esa-sceva/satcom-chunk-collection huggingface dataset:
# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 2 Related Work
Initially inspired by human cognitive attention, this mechanism has been applied to a broad array of applications, from natural language processing to computer vision [41]. In the realm of image analysis, attention mechanisms are typically explored to enhance expressive power by amplifying the importance of different spatial regions or feature channels [42]. The evolution of attention mechanisms in the field of deep learning aims to improve model interpretability and performance. Universal early forms of attention mechanisms are designed to enhance the performance of neural networks for tasks such as machine translation by focusing on relevant segments of the input sequence [43]. This concept expanded into the spatial domain of computer vision, evolving into sophisticated forms like the squeeze-and-excitation network (SE) and the convolutional block attention module (CBAM), which combine spatial and channel-specific attention [44; 45]. Additionally, the advent of vision transformers (ViT) marked a significant shift in attention-based models. ViT applies the self-attention mechanism, originally successful in natural language processing, to computer vision. It segments images into patches, processes them as sequences, and learns by focusing on interactions between arbitrary image parts. Separated from traditional attention mechanisms, this approach replaces the CNN structure as a whole with transformers to handle the embedding sequence [46]. Models like CBAM and ViT have shown high performance in various fields through extensive experimentation and validation [47]. However, conventional models have not been studied for data representing UAV trajectory and movements and are not ideal for capturing subtle differences between static and dynamic elements within such images. We therefore emphasize the need for specialized attention mechanisms for UAV trajectories. To bridge this gap, our study introduces the Spatial Trajectory Enhanced Attention Mechanism (STEAM), specifically designed for UAV trajectory analysis. This approach addresses the shortcomings of existing models in handling the unique challenges of UAV flight pattern data. The section that follows presents new solutions that provide an efficient and highly analytical UAV trajectory analysis model and compares performance with existing methods through experiments.
## 3 Network Framework Design to Communicate Interaction in Virtual Concerts
If the performer sends an interaction to the audiences, they bypass the separate MIF or Intelligence Controller and directly transmit interaction data to all nodes. As shown in Figure 8, not only does the performer provide performances through the streaming converter as a central server in the form of a CDN, but it also sends interaction data to each Edge Farm, supporting low-latency communication. Performers can configure the following CDN-based edge servers to transmit interaction data to large audiences. Edge servers are critical components in the proposed network structure to reduce latency and to manage traffic efficiently. They are strategically placed closer to the nodes (audience) and the performer, which allows data to travel shorter distances, resulting in faster data delivery. Here is a brief conceptual overview:  
* **Central Server (Performer)**: This is where the live performance happens. The performance data are streamed in real time from this location. Interaction data from the performer, if any, are also sent from here. * **Streaming Converter**: The streaming converter takes the raw performance data (motion, audio, and voice data) from the central server (performer) and converts them into a format suitable for transmission over the network. It might involve compression or other methods to ensure efficient data transmission. * **Edge Servers (Edge Farm)**: These are servers placed closer to the end users (nodes). They receive the performance stream from the central server via the streaming converter and then relay these data to the end users. Similarly, interaction data from the users are collected and sent back to the central server through these edge servers. * **Nodes (Audience)**: These are the end users or audience members in the virtual concert. They send interaction data and receive performance data through the edge servers. This setup ensures that data does not have to travel long distances all the way from the performer to each individual user, and vice versa. Instead, the data travel to nearby edge servers, which can relay them to the users or back to the performer as required, significantly reducing latency and enabling real-time interactions. However, because the above method needs to consider scalability, it is an option worth designing if the virtual performance platform is configured based on the cloud as shown in Figure 6. \begin{table}
\begin{tabular}{c c} \hline \hline
**Kind of Interaction** & **Details (0... N)** \\ \hline Gesture & Jump, Hurray \\  & Waving arms \\ \hline Emoticon & Smile, Nyah, Weeping \\  & Shoot Heart Emoticon \\ \hline  & “Hello!” \\  & “Nice to meet you.” \\ Text (Default Message) & “I love you.” \\  & “Very good.” \\  & “Wonderfull” \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Type of interaction data when used in virtual concerts.
## 2 Connection Options and Topology Optimization after Site Densification
The possible new mmW frequency bands, the radio link availability targets, and the outage caused by rain are discussed in later sections of the paper. Figure 4: Evolving core and radio access network (RAN) toward cloudification, 5G slicing, and edge sites. Figure 3: Example of topology re-planning for new and co-located sites. corresponds to an annual outage of approximately 52 min. Outages of the individual links are caused by the following:  
* propagation (intensive rain and other atmospheric conditions, such as snow, fog, oxygen, or water vapor absorption at very high frequencies);
* link unavailability due to radio equipment failure. With reasonable dish sizes, the link unavailability (\(U\)) should be \(U=0.01\%\) of the time or less. The link planning guidelines target individual link availability for an annual timeframe that includes the seasonal fluctuations of weather conditions. The goal is to ensure reliable links, regardless of the undesired effects of propagation phenomena and rain. Exceptions may occur, when antenna size is limited due to civil work constraints, e.g., wind load or authorities not permitting the installation of bigger diameter dishes. In the calculations, the frequently used frequency bands are investigated [24]. This paper focuses on rain intensities \(R<100\) mm/h that are typical in Central and Eastern Europe. For completeness, higher rain rates are also calculated, following the rain and propagation model of the International Telecommunication Union, ITU-R P recommendations [36; 37; 38; 39].
## 2 Navigation and Localization
### Geophysical Navigation
#### 2.3.2 Geomagnetic Navigation  
Geomagnetic Navigation relies on magnetic sensors and its essence is the Fitting of Two Point Sets (FTPS) process, where a marine geomagnetic map is used for matching [26]. Geomagnetic filed has many features which can be applied for matching [59], such as the intensity of the total field \(F\), the horizontal component \(H\), the north component \(X\), the east component \(Y\), the vertical component \(Z\), the declination \(D\), the inclination \(I\), the geomagnetic gradient, and so on. These features are shown in Figure 9.  
[PERSON] et al. [60] studied two matching algorithms, TERCOM and ICCP, used in the geomagnetic matching navigation. An experiment was designed to test the accuracy of the underwater navigation system, using a Differential GPS (DGPS) receiver for providing the exact position of the vehicle. In the results, matching positioning errors in the \(x\) direction or in the \(y\) direction were less than 100 m. Authors conclude that both TERCOM and ICCP can achieve credible geomagnetic navigation, with the difference that ICCP can provide a real-time positioning solution and TERCOM cannot. [PERSON] et al. [7] presented a new algorithm to solve FTPS in geomagnetic localization. The algorithm was an improved version of the ICCP algorithm, based on the algorithm proposed by [PERSON] et al. [61]. Simulation results showed that the ICCP-Menq-algorithm had a better performance than original ICCP algorithm in terms of dealing with geomagnetic-matching localization. [PERSON] et al. [62] presented a new method which was based on the integration of TERCOM, K-means clustering algorithm and an INS. An experiment was implemented for evaluating the accuracy and the stability of the method proposed. INS and DGPS were set on the surveying vessel. In order to verify the accuracy of this new method, the positioning result from D-GPS is used for comparing with the result of the matching navigation. After completing the experiment, the error of the new method was under 50 m, meanwhile the traditional method showed an error up to 7 times higher.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 5 SR in Wi-Fi 6
### Concept  
A BSS regroups stations that use the same parameters (e.g., security, radio channel) to access the medium. Two BSSs are overlapping if their signals can be mutually detected. In Figure 12, a laptop is in the intra-BSS area of BSS 1 and it is an inter-BSS node for BSS 2. An access point defines the parameters for its BSS and orchestrates the transmissions. It regularly broadcasts beacon frames that contain information about the channel used by the access point, encryption information, or power saving (see Section 2.2.3).  
Legacy Wi-Fi has only limited capabilities for SR. Access points that are substantially apart from each other may use the same channel to communicate with the stations within their BSSs. When the BSSs are within each other's transmission ranges and operate on the same channel, this causes huge interference [110], increases the congestion, and leads to poor data rates. Interference may become a problem in dense deployments with many access points within a small area as the number of different channels is insufficient to avoid overlapping BSSs.  
Applying SR among overlapping BSSs increases spectrum reuse that results in a higher throughput and network capacity. Each station identifies the originating BSS of the ongoing data packets: if the packets are from different BSSs and the signal strengths are lower than a predefined threshold, the station can transmit its data concurrently on the same channel.
# Real-Time Tracking and Position Control of an Elastic Cable-Driven Winch System
## 5 Experiments
### Experimental Data Acquisition  
Real-time parameter adjustments were made using the dSPACE ControlDesk user interface, and the process was iteratively continued until optimal results were achieved. The control method coefficients were systematically modified, and the experiments were repeated to assess the impact of these adjustments on system performance. This iterative approach identified and implemented the most effective control parameters.  
The experimental results for determining the effective coefficients of the PD controller and ESMC at minimum payload condition are shown in Figure 6.  
Figure 4: Comparison of ESMC and CSMC\(-\)tracking response (**a**); error signal (**b**).  
The experimental results identified the control coefficients as optimal for the system. In the PD control structure, the proportional gain (P) was set to 900 and the derivate gain (D) to 600. In the ESMC algorithm, Equation (30) was derived using a sampling time of T = 0.001 s, consistent with the DS1104 control card's sampling period. The coefficient D was selected as 0.001, leading to the determination of the control gain b as 4000.  
Figure 5: Experimental setup of a real-time controlled winch system with an elastic cable.  
Figure 6: Determination of (**a**) the PD coefficients and (**b**) the ESMC coefficients.
## 3 Wind Load Calculation  
### Principle of Wind Load Calculation  
Using FLUENT 2022 R1 software, the effect of the wind angle of attack on the wind load of the bridge was considered, and the change of the wind load of the bridge was analyzed under different wind angles of attack, so as to obtain the most unfavorable wind load value.  
Because when the bridge section is considered as a rigid body in the wind field, the wind field will be disturbed, so that the flow point on the beam surface will change, and the faster the flow, the smaller the pressure. Therefore, the lift load is the difference in pressure between the upper and lower surfaces of the bridge section, and similarly, the drag load is the difference in pressure between the windward and leeward surfaces of the bridge section, and the pitching moment is generated by the inconsistency between the resultant force of lift and drag on the bridge section and its centroid. The bridge section in a fluid with velocity \(V\) will be subjected to forces \(F_{V}\) along the bridge and \(F_{H}\) in the trans-bridge direction, as well as the flow-induced static moment \(M_{T}\), as shown in Figure 8.  
Figure 5: Scale Parameter.  
Figure 6: Position Parameter.  
Figure 7: Wind Frequency.  
The static three-component coefficient on the bridge section is defined as [25]:  
\[\begin{array}{l}C_{H}(\alpha)=\frac{2F_{H}}{\rho t2H}\\ C_{V}(\alpha)=\frac{2F_{V}}{\rho t2B}\\ C_{M}(\alpha)=\frac{2M_{T}}{\rho t2B^{2}}\end{array} \tag{9}\]  
In the formula, \(C_{H}(\alpha)\), \(C_{V}(\alpha)\) and \(C_{M}(\alpha)\) are the drag coefficient, lift coefficient and lift moment coefficient, respectively; \(F_{H}\), \(F_{V}\) and \(F_{T}\) are lift, drag, and lift moment, respectively; \(\rho\) is the density of air, taken as 1.225 kg/m\({}^{3}\); \(U\) is the average wind speed; \(H\) and \(B\) denote the height and width of the segmental model; and \(\alpha\) is the wind angle of attack. Because the three-component force varies accordingly with the wind angle of attack, it is generally taken as the maximum value in the range of \(-3^{\circ}\) to \(3^{\circ}\) deviating from the safe wind angle of attack.
## 3 Optically Pumped Probe Design
### Electronics Unit Design  
The probe electronics unit connects the front end of the probe to the magnetic signal processor. It provides temperature control for the probe's front end and generates excitation signals, processes the signals output by the front end, and outputs magnetic field signals to the magnetic signal processor.
# A Survey of Vehicular VLC Methodologies
## 2 Visible Light Communication
### VLC Transmitter  
VLC transmitters enable the conversion of data into modulated light emitted by LEDs, allowing transmission through the optical channel medium, as depicted in Figure 2. The transmitter serves a multifunctional purpose by simultaneously emitting both light and data. It generates white light through the LED while modulating the light with the information, ensuring that the transmission of data does not compromise the primary functionality of the transmitter. Additionally, the transmitter must fulfill lighting requirements, maintaining the same light power or supporting dimming capabilities. The modulation applied for dimming should align with the LED lights supported dimming levels, aiming to avoid flickering and ensure a stable light output [40]. There are four types of LEDs utilized in VLC applications, each with unique features suitable for specific use cases. The first type is the phosphor-converter (PC) LED, followed by organic LED (OLED), multi-color lighting RGB LED, and Micro-LED (\(\mu\)-LED) light. Organic LED lighting is a cost-effective option with limited bandwidth (around one megahertz [41; 42]), while \(\mu\)-LEDs offer a significantly larger bandwidth (approximately three hundred megahertz). RGB and PC LEDs are more commonly used than single-channel LEDs, with RGB LEDs providing a high output, equivalent to three times the modulated data output due to their three independent color channels. However, white PC LEDs are frequently preferred, as they are easier to implement and more cost-effective compared to RGB LEDs [43]. To achieve data modulation using the light emitted by LEDs and control the current flowing through them, a driver circuit is employed in conjunction with the LED light source [44].
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 4 Parameter Tuning and Evaluation through Simulations
### Metrics  
We can group the metrics we use in this study into three categories: success, overhead and latency.  
**Success**: The final goal of a multi-hop broadcast is to reach as many nodes as possible in a given area. We define the success with two different metrics. The _long-reach success_ is the proportion of cases where the dissemination goes further than the second hop, i.e., the message leaves the source area. It is normalized to that of simple flooding in the same situation. The other metric is the _coverage_, or the number of vehicles that received the message in the long-reach cases.  
**Overhead**: A high overhead is undesirable, as it occupies the limited shared bandwidth with useless data. It can be measured with the _redundancy ratio_, which we define as the ratio of vehicles that act as relays from the total number of receivers. Another metric that echoes the overhead is the _number of lost packets_, as congestion leads to this effect.  
**Latency**: The third performance indicator is the latency of the scheme. We may use the per-hop delay or the end-to-end delay. The _per-hop delay_ accounts for distance-based and MAC contention and for propagation time. The _end-to-end delay_ is averaged over all of the receivers, be they one or several hops away from the source.
# A Polarization-Dependent Frequency-Selective Metamaterial Absorber with Multiple Absorption Peaks
## 5 Experimental Results  
The MA was fabricated using standard photolithography. Firstly, the copper with a thickness of 0.5 \(\upmu\)m was deposited on the bottom surface of the polyimide film. Then, the photolithography was used to define the metallic patterned resonator at the top film surface. Lastly, the copper was evaporated, and a metal lift-off process was used to complete the pattern transfer. The fabricated sample composed by 18\(\times\)18 unit-cells with a dimension of 1.5 cm\(\times\)1.5 cm is displayed in Figure 8.  
The free space measurement method was adopted in the experiment. A pair of horn antennas were connected to the Agilent N5224A vector network analyzer (Agilent Technologies Inc, Santa Clara, CA, USA) via VNA extenders VDI VNAX600 (Virginia Diodes Inc, Charlottesville, VA, USA) that were used for frequency range 90-140 GHz. In this case, one antenna acted as a transmitting antenna and other as a receiving antenna. In order to avoid the reflections from the environment, the calibration was performed as follows. The reflections from an identical copper plate, which acts as an ideal reflector, were firstly measured. Then, the actual reflections from the sample were calculated by comparison of the reflection from the structure and the reflection from the copper plate. It should be noted that, for the given sample dimensions, a minimum far-field distance of 40 cm must be met. Moreover,  
Figure 8: Fabricated metamaterial absorber: (**a**) real size; (**b**) enlarged portion.  
Figure 7: Simulated absorption characteristic of the proposed structure for different polarization angles \(\varphi\) for (**a**) the TE-polarized wave and (**b**) the TM-polarized wave.  
the measurement of responses for different incident angles has not been carried out due to the large scattering of this finite sample at wide incident angles [30].  
The measured MA absorption for various polarization angles (\(\varphi\)) for both TE-polarized and TM-polarized normal incident waves is presented in Figure 9. For TE-polarization, a dual-band absorption is achieved for a polarization angle of 45\({}^{\circ}\). However, only one absorption peak with absorptivity of above 70% is achieved for polarization angle of 45\({}^{\circ}\) in the case of TM-polarized incident wave, which is in agreement with the simulated results.
## 5 Results
Through the utilization of scatter plots, the assessment of correlations between real-world values and values predicted by a model, as well as the evaluation of prediction quality, is carried out;  
Figure 4: Scatter plot of real-life values \(y\) and predicted values \(\hat{y}\) of costs for GAV\({}_{\text{ens}}\) model. (a) \(L\&V\) subset, (b) \(T\) subset. Figure 3: Scatter plot of real-life values \(y\) and predicted values \(\hat{y}\) of costs for SAV\({}_{\text{ens}}\) model. (a) \(L\&V\) subset, (b) \(T\) subset. An analysis of the \(PE^{\prime}\) distributions allows us to select the \(\mathrm{SAV}_{\mathrm{ENS}}\) model as the one that is slightly more stable when comparing training and testing errors. The shares of \(PE^{\prime}\) within the range \(<\)\(-\)30%; 30%\(>\) were as follows:  
* For \(\mathrm{SAV}_{\mathrm{ENS}}\): 90.6% for the \(L\mathcal{E}V\) subset and 94.7% for the \(T\) subset;
* For \(\mathrm{GAV}_{\mathrm{ENS}}\): 84.0% for the \(L\mathcal{E}V\) subset and 94.7% for the \(T\) subset. Table 9 presents a summary and performance measures of the two developed models, specifically \(RMSE\) and \(MAPE\) values, as well as the maximum values of \(APE^{\prime}\). The maximum values of \(APE^{\prime}\), which are lower for the \(\mathrm{SAV}_{\mathrm{ENS}}\) model, confirm that its predictive performance is slightly better than that of the \(\mathrm{GAV}_{\mathrm{ENS}}\) model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**EoNN** & \(RMSE_{L\mathcal{E}V}\) & \(RMSE_{T}\) & \(MAPE_{L\mathcal{E}V}\) & \(MAPE_{T}\) & \(\begin{array}{c}max\\ \{APE^{\prime}\}_{L\mathcal{E}V}\end{array}\) & \(\begin{array}{c}max\\ \{APE^{\prime}\}_{L\mathcal{E}V}\end{array}\) & \(\begin{array}{c}max\\ \{APE^{\prime}\}_{L\mathcal{E}V}\end{array}\) \\ \hline \(\mathrm{SAV}_{\mathrm{ENS}}\) & 436.8 & 344.5 & 15.1\% & 9.3\% & 57.6\% & 29.6\% \\ \(\mathrm{GAV}_{\mathrm{ENS}}\) & 266.3 & 257.5 & 14.9\% & 9.9\% & 64.6\% & 31.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Values of general predictive performance measures for the EoNN models. In general, it can be concluded that the obtained results are satisfactory. The developed predictive models provide cost estimates within the assumed and preferred range of accuracy for the majority of both training and, most importantly, testing cases. The model based on simple averaging performs slightly better and offers higher accuracy.