contains a \(k\)-cycle if any of its subpaths is a \(k\)-cycle. A path not containing a \(j\)-cycle for \(j\leq k\) is called \(k\)-cycle-free. Naturally, a \(k\)-cycle-free path is also \(j\)-cycle-free for all \(j<k\), and it is a tour if, and only if it is \((n-1)\)-cycle-free.  
In order to strengthen the formulations, we restrict the set of variables in the path-based formulation (TDTSP-P) to the subset \(\mathcal{P}_{k}\subseteq\mathcal{P}\) of \(k\)-cycle-free paths, noting that  
\[\mathcal{P}\rightleftharpoons:\mathcal{P}_{1}\subseteq\mathcal{P}_{2}\ldots \subseteq\mathcal{P}_{n-1}=\mathcal{P}^{*}.\]  
Similarly, the sets \(\mathcal{P}_{k}\) yield increasingly tighter Lagrangean relaxations, i.e., satisfying  
\[L_{\mathcal{P}}(\lambda)\leq L_{\mathcal{P}_{2}}(\lambda)\leq\ldots\leq L_{ \mathcal{P}_{n-1}}(\lambda).\]  
The restriction to the set \(\mathcal{P}_{k}\) requires us to adapt the pricing procedure to \(k\)-cycle-free paths. The \(k\)-cycle-free shortest problem has previously been examined [17], leading to a two-cycle-free labeling scheme with linear running time, as well as an algorithm computing \(k\)-cycle-free paths with a running time of \(\mathcal{O}((k!)^{2})\) in the parameter \(k\). In order to improve the overall performance of the column generation, we use the weighted Dantzig-Wolfe decomposition described above based on the values \(L_{\mathcal{P}_{k}}(\lambda)\).  
Adapting the original formulation (TDTSP-A) is not as straightforward, since we have no control regarding the path decomposition producing the values \(x_{uv,\theta}\). As a compromise, we propose to generate new columns according to the arcs of \(k\)-cycle-free paths. Since we cannot guarantee that the resulting flow can be decomposed into \(k\)-cycle-free paths, we cannot ensure that \(L_{\mathcal{P}_{k}}(\lambda)\leq\text{LP}\left(\text{TDTSP-A}\right)\). Nonetheless, it still holds that \(L_{\mathcal{P}_{k}}(\lambda)\leq\text{(TDTSP-A)}\), which is sufficient to ensure that, if no more \(k\)-cycle-free paths can be found, the restricted LP is a relaxation of (TDTSP-A). We are, however, unable to apply the weighted Dantzig-Wolfe decomposition to the arc-based formulation.
## 3 Results and Analysis
### Time Series Precipitation  
The capability of catching the temporal variation of rainfall is essential for the monitoring of flash floods and landslides. Figure 4 shows the time series plots of hourly-accumulated rainfall from gauge and satellite data in the HBT domain, Bejing and the rainfall center indicated by the red letter A in Figure 2a. The gauge-based rainfall generally increases steadily from the first hour to the peak occurring at the 20 th hour and then decreases dramatically to less than 1 mm/h in the 24 th hour in the HBT and Beijing regions. The 3B42 RT and 3B42V7 have high CC values (0.87 and 0.88, respectively) in HBT but negative CC values (\(-\)0.17) in Beijing. This figure shows that both 3B42 RT and 3B42V7 have generally captured the temporal variation of rainfall in HBT but witnessed the rainfall peak 6.5 h ahead of gauge observations (Figure 4a). It is also evident that 3B42 RT and 3B42V7 failed to capture the temporal trend of rainfall in Beijing (Figure 4b). This can be explained as  
Figure 3: Cloud top time series brightness temperature map over the entire HBT domain (**a**–**h**) time series area-average brightness temperature at the rainfall center shown in Figure 2a. The brightness temperature data are provided by the Climate Prediction Center, National Oceanic and Atmospheric Administration [40].  
follows. First, there were limited numbers of microwave overpasses over this area. Secondly, the cold center of cloud top temperature moved southeastward to Tianjin after 21:12 UTC July 2010 (see Figure 3d-g), and the IR-based product 3B41 RT, which is used to fill in the field where 3B40 RT does not exist, yields a large error when the convective cloud has a higher brightness temperature than the commonly used temperature threshold [38; 39]. For the rainfall center, although 3B42 RT and 3B42V7 have a similar trend of increase in rainfall amount, both of them miss the rainfall peak with great underestimates by 29.02% and 36.07%, respectively (Figure 4c). Figure 3h implies that the cloud top brightness temperature at the rainfall center jumped quickly from the minimum value of about 208 K at the 11 th hour to 255 K at the 18 th hour, dropped dramatically to about 216 K at the 19 th hour, and then rapidly went up to about 288 K at the 24 th hour. TMPA showed their peak rainfall from 13:30 UTC to 16:30 UTC when the cloud top brightness temperature was high above 230 K. The poor performance, _i.e._, underestimation, of 3B42 RT and 3B42V7 could be explained as follows. The rainfall center is located in the mountain areas, where the regional topographical lifting of moisture spawned the convective cells for the formation of the MCS [9]. The IR-based 3B41 RT, which is used to fill the gaps where 3B40 RT is not available [37], has limitations in estimating the precipitation in the complex terrain [1; 38; 39; 41]. Additionally, the errors of the individual algorithms also have an important contribution to the poor performance of 3B42 RT and 3B42V7 even when the microwave does contribute.
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
###### Abstract  
In order to deal with the shortcomings (such as poor robustness) of the traditional single-channel vibration signal in the comprehensive monitoring of the gearbox fault state, a multi-channel decision-level fusion algorithm was proposed based on symmetrized dot pattern (SDP) analysis, with the visual geometry group 16 network (VGG16) fault diagnosis model. Firstly, the SDP method was used to convert the vibration signal of a single multi-channel sensor into an imaging arm. Secondly, the obtained image arm was input into the VGG16 convolutional neural network in order to train the fault diagnosis model that can be obtained. Then, the SDP images of the signals that were to be measured from multiple multi-channel sensors were input into the fault diagnosis model, and the diagnosis results of multiple multi-channel sensors could then be obtained. Experimentally, it was demonstrated that the diagnostic results of multi-channel sensors one, two, and three were more accurate than those of single-channel sensors one, two, and three, by 3.01%, 16.7%, and 5.17%, respectively. However, the fault generation was not generated in a single direction, but rather multiple directions. In order to improve the comprehensiveness of the raw vibration data, a fusion method using DS (Dempster-Shafer) evidence theory was proposed in order to fuse multiple multi-channel sensors, in which the accuracy achieved 99.93% when sensor one and sensor two were fused, which was an improvement of 8.88% and 1.02% over single sensors one and two, respectively. When sensor one and sensor three were fused, the accuracy reached 99.31%, which was an improvement of 8.31% and 6.17% over single sensors one and three, respectively. When sensor two and sensor three were fused, the accuracy reached 99.91%, which was an improvement of 1.00% and 6.74% over single sensors two and three, respectively. When three sensors were fused simultaneously, the accuracy reached 99.99%, which was 8.93%, 1.08%, and 6.81% better than single sensors one, two, and three, respectively. Therefore, it can be proved that the number of sensor channels has a great influence on the diagnosis results.  
SDP; VGG16; multi-channel; fault diagnosis; gearbox gear teeth +
Footnote †: journal: Applied Sciences
## 2 System and Channel Model
The multiplicative pdf of the cascaded link is represented as \(h_{j}=\prod\limits_{n=1}^{N}h_{n}\)where \(N\) is the number of layers. Each layer has a different statistical parameter [21]\(\mu_{j}=\sum_{n=1}^{N}\mu_{n}\) and \(\sigma_{j}^{2}=\sum_{n=1}^{N}\sigma_{n}^{2}\). The statistical parameters depend upon the depth of the water [31]. It is assumed that the path loss of all \(\mathcal{SR}_{m}\) links (\(I_{\mathcal{SR}_{m}}\)) is identical, and, similarly, the path loss of all \(\mathcal{R}_{m}\mathcal{D}\) links (\(I_{\mathcal{R}_{m}\mathcal{D}}\)) is also identical. \[I_{j}\approx\left(\frac{\mathcal{D}_{R}}{\mathcal{Q}_{F}}\right)^{2}\ell_{j}^{ -2}\exp\left(-c\left(\frac{\mathcal{D}_{R}}{\mathcal{Q}_{F}}\right)^{\rho_{0} }\ell_{j}^{1-\rho_{0}}\right), \tag{4}\]  
where \(\mathcal{Q}_{F},\mathcal{D}_{R},\rho_{0}\) and \(c\) denote the transmitter beam divergence angle, receiver aperture diameter, correction coefficient and extinction coefficient, respectively. \(\ell_{j}\) is the distance of the \(j\)th link. The strength of fading is determined by the scintillation index, which is calculated by using the modified Nikishov spatial power spectrum model represented as [35; 36; 37]  
\[\Phi_{n}(\kappa) =(4\pi\kappa^{2})^{-1}\times C_{0}\left(\frac{\alpha^{2}\chi_{T}} {\omega^{2}}\right)\epsilon^{-1/3}\kappa^{-5/3}\left[1+C_{1}(\kappa\eta)^{2/3 }\right]\] \[\times\left[\omega^{2}\exp(-C_{0}C_{1}^{-2}P_{T}^{-1}\delta)+d_{r }\exp(-C_{0}C_{1}^{-2}P_{S}^{-1}\delta)\right.\] \[\left.-\omega(d_{r}+1)\times\exp(-0.5C_{0}C_{1}^{-2}P_{TS}^{-1} \delta)\right]. \tag{5}\]  
where \(C_{0}=0.72\) and \(C_{1}=2.35\) are the constants; \(\alpha\) and \(\beta\) are the concentration coefficients of thermal expansion and saline, respectively. The \(P_{T}\) and \(P_{S}\) are the Prandtl numbers of temperature and salinity, respectively [31]. The eddy diffusivity ratio (\(d_{r}\)), \(\delta\), Kolmogorov microscale length (\(\eta\)) and relative strength of temperature and salinity fluctuations (\(\omega\)) are calculated as follows: [37]  
\[d_{r}= \begin{cases}|\omega|/\left(|\omega|-\sqrt{|\omega|(|\omega|-1)}\, \right),&|\omega|\geq 1\\ 1.85|\omega|-0.85,&0.5\leq|\omega|\leq 1\\ 1.5|\omega|,&|\omega|<0.5,\end{cases}  \tag{6}\]  
beg  
\[\delta =1.5C_{1}^{2}(\xi\eta)^{(4/3)}+C_{1}^{3}(\xi\eta)^{2},\] \[\eta =(\upsilon^{2}/\epsilon)^{1/4},\] \[\omega =\alpha(\frac{dT}{dZ})/\beta(\frac{dS}{dZ}). \tag{7}\]  
By using (5), (6) and (7), the scintillation index \(\sigma_{h_{n}}^{2}\) of the \(n\)th layer is computed as [24]  
\[\sigma_{h_{n}}^{2} =8\pi^{2}k_{0}^{2}p\mathcal{W}\int\limits_{0}^{1}\int\limits_{0}^ {\infty}\kappa\Phi_{n}(\kappa)\exp\left[-\frac{\Lambda p\mathcal{W}\kappa^{2 }\beta^{2}}{k_{0}}-\frac{D_{R}^{2}\kappa^{2}\beta^{2}}{16}\right]\] \[\times\left\{1-\cos\left[\frac{p\mathcal{W}\kappa^{2}}{k_{0}} \beta(1-(1-\Theta)\beta)\right]\right\}dxd\beta, \tag{8}\]  
The scintillation index \(\sigma_{h_{n}}^{2}\) and log-amplitude variance are related as \(\sigma_{n}^{2}=0.25(1+\sigma_{h_{n}}^{2})\).