esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Transmit Subsampling
#### 3.2.5 Performance in the Presence of Structural Noise  
In addition to the investigations in the last subsection, which display the performance when the data is subject to 0 dB additive white Gaussian noise, in order to emulate a highly scattering medium we also added more realistic structural noise in CIVA. The scatterers were placed with a density of 100 pts/mm\({}^{3}\) and amplitude 1. The reconstructions of data subject to both noise sources are displayed in Figure 10.  
Using fewer transmit cycles clearly degrades the imaging quality in the presence of structural noise, with the effect being more pronounced than for the dataset that only contained additive white Gaussian noise in Figure 9. This is due to the fact that the additional small scatterers in the medium that emulate the structural noise show up over multiple measurements according to the forward model and therefore cannot simply be averaged out like the zero-mean Gaussian noise. In the presence of such small-scale scattering events, FISTA achieves a visually better defect to noise separation due to the sparsity constraint.  
In consequence, the simulation shows that for a real world application of the subsampling algorithm to a highly scattering specimen the noise level from e.g., the grain needs to be taken into account. Based on FMC measurement data, a tuning of the number of transmit channels should be performed.
## 3 Calculation Method for Assessment of the Energy Consumption of the Pump Unit  
The electrical power consumed from the grid is calculated using the interpolated pump mechanical power and interpolated motor efficiency according to Formula (1):  
\[P_{1.i.m}=P_{mech.i.m}/\eta_{M.i.m} \tag{1}\]  
where \(P_{mech.i.m}\) is the pump mechanical power; \(\eta_{M.i.m}\) is the efficiency of \(m\)-th motor in \(i\)-th loading point. Second-order polynomial interpolation of the motors' loss curves was used for the calculation of the efficiency at each loading point. As it is shown in [27], losses as a function of the load may be described by a second-order polynomial curve, the coefficients of which can be calculated using the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Type** & \(P_{RATE}\)**, W** & \(n_{RATE}\)**, rpm** & \(Q_{BEP}\)**, m3/h** & \(H_{BEP}\)**, m** & **Efficiency, \%** \\ \hline \multirow{2}{*}{Value} & NMS4 & \multirow{2}{*}{75,000} & \multirow{2}{*}{1450} & \multirow{2}{*}{352} & \multirow{2}{*}{50.3} & \multirow{2}{*}{81} \\  & 150/400A/A & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nameplate data of the pump.  
Figure 1: Pump interpolated curves and initial points: (**a**) \(Q\)-\(H\) curve of the pump and dependence of the pump power on water flow; (**b**) dependence of the pump efficiency on water flow.  
efficiencies at three standard loading points (50%, 75%, 100%) provided by the manufacturers of the electric motor.  
The electricity consumed per annum by one of the considered motors assuming the flow-time diagram shown in Figure 2 is determined using the Formula (2), where \(t_{\Sigma}\) is the total time of the operating cycle (24 h) and \(t_{i}\) is the operating time of the pump in \(i\)-th loading point (see Figure 2).  
\[E_{y.m}=365\cdot t_{\Sigma}\cdot\sum_{i=1}^{3}\left(P_{1.i.m}\cdot\frac{t_{i}}{ t_{\Sigma}}\right). \tag{2}\]
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 3 Setup of the Lidars  
### PPI Scans  
During phase 1, the three WindScanners performed PPIs covering an azimuthal range of 60\({}^{\circ}\) with an elevation angle of \(\approx\)1\({}^{\circ}\). The scans were centered at directions of 250\({}^{\circ}\), 270\({}^{\circ}\) and 290\({}^{\circ}\) for Sterenn (pos. 3), Vara (pos. 2) and Koshava (pos. 1), respectively. Each scan took 60 s to complete, i.e., the lidar was accumulating 1 s in each 1 degree azimuthal sector. There were 154 range gates for each LOS, except for Vara which had 156 range gates.  
During phase 2 and 3 of the campaign, Vara (pos. 2) was the only lidar performing 60\({}^{\circ}\) PPI scans facing west but at three different elevation angles. These elevation angles were chosen such that the scans reach 50, 100 and 150 m ASL near position 7. An integrated velocity azimuth processing (IVAP) algorithm was used to reconstruct the horizontal wind components at each range gate. The IVAP algorithm uses a summation of the angularly separated LOS velocities [22].  
During phases 2 and 3, the full trajectory was taking 145 s to complete; 45 s for each scan at the three different elevations and 10 s to travel back to the first measurement point. Each scan obtained 45 LOS velocities with 1 s accumulation time. Each LOS contained 156 range gates from 100 to 8150 m, so for each full scan 468 reconstructed horizontal wind components were obtained  
During phase 4, Vara (pos. 2) was performing a PPI scan with an azimuthal range of 120\({}^{\circ}\) centered at 270\({}^{\circ}\). For each 1\({}^{\circ}\), the lidar was accumulating LOS spectra during 1 s. The elevation angle during this period was \(\approx\)0\({}^{\circ}\) and for each LOS there were 159 range gates.  
The reconstruction algorithm described in [PERSON] and [PERSON] [23] was applied to the PPI scans in phases 2 and 3 only (so hereafter PPI refers to such PPIs) for each scan and data were filtered out if the average carrier-to-noise ratio (CNR) of the 45 LOSs at each range gate was below \(-\)27 dB. The estimated wind components were then averaged over 10, 30 and 60-min periods, i.e., using 4, 12 and 24 reconstructed velocities per averaged value, respectively. In this article, we only use the 10 min mean values.
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 5 Challenges and Opportunities
### Attackers
For instance, an attacker might simultaneously launch a spoofing attack while also introducing jamming or timing manipulation. By attacking from multiple angles, attackers can overwhelm the system's defenses, which are often designed to handle one type of attack at a time. These multi-modal attacks could exploit the interactions between different parts of the system to create complex failure modesthat are difficult for the system to isolate or mitigate. For example, by jamming certain frequencies while introducing subtle spoofing signals on another, attackers can confuse the system's decision-making process and evade detection. The challenge here is for attackers to develop a deep understanding of the interdependencies between various WiFi sensing components and defense mechanisms to launch successful, highly targeted attacks. These advanced attacks can catalyze significant advancements in both WiFi sensing and defense technologies. The continuous interplay between attack and defense not only strengthens the security of WiFi sensing systems but also fosters innovation in the field, leading to more effective and adaptive sensing technologies.
# Two-Layer Network Caching for Different Service Requirements
## 3 Model
### Caching Problems  
In this section, we identify different caching problems, starting from a basic one, analyze their complexity, propose heuristic solutions, and determine their computational cost. We present the principal used notation in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Meaning** \\ \hline \(i\): & index of layer-1 _caches_ (or attached _users_); \\ \(j\): & index of layer-2 _caches_; \\ \(m\): & index of _variable-length_ items; \\ \(\xi\): & index of _fixed-length_ items (or fixed-length _chunks_ of variable length items); \\ \(\xi^{*}\): & index of selected \(\xi\) for caching; \\ \(m^{*}\): & index of selected \(m\) for caching; \\ \(\{m^{*}\}\): & pre-selected set of items for selection; \\ \(\phi_{ij}=1\) & if the layer-1 cache i is connected with the layer-2 cache \(j\), otherwise \(\phi_{i,j}=0\); \\ \(\Phi\): & \([W\times|K_{2}|]\) matrix of the \(\phi_{i,j}\) values \\ \(g_{i,j}=1\) & if item \(\xi\) is in layer-2 cache \(j\), otherwise \(g_{i,j}=0\); \\ \(q_{i,j}=1\) & if item \(\xi\) is in layer-1 cache \(i\), otherwise \(q_{i,j}=0\); \\ \(\zeta(\xi,m)=1\) & if a chunk \(\xi\) is part of an item \(m\); \\ \(\Xi\): & set of items; \\ \(K_{2}\): & set of layer-2 caches; \\ \(K_{2,j}\): & size of \(j\)-th layer-2 cache; \\ \(K_{1,i}\): & size of \(i\)-th layer-1 cache; \\ \(P_{MZ,i}(\xi)\): & Zipf-Mandelbrot popularity of content \(\xi\) for user \(i\); \\
**P**: & \([W\times C]\) popularity matrix. \\ C: & cardinality of the set of items; \\ \hline \hline \end{tabular}
\end{table}
Table 1: Principal Notation.
## 4 CLAP Correlation and Information Sharing Services
### Correlation and Cross-Correlation Process
#### 4.1.2 Definition of Statements  
A statement specifies the types of events and creates a data structure where to store them. Internally the platform creates a table in a database for these events, which are used by the directives for its correlation and further identification of anomalies. A statement is composed of a unique name and a value that indicates the variables and the sources from which the information will be evaluated. Statements do not fire alerts, but the generated output is used by other services. Table 2 provides Examples of EPL statements.  
It is worth noting that EPL statements require the specification of two variables: (i) plugin_id corresponding to the data sources (e.g., IDS, JDet, NTSA, FCAC); and (ii) plugin_sid corresponding to the type of events we want to consider in the correlation. We can specify one or more events for a particular plugin by indicating its plugin_sid. If we want to consider all events associated with a particular plugin, we need to specify only the plugin_id (as done with the jammer detector statement).
# \(L_{1}\) Adaptive Control for a Vertical Rotor Orientation System
## 6 Experimental Results for the Rotor Orientation System
### Experimental Process  
The block diagram is illustrated in Figure 8. In the experiment, we choose dSPACE as the digital controller.  
The experimental program is as follows:  
1. Measurement of the rotor drift angle.  
The drift angles of the rotor in the \(X/Y\) directions \(angle_{x}\) and \(angle_{y}\), respectively, are converted from the two gap values \(gap_{1}\) and \(gap_{2}\). The two angles are the state variables \(x_{1}\) and \(x_{2}\) in Equation (2).
2. Calculation of the electromagnetic force. According to Equations (15) and (39), the control torque is: \[\mathbf{u}=\left[\begin{array}{c}T_{\rm re}\\ T_{\rm ye}\end{array}\right]=\mathbf{u}_{\rm m}+\mathbf{u}_{\rm ad}\] (43) The torque is given by: \[\begin{cases}T_{\rm re}=(F_{y-}-F_{y+})h_{\rm m}\\ T_{\rm ye}=(F_{x+}-F_{x-})h_{\rm m}\end{cases}\] (44) where \(F_{x+},\ F_{x-},\ F_{y+}\) and \(F_{y-}\) are the forces of each electromagnet in the \(X\) and \(Y\) directions and \(h_{\rm m}\) is the installation height of the electromagnets.  
Because the electromagnetic force can only be attractive, to make them continuous and smooth, the forces of each direction are realized via a bias control, i.e.,  
\[\left\{\begin{aligned} F_{x+}&=F_{\rm n0}+\frac{M_{ \rm ye}}{2h_{\rm m}}\\ F_{x-}&=F_{\rm n0}-\frac{M_{\rm ye}}{2h_{\rm m}}\\ F_{y+}&=F_{\rm n0}-\frac{M_{\rm ye}}{2h_{\rm m}}\\ F_{x-}&=F_{\rm n0}+\frac{M_{\rm ye}}{2h_{\rm m}}\\ \end{aligned}\right. \tag{45}\]  
where \(F_{\rm n0}\) is the reference value of each electromagnet, about which each force varies. The control force signal for each electromagnet is determined through Equations (43) and (45).  
Figure 7: Experimental platform for the orientation control system: (**a**) electromagnet; (**b**) rotor; (**c**) universal joint; (**d**) motor.  
3. Calculation of the current in every electromagnet. The electromagnetic force of each electromagnet yields: \[F=\frac{AI^{2}}{(B+g)^{2}}\] (46) where \(A\) and \(B\) are factors corresponding to the electromagnet. Substituting Equations (46) into the control force Equations (45) gives the current signal of each electromagnet. \[\left\{\begin{aligned} & I_{x+}=(B+g_{x+})\sqrt{\left(F_{n0}+ \frac{M_{xc}}{2l_{\text{m}}}\right)/A}\\ & I_{x-}=(B+g_{x-})\sqrt{\left(F_{n0}-\frac{M_{xc}}{2l_{\text{m}} }\right)/A}\\ & I_{y+}=(B+g_{y+})\sqrt{\left(F_{n0}+\frac{M_{yc}}{2l_{\text{m}} }\right)/A}\\ & I_{y-}=(B+g_{y-})\sqrt{\left(F_{n0}-\frac{M_{yc}}{2l_{\text{m}} }\right)/A}\end{aligned}\right.\] (47) where \(g_{x\pm}\) and \(g_{y\pm}\) are the gaps of each electromagnet. The current signal in Equation (47) can be loaded onto each electromagnet via a power amplifier.
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
## 3 Preliminaries
### Syntax of the Proposed CLAS Scheme  
The syntax of our CLAS contains the following sub-steps:  
_Setup:_ The DoT can play the role of a TA, and it will be able to run this phase upon receiving the security parameter, in which it can first choose the hyperelliptic curve. Then, the DoT can set the private and public keys. Further, it generates public param and publishes it to the network.  
_Partial Private Key Generation (PRPKG):_ Any participating user who needs a partial private key can first encrypt their real identity using the common secret key between the DoT and that particular user. After doing this, the user sends an encrypted identity to the DoT using an insecure network. Then, the DoT decrypts the encrypted identity and recovers the real identity. Further, the DoT generates the partial private key for that particular identity, encrypts it, and delivers it to the user using an insecure network.  
_Private Key Generation (PRKG):_ In this section, the user generates his public and private key pairs.  
_Signature Generation (SIGG):_ This section will be run by the OBU to generate and send the signature tuple (\(S_{OBU}\), \(W_{OBU}\)) to the RSU.  
_Signature Verifications (SIGV):_ This section will run by the RSU to verify the signature tuple (\(S_{OBU}\), \(W_{OBU}\)).
# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 3 Proposed Method
### Slope and Curvature Analysis with Road Structure Information  
The slope of roads is one of the critical factors in assessing the risk of traffic accidents caused by black ice [25]. It is certainly difficult to stop in road segments with sharp turns covered with black ice; therefore, major accidents may occur. For this reason, we created a road network to estimate the slope and curvature of roads in Korea, as shown in Figure 5. The related information and maps for the current highways and national roads were extracted from the Road Management System (RMS). Figure 6 shows an example of the slope information collected from the RMS.  
Figure 4: Hillshade simulation with 3D spatial information.  
Figure 3: Altitude and azimuth angles of the sun in Korea.  
In contrast, roads maintained by local governments are not managed by the RMS. Consequently, field surveys were necessary to obtain road structure information. In preparation for new innovations such as autonomous driving, the government has been building 3D spatial information using mobile mapping systems on some urban roads. Therefore, relevant information about the local roads in unpopular (i.e., for tourism) mountain areas is missing. To address this problem, we obtained the spatial data for segmentation of the roads using an unmanned aerial vehicle (UAV) (Figure 7). Recently, South Korea's national land development corporations have been actively advocating for the establishment of spatial information using UAVs. Public enterprises in Korea, such as Korea Expressway Corporation and Korean Land and Infomatix Corporation, have established UAV imaging platforms and actively promote image-based spatial data construction and measurement [26]. Such data are useful for generating precise 2D orthomosaic images and 3D geospatial data (see  
Figure 5: The full road network data of Korea obtained from Road Management System (RMS) and field surveys.  
Figure 6: An example of the slope information collected from the Road Management System (RMS).  
Figure 7) and estimating slope angles of various road segments by overlaying the road network data generated from RMS. The orthoimages and 3D models were generated by Pix4D Mapper 4.0, and the DSMs were created by ContextCapture 4.0. In this study, a fixed-wing UAV, sensFly ebee X was mainly used to take images along road segments, and the resolution of the 3D models and DSMs was 10 cm.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Country** & **BN** & **KH** & **ID** & **LA** & **MY** & **MM** & **PH** & **SG** & **TH** & **VN** \\ \hline BN & 0.000 & 1.356 & 0.697 & 1.866 & 0.598 & 1.240 & 0.741 & \(-\)0.241 & 0.170 & 0.453 \\ KH & 0.684 & 0.000 & 0.697 & 1.811 & 0.945 & 1.035 & 0.850 & 0.119 & 0.479 & 0.393 \\ ID & 0.414 & 1.108 & 0.000 & 1.843 & 0.360 & 1.132 & 0.751 & \(-\)0.155 & \(-\)0.076 & 0.420 \\ LA & \(-\)0.196 & \(-\)0.153 & \(-\)0.285 & 0.000 & \(-\)0.280 & 0.007 & \(-\)0.274 & \(-\)0.351 & \(-\)0.349 & \(-\)0.323 \\ MY & 0.568 & 1.176 & 0.524 & 1.686 & 0.000 & 1.404 & 0.700 & \(-\)0.036 & 0.137 & 0.663 \\ MM & 0.293 & 0.444 & 0.520 & 1.433 & 0.499 & 0.000 & 0.569 & \(-\)0.175 & \(-\)0.122 & \(-\)0.215 \\ PH & 0.379 & 0.807 & 0.304 & 1.827 & 0.385 & 1.155 & 0.000 & 0.002 & \(-\)0.020 & 0.123 \\ SG & 1.800 & 2.097 & 1.900 & 2.730 & 1.860 & 2.249 & 2.120 & 0.000 & 1.445 & 1.542 \\ TH & 1.239 & 1.526 & 1.267 & 2.468 & 1.120 & 1.714 & 1.417 & 0.035 & 0.000 & 0.812 \\ VN & 0.797 & 1.247 & 0.932 & 2.347 & 0.856 & 1.529 & 1.080 & 0.018 & 0.300 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The level of dominance with the loss attenuation coefficient \(\mu=60\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Country** & **BN** & **KH** & **ID** & **LA** & **MY** & **MM** & **PH** & **SG** & **TH** & **VN** \\ \hline BN & 0.060 & 0.000 & 0.008 & 0.001 & 0.005 & \(-\)0.002 & 0.031 & 0.038 & 0.012 & 0.011 \\ KH & 0.000 & 0.317 & 0.099 & 0.242 & 0.088 & 0.213 & 0.002 & 0.176 & 0.115 & 0.014 \\ ID & 0.008 & 0.099 & 0.116 & 0.147 & 0.090 & 0.131 & \(-\)0.001 & 0.060 & 0.099 & 0.000 \\ LA & 0.001 & 0.242 & 0.147 & 0.638 & 0.131 & 0.425 & 0.007 & 0.238 & 0.168 & 0.030 \\ MY & 0.005 & 0.088 & 0.090 & 0.131 & 0.102 & 0.116 & 0.000 & 0.038 & 0.075 & 0.000 \\ MM & \(-\)0.002 & 0.213 & 0.131 & 0.425 & 0.116 & 0.458 & 0.000 & 0.218 & 0.151 & 0.020 \\ PH & 0.031 & 0.002 & \(-\)0.001 & 0.007 & 0.000 & 0.000 & 0.050 & 0.020 & 0.001 & 0.023 \\ SG & 0.038 & 0.176 & 0.060 & 0.238 & 0.038 & 0.218 & 0.020 & 0.833 & 0.110 & \(-\)0.020 \\ TH & 0.012 & 0.115 & 0.099 & 0.168 & 0.075 & 0.151 & 0.001 & 0.110 & 0.173 & 0.000 \\ VN & 0.011 & 0.014 & 0.000 & 0.030 & 0.000 & 0.020 & 0.023 & \(-\)0.020 & 0.000 & 0.039 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The distance covariance of alternatives according to indicator between C1 and C2.
# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 4 Computational Experiments
### Preliminary Experiments
#### 4.1.1 Scaling and Precision  
For technical reasons, the CP solver requires the use of integer coefficients in the constraints; then, scaling is necessary to represent floating point values for the coefficients that appear in constraints (4) and (5) of our model. In practice, the coefficients need to be multiplied by a constant \(F\) and, then, only the integer part is considered. Once the optimal solution is found, the value of \(\alpha\) has to be scaled back by dividing by \(F\). The choice of the scaling factor \(F\) has a double impact on the performance of the solver: high values for the factor guarantee higher numerical precision in the results (once scaled back), but, at the same time, lower numerical precision leads to faster execution. Therefore, a trade-off has to be found.  
We carried out a preliminary study to identify a suitable value for the factor \(F\) that would then be adopted for the rest of the experiments. A representative instance to illustrate our results was identified in _berlin52\(0\)80\(2\)2\(1\) (originally proposed in [7] and with an optimal solution of cost 5290.652). The results obtained by considering \(F\in\{1;10;100;1000;10,000;100,000;1,000,000\}\) are reported in Figure 2. In the first chart, we report the computation time (in seconds) required by the solver for different values of \(F\); in the second chart, the value of the optimal cost affected by precision errors (estimated), and the value of the real cost of the same solution, are plotted.  
Figure 2: Times and optimal objective function cost for different values of the scaling factor \(F\)— instance _berlin52\(0\)80\(2\)2\(1\) from [7].  
From Figure 2, it can be observed that smaller scaling factors correspond to shorter computing times, both to retrieve the optimal solution and to confirm its optimality. On the other hand, the precision associated with a smaller scaling factor is not sufficient, as shown by the poor quality of the estimation of the cost of the tentative optimal solution retrieved. The results presented are illustrative and representative. Based on the results, we chose to use \(F=10,000\) for the remaining experiments. This value guarantees acceptable computation times and sufficiently safe precision for floating point numbers. Note that, from the results, the choice can appear as over-conservative, since values such as 100 and 1000 for \(F\) have already been shown to be good approximations; however, since the computation time associated with \(10,000\) is only marginally increased, we preferred it, to be sure to avoid any numerical instability problems in our results.
## 4 Methodology of Technical-Economic Analysis
### Payback Period  
The payback period (\(PP\)) is defined as the number of years (\(n_{r}\)) for which the condition is met:  
\[NPV=\sum_{i=1}^{n_{r}}\frac{IN_{i}-C_{i}-I_{i}}{\left(1+a\right)^{i}}=0 \tag{4}\]  
\[PP=n_{r} \tag{5}\]  
where \(PP\) represents the objective duration of exploitation, at the end of which the initial investment can be covered and an additional income corresponding to the considered discount rate can be achieved [9; 10].  
Defining the payback period requires establishing an origin of time. The accepted convention is to calculate this duration starting from the moment of implementation of the respective objective.  
The decision to accept or eliminate an investment project should be taken by comparing the recovery period of the capital \"\(n_{r}\)\" with the lifetime of the objective \"\(n\)\" (in the analysed case, 20 years). Theoretically, if \(n_{r}\leq n\), the investment project can be accepted, bringing net updated incomes, and if \(n_{r}>n\), the project must be rejected; it will not bring net incomes over the lifetime of the proposed solution.  
In practice, the project is considered economically efficient based on this indicator, if the value calculated for the \(PP\) is compared with a reference value (\(T_{r}\)), called the standard return of investment. From the literature, it results that the standard term is in the following range:  
\[T_{r}\leq\left(\frac{1}{2}\div\frac{1}{3}\right)\cdot n \tag{6}\]  
A particular case is the gross value of the payback period (\(GPP\)), which does not take into account the discount rate (\(a\)). The value for the \(GPP\) is determined at the beginning of the investment and is defined as the following:  
\[GPP=\frac{I}{IN-C} \tag{7}\]