esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G Network Architecture Using FSO-PDM/PV-OCDMA System with Weather Performance Analysis
## 5 Simulation Results
### Effect of Fog Weather Conditions on FSO-PDM/PV-OCDMA System  
The effects of different fog conditions (LF, MF, and HF) are discussed in this part.  
Figure 5 shows the BER versus the number of channels that the proposed system can support at different transmission powers under foggy weather conditions and for different transmitted power at 20 Gbps. As the level of fog rises, the attenuation increases, and the allowable number of channels decreases. The LF has an attenuation of 9 dB/km, which is increased to 16 dB/km under MF and further increased to 22 dB/km under HF [15]. Furthermore, the number of channels at \(t_{p}\) = 15 dBm is higher than that at \(t_{p}\) = 10 dBm. At BER \(10^{-9}\) and \(t_{p}\) = 10 dBm, the maximum number of channels is 25, 19, and 10, respectively, under LF, MF, and HF. These channels increased when \(t_{p}\) = 15 dBm is used to 83 under LF, 58 under MF, and 36 under HF at the same value of BER.  
Figures 6 and 7 depict the log (BER) versus the propagation range for the eight channels and Q-factor versus the propagation range for the eight channels, respectively. As the level of fog rises, the attenuation increases, the propagation range decreases, and the Q-factor decreases. Additionally, as the LF has less attenuation, our proposed system achieved the longest FSO range for the eight channels under its effect, which is 1.525 km. This range is slightly decreased to 1.05 km when the level of fog becomes medium, while at the heaviest level of fog, the shortest FSO link is achieved at 0.85 km and that is cleared as the largest attenuation is caused under HF. All these ranges are considered at acceptable BER values (less than \(3\times 10^{-3}\)).  
Additionally, as the propagation range increases, the Q-factor decreases. Table 4 shows the computed log (BER) and the Q-factor for the eight channels at a propagation range of 1.525 km under LF, 1.05 km under MF, and 0.85 km under HF.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Channels**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**3**} & \multicolumn{1}{c}{**4**} & \multicolumn{1}{c}{**5**} & \multicolumn{1}{c}{**6**} & \multicolumn{1}{c}{**7**} & \multicolumn{1}{c}{**8**} \\ \hline \multicolumn{2}{c}{Polarization signal} & \multicolumn{4}{c}{x-polarization} & \multicolumn{4}{c}{y-polarization} \\ Log (BER) & under LF & \(-6.7\) & \(-6.23\) & \(-7\) & \(-6.7\) & \(-6.39\) & \(-6.41\) & \(-6.66\) & \(-6.91\) \\ Q-factor & (1.525 km) & 5.07 & 4.86 & 5.21 & 5.07 & 4.93 & 4.9  
Figure 6: Log (BER) of FSO-PDM/PV-OCDMA system versus FSO link under **(a)** LF, **(b)** MF, and **(c)** HF.  
Figure 7: Q-factor of FSO-PDM/PV-OCDMA system versus FSO link under **(a)** LF, **(b)** MF, and **(c)** HF.
# Current Trends in Fluid Viscous Dampers with Semi-Active and Adaptive Behavior
## 2 Passive Fluid Viscous Damper
### Design Methods for FVDs in Buildings  
FVDs can be adopted in both new and existing structures to provide supplementary dissipation of seismic energy. In framed structures, these devices are usually incorporated within steel braces, where the most popular configurations of damped braces are the diagonal, chevron, scissor jack, and toggle ones [58].  
Several procedures are reported in the literature for the design of FVDs. Normally, the design is performed in order to guarantee the protection of buildings from earthquakes of high intensity corresponding to the ultimate limit state [59, 60, 61]. These methods aim at determining the FVDs' fundamental properties, namely the damping coefficient \(C_{D}\) and the damping exponent \(\alpha\). It is possible to categorize current design methods into three families determined by the input parameters. The first group, named Damping-Based Design method, consists of evaluating the damping coefficients of the added viscous dampers by selecting, as a first step, a damping ratio \(\xi_{v}\) of the structure based on a chosen target level of structural performances. The properties of the single FVD are then distributed at each floor, depending upon the specific structural characteristics of the case-study structure, such as the floor masses and the inter-story lateral stiffnesses [1, 62, 63]. In the second group, called Displacement-Based Design method, a target displacement demand (e.g., inter-story drift, roof displacement, etc.) is defined, and the FVDs are designed in order to guarantee the selected structural performance under the design earthquake. Based on that target-displaced configuration, a \"substitute\" single degree of freedom (SDOF) model is evaluated and used to replace the multi-degree of freedom (MDOF) structure. The substitute SDOF model consists of an equivalent linear system characterized by an effective (secant) stiffness and an effective energy dissipated, represented through an equivalent damping ratio. Once the properties of the SDOF FVD are determined, the distribution of the properties of the dampers at each story of the structure is performed depending on the displacement or the story shear [64, 65, 66]. The Energy-Based Design methods belong to the third group, where the properties of the FVDs to be inserted at each floor of the analyzed structure are selected based on the amount of seismic energy to be dissipated [67, 68, 69].
###### Abstract  
A compact circular structured monopole antenna for ultrawideband (UWB) and UWB dual-band notch applications is designed and fabricated on an FR4 substrate. The UWB antenna has a hybrid configuration of the circle and three ellipses as the radiating plane and less than a quarter-lowered ground plane. The overall dimensions of the projected antennas are \(16\times 11\times 1.6\) mm\({}^{3}\), having a \(-10\) dB impedance bandwidth of 113% (3.7-13.3 GHz). Further, two frequency band notches were created using two inverted U-shaped slots on the radiator. These slots notch the frequency band from 5-5.6 GHz and 7.3-8.3 GHz, covering IEEE 802.11, Wi-Fi, WLAN, and the entire X-band satellite communication. A comprehensive frequency and time domain analysis is performed to validate the projected antenna design's effectiveness. In addition, a circuit model of the projected antenna design is built, and its performance is evaluated. Furthermore, unlike the traditional technique, which uses the simulated surface current distribution to verify functioning, characteristic mode analysis (CMA) is used to provide deeper insight into distinct modes on the antenna.  
UWB; antenna; dual-band notch; group delay; fidelity factor Article
# Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System +
###### Abstract
Several members of the International Council for the Exploration of the Sea (ICES) have recently started to discuss appropriate vessel-tracking systems for the whole insbore fleet as well a common framework for identifying trips/hauls using SSF geo-spatial data [33]. Additionally, at the EU level, current negotiations between the EU Commission, Parliament, and Council are underway for the tracking of small-scale fishing vessels by all Member States [34]. Therefore, it is necessary to produce standardized protocols to securely gather and share data across the insbore fleet, classify events during fishing trips (fishing _vs._ steaming), and infer measures of effort (e.g., number of pots/traps, length of the net, and gear soaking time). If the use of high-resolution geo-spatial data becomes more wide-spread and standardized, it could fill the major data gaps that exist for the SSF segment. In this context, an architecture was drafted in [PERSON] et al. [35] to collect real-time positional data sent over LoRaWAN or 2G/3G/4G connections by small-scale vessels. Here, it is enriched and tested, and R scripts are released to resolve their individual fishing trips. The architecture relies on Traccar [36], while the high-tech and cost-efficient Teltonika FMM640 [37] worldwide tracker is proposed as Fleet Management System (FMS) hardware. Instead of just enforcing small vessels to use other cheaper (e.g., raspberry-based) AIS systems, the choice of this FMS was due to the need to easily share positions and enable monitoring, purchasing inexpensive but robust, well-documented, accessible, and license-free transmitters. This is in line with other ongoing projects [29], which chose similar FMSs. Moreover, it is worth noting that the use of HTTPS and LoRaWAN technology allows to implement an encrypted communication channel thanks to the TLS/SSL and LoRaWAN protocols, respectively. Both protocols, unlike AIS, enable authentication and encryption of sent data. A first compact prototype was installed on a small fishing vessel. It records its accurate location, speed, and other features; and sends this data to a dedicated back-end. A sensor attached to the hauler is used in tandem to record when and where it is activated, indicating that the retrieving of the gear is occurring. This information can be assessed in near real time using a secure-access web platform and is recorded allowing future machine-learning analysis and algorithm development in case of vessels that could not be equipped with the hauler sensor. The coupled use of such low-cost technologies and machine-learning automated analyses opens up the potential for more integrated platforms to inform coastal resource and fisheries management, to support the design and development of new polices, and to understand impacts on the marine ecosystem [3; 38].
## 3 Study Methodologies
### Numerical Simulation Study  
Due to the obscurity of theoretical study, it is necessary to combine the theoretical conclusions with numerical simulation. Compared with the experimental measurement, the numerical simulation method has the advantages of a low cost and short cycle and so forth. With the development of the computer performance, both the calculation accuracy and speed have improved significantly. Computational Fluid Dynamics (CFD) is currently the most efficient method for analyzing the flow and flow-induced noise [49; 50; 51; 52]. It is worth noting that the empirical model and assumption are used in the simulation process, so the experimental validation is essential.  
The numerical simulation of flow-induced noise is mainly guided by Lighthill acoustic analogy theory and is combined with CFD software. The simulation process could be divided into two steps [53]. The first step is to obtain the unsteady flow characteristics [54; 55; 56; 57; 58] by adopting a suitable turbulence model [57; 58; 59; 60; 61; 62]. The second step is to extract the unsteady flow fluctuations in walls which are used as a dipole source, and then to use the finite element method, infinite element method or boundary element method to solve the sound wave equation [63; 64; 65].  
The generation of noise is accompanied by the vibration of pump structures, so the noise simulation method that only considers the dipole source has some limitations. With the deepening of study, scholars have adopted the acoustic-vibration-coupling method, which is realized by taking the modal response results of structures into consideration, to analyze the flow-induced noise under the joint action of structural vibration and dipole source [66; 67; 68; 69; 70; 71; 72].
## 5 Conclusions and Future Directions  
### Conclusions  
This paper introduced the guided-attention hybrid deep learning architecture (called GAHD-VAE) and showed its capacity for pedestrian and bicycle traffic flow modeling and forecasting. Notably, the proposed approach improves the VAE capacity to learn temporal dependencies in time-series data by adding the self-attention mechanism at different levels in the VAE structure and including a recurrent neural network (LSTM) in the VAE encoder side. The role of the self-attention mechanism in the GAHD-VAE is to uncover the most relevant part of features. LSTM is embedded in the VAE encoder to enable modeling the time-dependence in time series data. Results based on six traffic flow datasets demonstrated that the GAHD-VAE could generate high accurate traffic flow forecasting (one-step and multi-step) and outperform the traditional VAE model and the state-of-the-art models, namely LSTM, GRU, BiGRU, and BiLSTM, as well as four shallow models (i.e., LR, SVR, RR, Lasso regression). Furthermore, we investigated the impact of using the attention module's multi-configuration on the forecasting quality of the GAHD-VAE. It has been shown that results could be improved by changing the attention mechanism's internal data processing (activation function) and the attention mode.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Dataset** & **MODEL** & **RMSE** & **MAE** & \(R^{2}\) & **EV** \\ \hline \multirow{8}{*}{5} & BiGRU & 20.068 & 16.32 & 0.825 & 0.826 \\  & BiLSTM & 19.115 & 15.441 & 0.842 & 0.843 \\  & GAHD-VAE & 16.98 & 13.613 & 0.875 & 0.878 \\ \hline \multirow{8}{*}{5} & ConvLSTM & 23.738 & 24.604 & 0.779 & 0.78 \\  & CNN & 15.112 & 12.716 & 0.856 & 0.902 \\  & LSTM & 11.265 & 7.663 & 0.936 & 0.936 \\  & GRU & 15.07 & 12.106 & 0.886 & 0.901 \\  & BiGRU & 18.482 & 12.609 & 0.828 & 0.833 \\  & BiLSTM & 10.577 & 7.097 & 0.944 & 0.944 \\  & GAHD-VAE & 10.202 & 7.015 & 0.948 & 0.951 \\ \hline \multirow{8}{*}{6} & ConvLSTM & 89.583 & 72.832 & 0.733 & 0.755 \\  & CNN & 68.138 & 56.918 & 0.87 & 0.901 \\ \cline{1-1}  & LSTM & 82.286 & 64.834 & 0.792 & 0.807 \\ \cline{1-1}  & GRU & 84.914 & 67.21 & 0.779 & 0.787 \\ \cline{1-1}  & BiGRU & 75.26 & 64.157 & 0.826 & 0.854 \\ \cline{1-1}  & BiLSTM & 76.796 & 65.442 & 0.819 & 0.844 \\ \cline{1-1}  & GAHD-VAE & 63.531 & 52.38 & 0.876 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**MODEL** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **EV** \\ \hline ConvLSTM & 68.48 & 53.35 & 0.80 & 0.81 \\ CNN & 71.52 & 52.77 & 0.81 & 0.84 \\ BiGRU & 48.24 & 38.29 & 0.84 & 0.87 \\ BiLSTM & 45.23 & 35.49 & 0.87 & 0.89 \\ LSTM & 52.55 & 39.17 & 0.82 & 0.84 \\ GRU & 49.92 & 39.26 & 0.84 & 0.87 \\ GAHD-VAE & 37.34 & 28.30 & 0.91 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Averaged measurements of effectiveness per model for daily forecasting of pedestrian and bicycle traffic flows.
# Enhancing Efficiency and Cost-Effectiveness: A Groundbreaking Bi-Algorithm MCDM Approach +
## 2 Methods and Experiment
### Stage II: Distance from Average Solution Assessment  
The evaluation based on the distance from average solution (EDAS) technique is a modern MCDM approach that is both effective and time-saving. In this approach, the attractiveness of various options is determined by calculating their distances from a specific solution [5; 7; 9; 11; 22; 25; 39; 44; 48; 51; 64].  
Step 1: Choose the essential criteria for the alternatives.  
Step 2: Create the decision matrix (\(B\)), shown in Equation (1) below:  
\[B=[B_{ij}]=\begin{bmatrix}b_{11}&b_{12}&\ldots&b_{1n}\\ b_{21}&b_{22}&\ldots&b_{2n}\\ \ldots&\ldots&\ldots&\ldots\\ b_{n1}&b_{n2}&\ldots&b_{nn}\end{bmatrix} \tag{1}\]  
where \(B_{ij}\) is determined as the performance values of \(i\)th alternative concerning \(j\)th criteria.  
Step 3: The average solution to the criteria is determined by the following equation (Equation (2)):  
\[Mean=\left[Mean_{j}\right]_{1\times k} \tag{2}\]  
Step 4: Calculate the matrix of \(PDA\) and \(NDA\) according to the benefit and expense criteria presented in Equations (3)-(6) as follows:  
\[PDA=\left[PDA_{ij}\right]_{r\times k} \tag{3}\]  
\[NDA=\left[NDA_{ij}\right]_{r\times k} \tag{4}\]  
\[PDA_{ij}=\frac{\max(0,(b_{ij}-Mean_{j}))}{Mean_{j}} \tag{5}\]\[NDA_{ij}=\frac{\max(0,(Mean_{j}-b_{ij}))}{Mean_{j}} \tag{6}\]  
where \(PDA_{ij}\) and \(NDA_{ij}\) indicate the positive and negative distances of the \(i\)th alternative from the average solution in terms of the \(j\)th standard, respectively, if the \(j\)th criterion does not produce a favorable result.  
Step 5: Define the weighted sum of \(PDA\) and \(NDA\) shown in Equations (7) and (8):  
\[SP_{i}=\sum_{j=1}^{k}w_{j}PDA_{ij} \tag{7}\]  
\[SN_{i}=\sum_{j=1}^{k}w_{j}NDA_{ij} \tag{8}\]  
where \(w_{j}\) shows the criterion weight of the \(j\)th.  
Step 6: Calculate \(SP\) and \(SN\) that are normalizing, shown in Equations (9) and (10) below:  
\[NSP_{i}=\frac{SP_{i}}{\max_{i}(SP_{i})} \tag{9}\]  
\[NSN_{i}=\frac{SN_{i}}{\max_{i}(SN_{i})} \tag{10}\]  
Step 7: Calculate the appraisal score for the options using Equation (11):  
\[AS_{i}=0.5(NSN_{i}+NSP_{i}) \tag{11}\]
## 2 Materials and Methods
### Relationship Analysis  
The street vending locations identified from interviews with urban managers were mapped in the QGIS platform. Each location was given a coding name with a combination of numbers and capital letters (Figures A1 and A2). The capital letters refer to the particular land uses close to the location: H-hospital, M-market, P-park (or square), R-residential area and S-school. The street vending data was then automatically linked to their nearest street segment on the syntactical map using the'spatial joint' function.  
Space syntax research has repeatedly used the term'spatial core', particularly in indicating that pedestrian movement distribution and land-uses pattern were associated with the spatial core of 'Integration' [15; 39]. The spatial core consists of a given percentage of spaces that take the highest values from syntactic properties [40]. For example, [PERSON] (2009) claimed the '80/20 principle' meaning 20% of the most accessible streets accommodate 80% of the city's traffic flow [41]. We hypothesised that the popular locations of street vendors reported by the urban managers are more likely to be the most accessible streets with the greatest potential for movement in the city at key times. To test this hypothesis, we therefore adopted the'spatial core' principle to filter out the streets with the greatest potential for movement in the research area [17; 42]. We then investigated the distribution of street vendors from the perspective of the'spatial core' by calculating the number/percentage of streets that were favoured by vendors and which fell into the 10% and 20% most accessible spatial core categories of 'Integration' and 'Choice' at different scales respectively.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results
### SAR Image Comparison
#### 3.3.2 Multistatic and Multibistatic Simulations  
Since the goal is to detect non-metallic targets, the multistatic and multibistatic simulations comparison was performed when the buried target is dielectric. SAR images are shown in Figures 10 and 11 for the multistatic and multibistatic scenarios, respectively. In RT simulations, the specular reflections from the soil surface do not reach the receiver. Therefore, in FDFD simulations, the known flat ground background is removed, thus showing only the target-scattered response. The results are almost the same for both configurations, where the dielectric object is clearly distinguishable. There is also a good agreement between the RT and FDFD simulations, thus it can be concluded that RT is a useful tool for designing new GPR configurations.  
Figure 10: SAR image from RT (**a**) and FDFD (**b**) simulations: multistatic scenario with a dielectric target.  
Figure 9: SAR image from RT (**a**) and FDFD (**b**) simulations: multimonostatic scenario with a dielectric target.
## 2 Results
### Index of Refraction in Moving Frames and Fresnel-Fizeau Drag
Nevertheless, the dot product \((\mathbf{\beta}\cdot\mathbf{n})\) is different for waves with different propagation directions; therefore, they transform into waves with different \(\mathbf{n}_{\mathbf{\beta}}\), such that even isotropic material appears bianisotropic to a moving observer [22, 57]. Our results for Fresnel wave surfaces, topological classes, and wave indices of refraction in moving frames are related to the Fresnel-Fizeau drag for the phase velocity of light. We considered a frame \(S_{\beta\mathbf{z}}\) moving with respect to the material rest frame \(S_{mat}\) with speed \(V=\beta c\) in the direction of the wave propagation \(\hat{\mathbf{k}}=\hat{\mathbf{z}}\). In this case, Equation (15) turns into  
\[\mathbf{n}_{\hat{\mathbf{z}}\mathbf{z}}=\frac{(n-\beta)}{1-\beta n}\hat{\mathbf{z}}\]Correspondingly, the difference between the phase velocity \(v_{ph,\beta}\) in the moving frame \(S_{\beta\hat{z}}\) and \(v_{ph}\) in the material rest frame \(S_{mat}\) is  
\[\frac{c}{n_{\beta z}}=\frac{c}{n}\frac{(1-\beta n)}{\left(1-\frac{\beta}{n} \right)}\underset{\beta\ll 1/n}{\approx}\frac{c}{n}-c\beta\left(1-\frac{1}{n^{2}} \right)=\frac{c}{n}-Vf \tag{16}\]  
\[\Delta v_{ph}=\frac{c}{n_{\beta z}}-\frac{c}{n}=-c\beta\frac{1-\frac{1}{n^{2}} }{1-\frac{\beta}{n}}\underset{\beta\ll 1/n}{\approx}-Vf\]  
This change in the phase speed of light under frame transformation is called Fresnel-Fizeau drag with drag coefficient \(f\).
## 5 Conclusions  
Energy-efficient reliable transmission is a fundamental routing issue in lossy wireless sensor networks. We have presented detail study about existing routing schemes to guarantee reliability in an efficient way. However, these reliable routing schemes only work well for specific applications which means that they cannot be used as a general solution to provide reliability. To solve the problem, we have proposed a DRDT scheme. DRDT enhances reliability in transmission by cooperative retransmission task of helper nodes. Furthermore, DRDT is extendable, as well as adaptable to existing routing schemes since DRDT operates as an extension layer under the network layer. Therefore, we believe that DRDT  
Figure 8: End-to-end transmission delay.  
Figure 9: End-to-end transmission rate.  
based on a pluggable modular approach can be a general solution in providing reliability to the existing routing schemes. Simulation results, in which GF-HOP is implanted as a basic routing scheme, indicate that poor performance of GH-HOP can be improved by applying DRDT. DRDT enhances end-to-end transmission cost and reduces its delay as a result of high transmission rate. In case of GF-ETX after applying DRDT, slight improvement is observed over these performance metrics. This means that DRDT performs retransmission task with very low overhead. Furthermore, DRDT demonstrates superiority in data retransmission compared to CBF.  
In our upcoming research, we plan to study the scheduling of sensor modes to extend DRDT as a more practical scheme. We expect that consideration of low duty cycle networks helps to prolong operational time. However, end-to-end transmission cannot afford to maintain an always-awake transmission which leads a decrease the number of overhearing nodes. The performance metrics such as end-to-end transmission cost and delay can be reduced since improvements of DRDT depend on the number of overhearing nodes. Therefore, we also plan to analyze the interaction between low duty cycle networks and the number of overhearing nodes. The interesting overhearing phenomenon is expected according to duty-cycle networks.