esa-sceva/satcom-chunk-collection huggingface dataset:
# WhistleGAN for Biomimetic Underwater Acoustic Covert Communication
## 2 Proposed Method
### WhistleGAN  
When a whistle generative network is designed, reflecting the characteristics of the desired whistle is important. In the biomimetic UWA covert communication, the transmitted signals are eavesdropped by humans. Thus, it is necessary to generate the whistles by reflecting the characteristics of the human ear.  
In this paper, we propose the WhistleGAN, which uses the Mel filter bank to generate new whistles with higher mimicking than conventional methods with a lower computation. Since human hearing is perceived in octaves, learning nonlinear data based on the human ear can produce a more imitative whistle than learning a traditional discrete Fourier transform (DFT)-based linear scale spectrogram. Learning a nonlinear scale that is log-like rather than linear in the frequency domain reduces the spectrogram data size, and whistles with a high degree of mimic (DoM) are generated by learning the human hearing characteristics.  
The proposed WhistleGAN consists of a generator network \((G)\) and a discriminator network \((D)\) with Mel filter bank. The goal of the \(G\) is to generate fake whistles similar to the real whistles to fool the \(D\) into judging the generated whistles as the real whistles. The \(D\) takes the fake whistles generated by the \(G\) and the real whistles as input and decides whether the input is real. Since the two networks have conflicting goals, they train competitively and simultaneously to generate new whistles.  
The Mel filter bank reduces the image size of the whistle spectrogram by converting a linear scale frequency to a logarithmic scale, known as the Mel scale. This processing emphasizes the low-frequency bins that correspond to human auditory sensitivity in detail, while sparsely representing high-frequency regions. The bilateral filter is used to remove noise that may occur during the GAN training process. The block diagram of the proposed WhistleGAN is shown in Figure 2.  
Figure 1: The dolphin’s call signals.
# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 6 Case Studies
### Network Diagnosis and Optimization
#### 6.2.2 Optimization Stage  
Considering that the cells causing low coverage to UEs have been identified in the previous stage, the optimization of the respective EDTs is a possible solution to restore (or even improve) QoS. Thus, the antenna tilt optimization algorithm was configured to maximize the coverage according with the previously used low coverage threshold, \(-120\) dBm. In Figure 19, the DT measurements corresponding to that geographical area were filtered to only the four cells previously associated with the low coverage areas, allowing to visualize their joint service area.  
The antenna tilt algorithm considered the cell's initial EDT configuration, and optimized the EDT configuration by evaluating the coverage impact in all the DT measurements represented in Figure 19. Interestingly, the algorithm suggested keeping the actual EDT configuration. In fact, these cells have wide service areas (see Figure 19) so, a new EDT configuration that would increase the coverage, in the identified areas, while not causing excessive degradation in other areas, was not found. Moreover, only 1.4% of the measurements have been identified below the serving threshold, which limits the effectiveness of a tilt optimization, without causing additional signal degradation.  
In scenarios where the antenna physical parameters are already optimized, a possible workaround is to optimize other configuration parameters, as the handover parameters. In this case, the DT measurements identify low coverage areas for a specific carrier frequency; however, other carriers might provide good coverage. To evaluate this hypothesis, another carrier frequency was represented in the study area (see Figure 20).  
Figure 19: The joint service area of the cells for optimization.  
Comparing the initial carrier coverage (Figure 14) with the new carrier presented in Figure 20, the coverage for this new carrier frequency does not present low coverage areas. Thus, the handover optimization algorithm can be executed for that area, to guarantee that when a UE reaches the low coverage area, the inter-frequency parameters, allow an handover to a new frequency, maintaining QoS. However, as for the study area, there were not dedicated DTs (mandatory for the execution of the handover optimization algorithm). Hence, for demonstration purposes, an example is presented in Figure 21.  
Figure 21: Inter-frequency handover optimization algorithm example.  
Figure 20: Lower carrier frequency coverage (DTs).  
In Figure 21, two areas with low coverage in dedicated mode are represented. These areas were optimized by adjusting the inter-frequency parameters between two distinct carrier frequency cells that serve in that specific area. Applying the algorithm's proposed configuration, the network will allow UEs to handover from the worst coverage carrier frequency to the other, before the service QoS drops excessively.
## 2 Materials and Methods
### Mutagenicity Test  
The Ames test was carried out as previously described by [PERSON] and [PERSON] [39], but it was adapted to a miniaturized version in 6-well plates to reduce the test product needed. The assay based on [PERSON] et al. [40] with some modifications, was performed with _Salmonella typhimurium_ strains TA97a and TA98 at exponential growth conditions and a concentration of 2 \(\times\) 10\({}^{9}\) bacteria/mL. Bacterial suspensions (25 \(\upmu\)L) were exposed to the test product (20 \(\upmu\)L) in the absence or presence of metabolic activation (100 \(\upmu\)L of S9 mix) for 30 min. Then they were poured into 6-well plates, containing 0.5 mL per well of minimal agar containing biotin and traces of histidine, to allow a few bacteria divisions. A commercial post-mitochondrial liver fraction from rat (S9) was used at 10% \(v/v\) (S9 mix), as the metabolic activation system.  
The assay included three technical replicates. In addition, a negative control was used for each strain with and without metabolic activation, which was 20 \(\upmu\)L/plate of the solvent used, and a positive control, also per strain and per metabolic condition. The positive controls used were: (a) without metabolic activation (0%S9), 4-nitro-o-phenylenediamine (NPD) 10 \(\upmu\)g/well for TA97a and 2 \(\upmu\)g/well for TA98; (b) with metabolic activation (10%), 2-aminoanthracene (AA) 2 \(\upmu\)g/well for TA97a, and 2-aminofluorene (AF) 10 \(\upmu\)g/well for strain TA98. All controls were also in triplicate.
## 4 Test System and Simulation Results  
### Configuration of Test System  
The test system for verifying the NCCS was built in PSCAD/EMTDC. Figure 8 shows the small-scale grid model, which consisted of a PMSG-based wind turbine, a BESS, two synchronous generators, and a static power load of 30 MW and 3.4 MVAR. The synchronous generators were based on a thermal power plant with an IEEE-type generic  
Figure 6: RoCoF and RoCoF constant \(\alpha\).  
Figure 7: Flowchart of the proposed NCCS.  
steam turbine model, an approximate mechanical-hydraulic control governor model, and a type 1 excitation system [14; 15; 16; 17; 18]. The inertia constants of 50 MVA (SG #1) and 10 MVA (SG #2) were 6.0 s and 4.5 s, respectively. The droop coefficient of all synchronous generators was set to 5%. The initial operating points of each generator were 0.4 pu and 1.0 pu, respectively. The capacity of the PMSG-type wind turbine was 7 MW with a wind speed of 8 m/s, which can generate an output of 3.3 MW. The capacity of the BESS was 3 MWh, and the BESS included a 2 MW converter with an initial state of charge (SoC) of 0.5.
# Coefficient of Thermal Expansion of RCA Concrete Made by Equivalent Mortar Volume
## 2 Experimental Program
### Aggregates Properties  
Table 1 shows the specific gravity, and water absorption of 3 types of RCA, 2 types of natural coarse aggregate (NCA), and 2 types of fine aggregate. The specific gravity and the water absorption of the RR produced from concrete sleepers were 2.48% and 4.53%, respectively, and those of the RP, produced from concrete culvert, were 2.60% and 2.62%, respectively, and those of the RA, produced from a plant, were 2.52% and 3.82%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Items** & **RR \({}^{\text{a}}\)** & **RP \({}^{\text{b}}\)** & **RA \({}^{\text{c}}\)** & **NCA1** & **NCA2** & **Fine Agg1** & **Fine Agg2** \\ \hline Specific gravity & 2.48 & 2.60 & 2.52 & 2.65 & 2.71 & 2.56 & 2.60 \\ \hline Absorption ration (\%) & 4.53 & 2.62 & 3.82 & 0.70 & 0.37 & 1.10 & 0.95 \\ \hline RMC (\%) & 39.9 & 20.0 & 25.0 & - & - & - \\ \hline \hline \end{tabular} Note. \({}^{\text{a}}\). The first letter 'R’ refers to RCA, and the second letter 'R’ means its production from railway sleepers. \({}^{\text{b}}\). The first letter 'R’ refers to RCA, and the second letter ‘P’ means its production from PC culvert. \({}^{\text{c}}\). The first letter ‘R’ refers to RCA, and the second letter ‘A’ means general aggregate produced from a plant.  
\end{table}
Table 1: Basic RCA properties.  
Figure 1: RCA sources.  
Crushed granite was used as NCA; its specific gravity and water absorption were 2.65% and 0.70%, respectively, in test series 1, and 2.71% and 0.37%, respectively, in test series 2. River sand was used as fine aggregate; its specific gravity and water absorption were 2.56, 2.60 and 1.10, 0.95 respectively for fine aggregate 1 and fine aggregate 2. Table 2 provides details of the test series.  
The thermal treatment method suggested by [PERSON] and [PERSON] [41] was applied to calculate the RMC value of RCA. The prepared RCA samples were heated in an electric furnace at 500 \({}^{\circ}\)C for 2 h, and the heated samples were taken out of the electric furnace and immersed in ice water to apply a heat shock to separate the mortar. The measurements were inserted into Equation (1) to calculate the RMC values. The RMC values calculated for RR, RP and RA were 39.9%, 20.0% and 25.0%, respectively.  
\[\text{RMC}=(W_{\text{RCA}}-W_{\text{OVA}})/W_{\text{RCA}}\times 100 \tag{1}\]  
In Equation (1), \(W_{\text{RCA}}\) denotes the weight of RCA dried in a dryer after the first sampling; \(W_{\text{OVA}}\) is the weight of the original virgin aggregate (OVA) obtained by drying the sample in a dryer after removing the RM.
## 2 Signal Processing and Height Inversion
### Signal Processing  
#### 2.1.1 Data Fetch  
The IF data is read in 4 bytes at a time, which correspond to the in-phase and quadrature components of the 16 antenna elements sampled at a time. The binary data is converted from 0, 1 code to 1, \(-1\) code. That means the logic level is converted to a polarity non-return-to-zero level. The direct signal and the reflected signal sampled each time can be expressed as [24]:  
\[\begin{split} S_{\text{up}}^{nT_{c}}(k)=\sum\limits_{i=1}^{8}s_{i \text{,up\_I}}(k)+j\sum\limits_{i=1}^{8}s_{i\text{,up\_Q}}(k)\\ S_{\text{down}}^{nT_{c}}(k)=\sum\limits_{i=9}^{16}s_{i\text{, down\_I}}(k)+j\sum\limits_{i=9}^{16}s_{i\text{,down\_Q}}(k)\end{split} \tag{1}\]  
where \(S_{\text{up}}^{nT_{c}}(k)\) and \(S_{\text{down}}^{nT_{c}}(k)\) are the amplitudes of the kth sample of the direct signal and the reflected signal during the nth coherent integration, \(s\) is the signal component of each antenna element, \(i\) is the antenna element number, up_I and up_Q respectively represent the in-phase and quadrature components of the zenith pointing antenna, and down_I and down_Q denote the in-phase and quadrature components of the nadir pointing antenna respectively.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 2 Optimization of Trusses  
Trusses are probably the most frequent grid-like structures to be applied for testing different optimization techniques since the early 1960s [3]. Compared to other types of structures, the analysis and design processes of trusses are relatively simple and can be easily written into a mathematical model. On the other hand, trusses are usually composed from a large number of elements (bars) and are, therefore, unsuitable for optimization by repeating the calculation of all different structural alternatives. In the case of real engineering trusses, a lot of bars are oversized and thus not fully exploited. These structures consequently offer numerous possibilities for improvement and optimization.  
In general, truss optimization problems can be divided into two main spheres of activity. The first one belongs to the topology optimization where an optimal structure with an optional number and configuration of structural elements inside a defined superstructure has to be defined, while the second one presents a discrete sizing optimization problem, where the cross-sections of elements are forced to have discrete, in most cases standard, dimensions. Both mentioned optimization spheres represent the greatest challenge to the problem of truss optimization. In the last decades, some effective attempts of the topology and discrete dimension optimization of trusses have been made. A brief review and a history of different optimization techniques for solving these optimization problems are discussed in the following sub-sections.
## 3 Wind Turbine Generator Model
### Multi-WTG Aggregation Model
#### 3.3.2 Aggregation of Generators  
When the types of WTGs are different, or the operating condition differences between the same kinds of wind turbine is large, the generators of WTGs should be respectively aggregated. There are two ideas for the division of wind turbines [8].  
(1) Wind turbines with the same or similar operating states have the same or similar transient response characteristics. Therefore, selecting the physical quantities that reflect the operating states is a proper method for turbine division.  
(2) The division is carried out according to the types of WTGs, considering transient response characteristics such as the slip homology or the transient voltage characteristic curve [62].  
For a large-scale wind farm, the WTGs can be firstly divided into different classes based on their types, and then be divided into different groups based on the operation conditions. Each group of WTGs under similar operation states is aggregated to one WTG. Figure 9 shows the simplified process of the multi-machine aggregation.  
The multi-machine aggregated model mainly includes the wind turbine parameters and the generator parameters. To make the distinction, the parameters can be classified into the steady and the transient parameters according to the nature, and these parameters can be subsequently classified according to the sensitivity. The way of parameter aggregation greatly affects the accuracy and validity of the model. At present, there are two kinds of parameter aggregation methods, respectively, the frequency-domain aggregation and the time-domain aggregation. More specifically, the characteristics of these two aggregation methods are:  
(1) Frequency-domain aggregation: The frequency characteristics of wind turbines can be fitted effectively. First, the transfer functions of wind turbines are aggregated according to the features of the transfer function of each unit, and then fit to the frequency characteristics of the transfer function, finally obtaining the best fitting point corresponding to the actual response. The least square method can be used for this aggregation method [63; 64].  
(2) Time-domain aggregation: The aggregation parameters can be adjusted by means of intelligent algorithms such as the genetic algorithm [48] and the neural network algorithm [35], until the aggregation model satisfies the accuracy requirements. The literature [65] has compared several optimization methods, i.e., the basic genetic algorithm, Hopfield neural network, and basic ant colony algorithm, in time complexity, space complexity, and the difficulty of realization by using a paired  
Figure 9: Multi-machine aggregation method.  
comparison matrix evaluation method. In this evaluation, the ant colony algorithm has the best performance. However, no single optimization algorithm cannot be considered the best or worst based on one application, as for some applications, one may be better than the others [66].
## 3 Materials and Methods
### Seismological Data and Methods
Recordings of the temporary station CRE1 (Figure S1b) were also used to enhance the reference cross-correlation measurements, particularly those of smaller events, during the second period. The waveforms were band-pass filtered in the range 2-8 Hz, for KNSS, and 2-15 Hz, for CRE1, before cross-correlation was calculated for the full P and S waveform signals in all combinations of event pairs with available data. The cross-correlation maximum (XCmax) of every pair was registered in a matrix for every component. The matrices produced for different reference stations and components were then combined, retaining the RMS value of the XCmax determined for each pair. The nearest neighborhood linkage was applied and a Cth = 0.81 optimal threshold was selected, maximizing the difference between the size of the largest multiplet and the sum of clustered events [55]. This procedure created 234 multiplets containing a total of 1725 events. The P- and S-wave windows for each pair of events within the same multiplet were then cross-correlated for all stations with available picks, measuring the XCmax and its respective time lag to be used as input for HypoDD. The relocation procedure was applied separately for the two periods, acknowledging issues due to the different network geometry, so as not to degrade the data quality of the second period. On the other hand, we chose to relocate the mainshock and its early aftershocks (roughly a hundred), which occurred before the deployment of the temporary local stations, together with events that were located with data from the local network. This manages to improve the relative locations of the first aftershocks by exploiting their links with the rest of the events of the second period. Each relocation procedure was mainly divided into two types of sets, one with stronger a priori weights to the catalogue data and the other with stronger weights to the cross-correlation data.
## 4 Methods
### Anti-Entropy Weighting  
AEW technique is an important method used in weight calculation by applying management decisions. The concept of entropy was introduced by thermodynamics in physics, which represents an information theory of the degree of disorder of a system. The entropy weighting method can use an accurate calculation to determine the uncertainty probability of random events. The weight calculation of the corresponding entropy not only has the advantages of high accuracy, adaptability, and objectivity, but can also reduce the influence of human subjective factors in the evaluation process [16].  
The weight of criteria are calculated according to the decision matrix. In particular, the method is responsible for producing detailed data. The following steps describe how criteria weights are determined via the AEW method.  
**Step 1:** The decision matrix is normalized as given in Equation (1).  
\[\mathbf{P}_{ij}=\frac{\mathbf{X}_{ij}}{\sum_{i=1}^{m}\mathbf{X}_{ij}} \tag{1}\]  
\(i\)**:** alternatives;  
\(j\)**:** criteria;  
\(P_{ij}\)**:** normalized value;  
\(\mathbf{X}_{ij}\)**:** the value of the benefit given.  
**Step 2:** Anti-entropy values are calculated for each criterion. The anti-entropy can be obtained via Equation (2). Entropy value:  
\[\mathbf{e}_{ij}\ =-\frac{1}{\ln m}\sum_{i=1}^{m}P_{ij}\ln P_{ij} \tag{2}\]  
\(\frac{1}{\ln m}\): entropy coefficient;  
\(\mathbf{e}_{ij}\)**:** entropy value.  
**Step 3:** The weight of each criterion is calculated. The determination of the criteria's weights is important in MCDM problems in terms of aiming accurate results.  
The goal weights of criteria are derived considering the information provided by the decision matrix. The weights of criteria can be determined objectively by calculating entropy measures of criteria.  
Low values of entropy reveal that the evaluation scores of the alternatives, according to a criterion, are less similar and more information is transmitted by the criterion, and thus, the weight of the criterion is higher [17].  
In this step, the weights of criteria are obtained with the help of the IF-entropy measure, which can be obtained via Equation (3).  
\[w_{i}\ =\frac{\mathbf{1}-\mathbf{e}_{ij}}{\sum_{j=1}^{n}\left(\mathbf{1}-\mathbf{e}_{ij}\ \right)} \tag{3}\]\(w_{i}:\) importance level.
## 5 Performance Analysis
Therefore, to estimate the communication costs, the sizes of the various credentials we take into account, such as timestamps, identities, random numbers, the digest of a hash function and elliptic curve point are 32 bits, 160 bits, 160 bits, 160 bits and 160 bits, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{3}{c}{**Node Overhead Calculation**} \\ \cline{2-4}  & **UAV** & **GCS** & **Total** \\ \hline Ref. [31] & \(2T_{E}+5T_{SM}\) & \(2T_{E}+7T_{SM}\) & \(4T_{E}+9T_{SM}\) \\ Ref. [32] & \(4T_{BP}+8T_{H}\) & \(9T_{H}+2T_{BP}+4T_{SM}\) & \(17T_{H}+6T_{BP}+4T_{SM}\) \\ Ref. [33] & \(6T_{SM}+10T_{A}+6T_{H}\) & \(4T_{K}^{*}\) & \(6T_{SM}+10T_{A}+6T_{H}+4T_{K}\) \\ Our scheme & \(2T_{X}+2T_{SM}+T_{N}+3T_{H}+5T_{C}\) & \(2T_{X}+T_{SM}+T_{N}+2T_{H}+3T_{C}\) & \(4T_{X}+3T_{SM}+2T_{N}+5T_{H}+8T_{C}\) \\  & \(+2T_{S}+T_{K}\) & \(+T_{S}+T_{K}\) & \(+3T_{S}+2T_{K}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of node computation overheads. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Security Features**} & \multicolumn{4}{c}{**Schemes**} \\ \cline{2-5}  & **Ref. [31]** & **Ref. [32]** & **Ref. [33]** & **Our Scheme** \\ \hline Perfect Backward Secrecy & NA & NA & NA & \(\surd\) \\ Mutual Authentication & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Session Key Agreement & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Resisting Privileged Internal Attacks & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ Resisting Replay Attacks & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Resisting Man-in-the-middle Attacks & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Resisting Denial of Service Attacks & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Resisting Drone Capture Attacks & \(\times\) & NA & \(\surd\) & \(\surd\) \\ Desynchronization Attack: & NA & NA & NA & \(\surd\) \\ Formal Verification & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._respectively. Moreover, the outputs of AES symmetric encryption/decryption and signature operation are assumed 256 bits and 1024 bits respectively. In the proposed protocol, the participating entities exchange three messages during the authentication and key negotiation phases:{\(M1,HMAC\)},{\(M2,HMAC,E(SK_{GUI,},MSG)\)}, {\(M3,S3,HMAC\)}, whose costs are 576 bits, 832 bits, 1536 bits. Therefore, the total communication cost is 576 + 832 + 1536 = 2944 bits. On the other hand, the existing schemes of Ref. [31], Ref. [32] and Ref. [33] demand the communication costs of 3254 bits, 3312 bits, and 2496 bits, respectively. It is evident from Table 11 that the proposed protocol needs slightly lower communication cost than other existing schemes.