esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The LOBIN Prototype: A Practical Study Case
### System Description
#### 3.1.3 Wireless Communications Infrastructure Subsystem
Furthermore, in order to save battery life, end nodes are not involved in routing [71]. In order to avoid possible interferences with the Location Subsystem, as well as with other widespread communication technologies such as IEEE 802.11 [73], the 802.15.4 channel 25 is used for communication within the WCIS, since it is the furthest one from the channels used to broadcast location beacons. Thus, among the WHMS studied based on IEEE 802.15.4, two groups can be differentiated according to their network and application layer. On the one hand, there are some projects that work directly on top of IEEE 802.15.4, designing and developing their own network and application layers (e.g., LOBIN, MASN [38], that adopts a cluster-based communication scheme as its routing protocol, MEDISN [46], in which their distribution points are self-organized into a routing tree, and CodeBlue [25], which is based on a publish/subscribe routing framework allowing multiple sensor devices to relay data to all receivers). On the other hand, there are some projects that use Zigbee (in the body area network) such as [PERSON] _et al._[41], and [PERSON] _et al._[43]). The network layer of Zigbee natively supports both star and tree topologies, as well as generic mesh networks.
## 2 Materials and Methods
### Analysis of the Total Bacterial Disinfection Efficiency on Surfaces
#### 2.2.3 Determination of the Factors Affecting Bacterial Inactivation  
Microorganisms exhibit various inactivation characteristics according to the disinfectant concentration, spray amount, and room size [16; 17; 18]. Therefore, we determined whether the change in these factors affected the disinfection efficiency of bacteria. To set the minimum and maximum levels of each factor on efficiency, we reviewed several studies and determined the ranges based on the recommended volume and actual-use concentrations. We set the values to be elements of an arithmetic sequence. Disinfectant concentration (dilution rate) was divided into 50, 100, and 200 times based on the recommended dilution rate of the disinfectant we used of 100 times. Spray amount was divided into 2, 5, and 8 L, based on the recommended usage volume of ULV sprayer per time of 5 L. The rooms were selected in order of size by measuring the room size in advance. They reflected the size of the location where quarantine was conducted at the actual site. Room sizes were 8400 mm (length) \(\times\) 7500 mm (width) \(\times\) 2500 mm (height) (approximately 160 m\({}^{3}\)), 10,800 mm (length) \(\times\) 8400 mm (width) \(\times\) 2500 mm (height) (approximately 230 m\({}^{3}\)), and 12,600 mm (length) \(\times\) 9500 mm (width) \(\times\) 2500 mm (height) (approximately 300 m\({}^{3}\)), respectively.  
The Box-Behnken design (BBD), one of the response surface methodology designs, was used to identify the individual and interaction effects of each factor on the disinfection efficiency. Response surface methodology is a combination of statistical and mathematical techniques used to develop and optimize processes in which the response of interest is influenced by several factors [29]. The BBD is an independent and rotatable quadratic design with variable combinations at the edges' midpoints and the center of the experimental space. It requires three levels for each explanatory variable, which requires fewer experiments than the other response surface methodology designs to estimate the effects of variables and their interactions. It is a widely used method for optimizing various conditions for bacteria inactivation [30].
## 4 Proposed Power Consumption Modes
### Power Consumption Model  
The total power consumed in the network depends on the number of active, standby and sleeping BSs. The following power consumption model presented in Equation (4) is used to determine the total power consumption of a BS for our proposed 3-state model, it is derived from the paper [15; 16].  
\[\mathfrak{T}_{3-state}=N_{act}(\mathfrak{T}_{o}+d\mathfrak{T}\cdot\mathfrak{T}_{ out})+N_{std}\cdot\mathfrak{T}_{std}+N_{slp}\cdot\mathfrak{T}_{slp} \tag{4}\]  
where \(N_{act}\), \(N_{std}\) and \(N_{slp}\) denote the number of BSs in active mode, stand-by mode and sleep mode respectively; \(\mathfrak{T}_{out}\) is the output power or transmit power; \(d\mathfrak{T}\) is the slope of load-dependent power consumption which is represented as a linear transmission power dependence factor in [15]; \(\mathfrak{T}_{o}\) and \(\mathfrak{T}_{std}\) are the power consumption at minimum non-zero load and in stand-by mode respectively. Please note that the BSs in sleep mode consume zero power. The reference values of all these variables for a macro BS [16] have been shown in Table 2. We assume \(d\mathfrak{T}\) to be unchanged as suggested in the paper [15; 16]. We apply trapezoidal numerical integration [17] on the power consumption curve to find the total energy consumption of the BS. For the 'All BS active' mode we do not apply any sleep or standby mode to the BSs, hence we consider that all the BSs are always active regardless of the traffic requirement. The power consumption model for this mode become as Equation (5) where \(N\) is the total available BSs in the eNodeB and \(N_{tx}\) is the number of transmitting BSs which is equivalent to \(N_{act}\) in Equation (4).  
\[\mathfrak{T}_{all-BS-active}=N\cdot\mathfrak{T}_{o}+N_{tx}\cdot d\mathfrak{T} \cdot\mathfrak{T}_{out} \tag{5}\]  
**Two-State Power Consumption Model:** In the commonly used 'two-state model' [2; 3; 4; 5; 12], all of the inactive BSs are kept in either stand-by mode or in sleep mode, when there is no active user at all. This model can be expressed as the following power consumption equation Equation (6). We have compared our proposed model with two different 'two-state models' namely 'Rangisetti 2 state (active-standby) model' where \(N_{low}=N_{std}\) and 'MDP-based 2 state (active-standby) model' where \(N_{low}=N_{slp}\).  
\[\mathfrak{T}_{2-state}=N_{act}(\mathfrak{T}_{o}+d\mathfrak{T}\cdot\mathfrak{T}_ {out})+N_{low}\cdot\mathfrak{T}_{std} \tag{6}\]
## 2 Signal Self-Cancellation of MVDR Beamformer
For example, the specified direction is usually difficult to accurately equal the DOA of the desired signal, which leads to a direction error. In addition, when the acoustic signal propagates a long distance in the inhomogeneous ocean medium, the wavefront of the acoustic wave will no longer be a theoretical plane wave, and a so-called random wavefront fluctuation occurs. Other negative factors include, errors in mounting positions of the hydrophones, errors in the amplitude and phase gain of the hydrophones. Thus, the given steering vector is equal to the sum of the true steering vector and the steering vector error, that is,  
\[\hat{\mathbf{a}}=\mathbf{a}+\mathbf{a_{e}}, \tag{6}\]  
where \(\hat{\mathbf{a}}\) is the given steering vector with respect to the desired signal, \(\mathbf{a}\) represents the actual steering vector for the desired signal, and \(\mathbf{a_{e}}\) denotes the steering vector error caused by the aforementioned unfavorable factors. From Equations (4) and (6), it can be found that when the steering vector error in Equation (6) is not equal to zero, the constraint in Equation (4) will become \(\mathbf{w}^{H}\hat{\mathbf{a}}=1\), which means that the MVDR beamformer will retain a certain signal corresponding to the given steering vector \(\hat{\mathbf{a}}\), and Not the desired signal. Even worse, the minimization of the objective function in Equation (4) will result in the power of the desired signal being greatly reduced as it passes through the MVDR beamformer. This phenomenon in which the desired signal is cancelled is often referred to as the signal self-cancellation of the MVDR beamformer.
## 3 Methods
### Power Spectral Density  
We estimated the power spectral density using the Welch method. [PERSON] discovered this method in 1967 [47]. It converts time series into (overlapping) segment data, calculating a modified periodogram of every segment, and takes the average of the power spectral density [48]. The average altered periodogram tends to reduce the variance [49]. Further, it estimates the relation to a single periodogram of aggregate data. Power spectral density offers signal power with respect to the frequency spectrum. We require specifying the number of frequency slots to allocate the power spectral that is called the number of fast Fourier transform [50,51]. The Welch techniques are described in Equations (1)-(3) below:  
\[U=\frac{1}{L}\sum_{n=0}^{L-1}[w_{hm}(n)]^{2} \tag{1}\]  
\[P_{w}(f)=\frac{1}{LU}\sum_{n=0}^{L-1}\bigl{\{}w_{hm}(n)x(n+iD)e^{-j2\pi fn} \bigr{\}}^{2} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name of the Physiological Signal**} & \multirow{2}{*}{**Channel of the Physiological Signal**} & \multicolumn{3}{c}{**Number of the Subjects**} & \multicolumn{3}{c}{**Number of the Segment**} & \multicolumn{2}{c}{**Duration of**} \\  & & & **(m)** & & **(n)** & & **the Signal** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} ECG \\ EMG \\ EEG \\ EEG \\ \end{tabular} } & ECG1-ECG2 & 6 & 3 & 9 & 149 & 95 & 244 & 14,640 \\  & EMG1-EMG2 & 6 & 3 & 9 & 149 & 95 & 244 & 14,640 \\  & C4-P4 & 4 & 4 & 8 & 140 & 84 & 224 & 13,440 \\  & C4-A1 & 4 & 4 & 8 & 140 & 84 & 224 & 13,440 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Mean \\ \(\pm\)SD \\ \end{tabular} } & Mean & 5 & 3.5 & 8.5 & 144.5 & 89.5 & 234 & 14,040 \\  & & 1.154 & 0.577 & 0.577 & 5.196 & 6.350 & 11.547 & 692,820 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset of the present work.  
Figure 2: Organizational structure of the proposed work.  
\[P_{w}(f)=\gamma\sum_{n=0}^{L-1}\left(\left\{X_{a}^{n}\right\}^{2}+\left\{X_{b}^{n }\right\}^{2}\right) \tag{3}\]  
where \(U\) is equal to compensate for the loss of signal, \(L\) and \(D\) are the data of the segment, \(w_{hm}(n)\) is the hamming window, \(\gamma\) is a constant, \(X_{a}^{n}\) and \(X_{b}^{n}\) are the real and imaginary part of the \(n\)th segment, and \(P_{w}(f)\) is the Welch method.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 4 Parameter Differences between Combined PPP Models
### Station Clock and Pseudo-Range Residual  
In Equation (9), the ISB assimilation shows opposite signals for station clock and GPS pseudo-range observation residuals, thus the epoch-wise sum quantity of station clock and GPS pseudo-range residual is theoretically the same for the traditional and simplified model. The upper subplot of Figure 9 shows the station clock differences between the traditional and simplified PPP models, where the differences are between the range of \(-\)31 and \(-\)28 m. The bottom subplot of Figure 9 shows the magnitude distribution of the epoch-wise sum quantity differences between the two models for all BDS satellite/station pairs at all epochs. The mean difference is \(-\)0.7 \(\upmu\)m and all the differences are below 0.1 mm.  
Figure 8: Magnitude distribution of differences between the traditional and simplified models of (**a**) epoch-wise sum of GPS ambiguity and station clock parameters; (**b**) epoch-wise sum of BDS ambiguity, ISB and station clock parameters. Both plots exhibit normal distributions. The top-left corner of each subplot shows the bias and the standard deviation (\(\sigma\)), whereas the top-right corner shows the percentages of deviations that are within 2\(\sigma\) and 3\(\sigma\).
## Appendix A Complete Kinematics Analysis of the Four-Bar Mechanism  
Considering that the angle \(q_{1}=\arctan 2(y_{d}-y_{a},x_{d}-x_{a})\) and the bar length \(l_{1}=\sqrt{(x_{d}-x_{a})^{2}+(y_{d}-y_{a})^{2}}\) can be calculated from the fixed points \(A\) and \(D\), the equation of the closed loop formed by the bars of the mechanism in Figure 1 can be written in vector form as:  
\[l_{2}e^{iq_{2}}+l_{3}e^{iq_{3}}=l_{1}e^{iq_{1}}+l_{4}e^{iq_{4}} \tag{11}\]  
Since \(e^{i\theta}=\cos(\theta)+j\sin(\theta)\), the vector equation in (11) is separated into the following two equations for the real and complex parts:  
\[\begin{split} l_{2}\cos(q_{2})+l_{3}\cos(q_{3})&=l _{1}\cos(q_{1})+l_{4}\cos(q_{4})\\ l_{2}\sin(q_{2})+l_{3}\sin(q_{3})&=l_{1}\sin(q_{1 })+l_{4}\sin(q_{4})\end{split} \tag{12}\]  
For simplicity, the known terms in (12) are grouped as follows:  
\[\begin{split}\alpha_{c}&=l_{1}\cos(q_{1})-l_{2}\cos (q_{2})\\ \alpha_{s}&=l_{1}\sin(q_{1})-l_{2}\sin(q_{2})\end{split} \tag{13}\]  
Then, (12) is rewritten as:  
\[\begin{split} l_{3}\cos(q_{3})&=\alpha_{c}+l_{4} \cos(q_{4})\\ l_{3}\sin(q_{3})&=\alpha_{s}+l_{4}\sin(q_{4})\end{split} \tag{14}\]  
Squaring the equations in (14) yields:  
\[\begin{split} l_{3}^{2}\cos^{2}(q_{3})&=\alpha_{c}^{2 }+2\alpha_{c}l_{4}\cos(q_{4})+l_{4}^{2}\cos^{2}(q_{4})\\ l_{3}^{2}\sin^{2}(q_{3})&=\alpha_{s}^{2}+2\alpha _{s}l_{4}\sin(q_{4})+l_{4}^{2}\sin^{2}(q_{4})\end{split} \tag{15}\]  
The simplified sum of the two equations above results in the following:  
\[l_{4}^{2}-l_{3}^{2}+\alpha_{c}^{2}+\alpha_{s}^{2}+2\alpha_{c}l_{4}\cos(q_{4}) +2\alpha_{s}l_{4}\sin(q_{4})=0 \tag{16}\]  
Then, the equation in (16) can be expressed in [PERSON]'s form [50]:  
\[k_{1}\sin(q_{4})+k_{2}\cos(q_{4})+k_{3}=0, \tag{17}\]where:  
\[\begin{split} k_{1}&=2\alpha_{s}l_{4}\\ k_{2}&=2\alpha_{c}l_{4}\\ k_{3}&=l_{4}^{2}-l_{3}^{2}+\alpha_{c}^{2}+\alpha_{s} ^{2}\end{split} \tag{10}\]  
The solution to the Freudenstein equation in (11) makes it possible to calculate the unknown angle \(q_{4}\) from the input \(q_{2}\):  
\[q_{4}=2\arctan 2\biggl{(}-k_{1}\pm\sqrt{k_{1}^{2}+k_{2}^{2}-k_{3}^{2}},k_{3}-k_ {2}\biggr{)} \tag{11}\]  
On the other hand, the unknown angle \(q_{3}\) can be calculated using the value of \(q_{4}\) from (11) in (12):  
\[q_{3}=\arctan 2(\alpha_{s}+l_{4}\sin(q_{4}),\alpha_{c}+l_{4}\cos(q_{4})) \tag{12}\]
## 1 Introduction
### Literature Review
These parameters were then incorporated into a microscopic traffic model for realistic traffic characterization of CAVs. Cyberattack intensity is also considered in this model to assess the impact on traffic. The proposed and ID models were discretized using the Euler scheme and implemented in MATLAB code. The results for both models are analyzed and compared. In this paper, a microscopic traffic model is presented to investigate the behavior of CAVs at bottlenecks considering cyberattacks. This is based on data collected from a sensor node installed on University Road in Peshawar, Pakistan. This is the main city road that connects various government organizations and educational institutions. Most of the classes scheduled in the educational institutions are between 9:00 AM and 4:00 PM. This creates the highest traffic volumes in the city which results in frequent bottlenecks. The data are analyzed as in [44] and the traffic parameters are integrated with the ID model to obtain a new model. A custom MATLAB module was developed to solve the differential equations for the proposed and ID models using the Euler scheme. This was used to simulate a platoon of vehicles on a 1 km circular road. MATLAB is employed for performance evaluation based on its versatility and interactive environment. The language is simple and is ideal for quick modeling. The results obtained demonstrate that the proposed model realistically characterizes traffic at bottlenecks and so can be used to improve traffic efficiency and manage traffic flow, particularly for CAVs. Further, it provides useful results considering cyberattacks. This is important at bottlenecks where traffic flow should be closely controlled. This model can also be used to replace traffic control methods such as VSL to provide better flow based on CAV reaction time and distance headway. The rest of this paper is structured as follows. Section 2 details cyberattacks in CAVs and Section 3 presents the proposed model. The proposed and ID models are evaluated in Section 4, and Section 5 concludes the paper.
## 1 Introduction  
The mite _Varroa destructor_[1] is one of the main threats to the European honeybee _Apis mellifera_ and, thus, for the beekeeping sector [2]. The number of suitable veterinary medicine products (VMPs) for the treatment is limited [3] due to the need of low honey bee toxicity [4], the risk of residues in honey bee products [5, 6] and the _V. destructor_ mite capability to develop resistance to the frequently used active substances [7]. In this context, the availability of new VMPs in the market, as well as upgraded treatment measures or tools (i.e., brood interruption techniques) can be valuable help for beekeepers [4, 8].  
From previous studies, it is known that soft acaricides [7] have one or more components as an active ingredient: organic acids (oxalic [9, 10, 11, 12, 13], formic [14, 15, 16] and lactic acid [17]), other acids [18], thymol [19, 20, 21, 22] and other essential oils [23, 24]. Recently, different formulations of natural ingredients were evaluated, such as a formulation composed of oxalic acid, thymol and oregan oil [25], and some were compared or combined with brood interruption technique [8, 26]. Since 2017, VarroMed(r), an organic acid-based VMP against Varroa was registered for its use on the honey bees in EU.  
In the context of the Varroa control task force of the COLOSS association [27], we decided to test the performances of VarroMed(r) concerning efficacy against Varroa mite and toxicity for honey bees in different European climatic conditions in agreement with EMA guidelines [28], following the instructions given by the producer.  
The aim of our study was to test the application of VarroMed(r) in different conditions and to give detailed and reliable information to European beekeepers in the framework of a Varroa management strategy.
## 5 Discussion
In our solution, we consider the use of image compression function (not less than 16\(\times\)), which can transmit 1080P real-time video and high-resolution images of not less than 32 M in real time in the later stage. From this, it can be seen that our solution can fully meet emergency response needs. So far, disaster management based on aerial remote sensing has been commonly used for building damage assessment after earthquakes. In this assessment, due to its ability to obtain on-site data and process it as information on the area, quantity, rate, and type of damage, rescue teams will have a better understanding of the safe path and potential damage caused by secondary impacts [26]. In addition, it is also used for landslide dynamic monitoring [14; 35] and change detection of coastal facilities to help assess the vulnerability caused by natural disasters such as tornadoes [59; 60]. However, these applications do not require high real-time performance, but for natural disaster emergency response, fast information acquisition and transmission are required. With the rise of low-cost and high-payload commercial drones in recent years, as well as the maturity of airborne mobile communication equipment technology in the Ku- and KA-bands [45], aerial remote sensing platforms are more maturely applied to natural disaster emergency rescue. For example, during the rainstorm disaster in Henan Province, China on 20-21 July 2021, the \"petrosaur-2H\" emergency relief UAV produced by Aviation Industry Corporation of China, Ltd., Beijing, China operated for about 8 h in the disaster area. The aerial camera, optoelectronic equipment, and synthetic aperture radar were used to take pictures and monitor the disaster area, and relevant information was sent back to the command center in real time to realize efficient and accurate command of emergency rescue operations ([[https://mp.weixin.qq.com/s/pk0](https://mp.weixin.qq.com/s/pk0) JGhBuyyrR9a-ylhr65w]([https://mp.weixin.qq.com/s/pk0](https://mp.weixin.qq.com/s/pk0) JGhBuyyrR9a-ylhr65w), accessed on 25 July 2021). According to the test results, it is believed that the aerial remote sensing platform based on real-time satellite communication proposed in this paper will be successfully applied to natural disaster rescue in the future.
## 3 Results and Discussion
### Numerical Simulation Set-Up
#### 3.1.4 Optimization  
For realistic ocean engineering applications, the lattice constant \(a\) and the water depth \(h\) are usually on the order of several meters and these parameters can be converted to the real water wave conditions when the wave velocity, the wavelength and the water level are known. For example, for the square lattice structure, when \(h_{0}/h=9\) and \(r/a=0.25\), the maximum amplitude \(H\) is predicted to be 3.86, at which point the normalized frequency is 0.46. If the wave velocity extends from 0.78 to 1.05 m/s, the wavelength is from 4.05 to 5.46 m and the water level is about 20 m. With this design \(h\) is 2.22 m, \(a\) is 0.46 \(\times\) (4.05 + 5.46)/2 = 2.19 m and \(r\) is 0.55 m. Table 6 lists the recommended geometric parameters and the corresponding frequencies with which the best enhancement factor can be realized in normal sea conditions. It should be pointed out, that the range of \(r/a\) of the square structure is appreciably larger compared to those of the other two. However, notice that the above enhancement factor may be inaccessible when \(r/a\) is too large because of geometry limit and/or frequency constraint.  
Next, the dependence of \(H\) on \(f_{n}\) for the three lattice types is displayed in Figure 6. The four curves correspond to different lattice types with different values of \(h_{0}/h\). We can see that the largest \(H\) of 5.3 occurs at \(f_{n}\) = 0.6 in the square structure. At low frequencies, the wave interference is not obvious for any given lattice type so the enhancement is not significant. It can be seen, that larger \(H\) appears when \(f_{n}\) is larger than 0.32. Overall, the \(H\) for the hexagonal lattice type is appreciably less as compared to the other two.
## 2 Related Work
Other methods use neighbor-knowledge to form clusters and minimize the broadcast messages produced by flooding. One example is MPR (MultiPoint Relay) [28], in which each node periodically broadcasts hello messages to obtain a list of all its two-hop neighbors. Every node selects some symmetric nodes in its neighborhood called MultiPoint Relays, and only those nodes are selected to retransmit route search packets. In MPR, the number of retransmissions depends on the number of multipoint relays selected. MPR, used by OLSR (Optimized Link State Routing) [29], reduces duplicated retransmissions locally. However, maintaining this physical organization of the multipoint relays involves the use of a significant amount of signaling. The area-based method [22] is a different strategy for flooding packets in search of a destination, in which nodes use location information to decide whether or not to rebroadcast search packets. This method's main disadvantage is that it relies on location hardware.