esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks
## 6 Simulation and Performance Assessment
### Comparative Graphs  
This section mainly refers to the comparison of the proposed method with single-hop routing and the existing GR-Protocol. Figure 12 shows the maximum vacation time. It is observed that our proposed mechanism displays almost 50% higher vacation time than that of single-hop routing. GR-Protocol divides the entire topology into several grids in a way that sensor nodes in the same grid are all  
Figure 11: Minimal energy of each node.  
Figure 10: Time needed to recharge the specific node.  
serviced by the WPCD at the same time. So, this technique requires more time to recharge the sensor nodes for its one-to-many wireless recharging method. In our proposed method, the WPCD follows the shortest path algorithm to traverse the sensor nodes and at the same time, our energy conserving technique helps to keep enough energy for each sensor node. Thus, our solution outperforms the GR-Protocol. Energy management for a WSN is a basic issue that needs to be standardized. In our WSN, each node has some tasks (data transmission) to perform by utilizing its energy, while our objective is to perform those tasks with a minimum amount of energy.  
In the proposed approach, the clustering technique minimizes the transmission (data sending/receiving) energy of the sensor nodes. In single-hop routing, all the sensor nodes send the data to the base station directly with high transmission energy which gives the worst performance. GR-Protocol optimizes the traveling path of the WPCD only and does not consider the overall protocol for data routing in the WSN. Figure 13 shows that the proposed protocol outperforms both methods regarding energy consumption.  
Figure 12: Comparative vacation times of the WPCD.  
Figure 13: Energy consumption of 50 deployed nodes.  
Once the WPCD completes its first cycle and returned to self-maintenance at the rest station, the next cycle needs to occur to make the WSN functional again. For this cycle, the remaining battery of each node plays a pivotal role. Suppose node 5 has a fifty percent charge remaining, then the WPCD spends less time to fully charge its battery. So, the proposed method manages the high remaining energy at each node. For the remaining energy, single-hop routing performs poorly. GR-Protocol shows moderate performance as its energy consumption level is better than single-hop and worse than our proposed method. This is shown in Figure 14.  
Table 4 represents the overall time consumed, total distance and shortest traveling time by the WPCD in the renewable wireless network.
## 4 Results and Discussion
### Testing on a Different Dataset: Stock Price Prediction  
In this section, the primary goal was to validate the generalization ability and adaptability of the proposed method for detecting cyberattack behaviors on different types of datasets. Applying the method to the domain of stock price prediction, which is significantly different from the original task, was intended to demonstrate the robustness and efficacy of the proposed method in dealing with various types of problems.  
Table 7 shows that the proposed method achieved the highest Precision, Recall, and Accuracy at 0.91, 0.87, and 0.90, respectively, significantly outperforming the other models. Compared to the other models, the proposed method demonstrated the best performance, showcasing its excellent adaptability and generalization capability when dealing with different types of datasets. Among these, ChatGLM3-6B also displayed high performance, with Precision, Recall, and Accuracy at 0.88, 0.84, and 0.87, respectively. The baseline Transformer model exhibited the poorest performance, with Precision, Recall, and Accuracy at 0.78, 0.75, and 0.77, respectively. From a mathematical perspective, the proposed method, with its synchronous attention mechanism and loss function tailored for specific tasks, has significant advantages in capturing and processing complex data patterns. The synchronous attention mechanism effectively integrates multi-source information, enhancing the model's capability to capture factors influencing stock prices, while the specific loss function further optimizes the model's performance in stock price prediction tasks, leading to its superior performance in Precision, Recall, and Accuracy over other models. Furthermore, the design of the proposed method fully considers the temporality and dynamism of the data, improving the accuracy of the model in predicting stock price changes through refined feature extraction and information processing strategies. These experimental results indirectly reflect the importance of the proposed method's design philosophy and mathematical characteristics in enhancing model generalization capability and adapting to different task types, providing new perspectives and ideas for subsequent research in related fields.
## 4 Experimental Results
### GPS Tracking Test  
In this study, the term location refers to the data collection process used to determine the wheelchair position in a particular setting. For this, different localization methods, algorithms, and approaches are needed. By synchronizing these parameters, we can use ubiquitous computing applications to calculate the position and build a positioning system [25]. A collection of localization techniques and algorithms enables accurate position estimation of objects in both internal and exterior settings. The Global Positioning System (GPS) is a technology that can be used outside. A device will detect a GPS signal and use that signal to find the wheelchair. After processing, the data is transmitted over the cellular network in a General Packet Radio Service (GPRS) format to the operator and is then sent to the user's device to display a map of the location and the data detected as an image and a symbol showing the location and status of the wheelchair. The longitude and latitude (states 1 to 3) are shown in Table 4. Some application interfaces for wheelchair users are shown in Figures 10-13. This application enables the wheelchair user to determine the expected GPS setting, GPS moving (tracking 1), GPS moving (tracking 2), and GPS moving (tracking 3). A supervisor can also manage the user's account, contact the wheelchair user, and determine the latest location of the wheelchair and the shortest way to get there using Google Maps. They can also receive notifications via e-mail, Line, Facebook, and short message service (SMS) if the user is in danger. In addition, they can see how much power is left in the wheelchair batteries and whether there is enough to return home.  
Figure 10: GPS tracking app settings.  
Figure 9: Displacement distance comparison chart.  
Figure 11: GPS moving (tracking 1).  
Figure 12: GPS moving (tracking 2).  
Figure 13: GPS moving (tracking 3).
###### Abstract  
Significant changes in road and traffic conditions in transition areas are key to traffic organization and guaranteeing safety in freeway work zones. Currently, most of the related studies on transition area use theoretical calculations, traffic and driving simulations, and the impact of different transition area conditions on drivers' psychophysiological indicators and driving behavior are unclear. In this paper, the gap acceptance theory was used to establish a calculation method of the transition area length, and the transition area length was calculated under different closed lane widths, speed limits, and traffic volumes. Based on the results of our theoretical calculations, naturalistic driving experiments were conducted with 48 participants in 12 scenarios involving 3 lane closure forms and 4 transition area lengths, and the relationship of transition area with driving workload and vehicle speed was determined. A transition area that was too short or too long increased traffic safety risks. The overall experimental results were consistent with the theoretical calculation length, and the theoretical calculation model was reliable. Compared to unaffected straight-through vehicles, merging vehicles and vehicles affected by merging have lower speeds, higher driving workloads, and increased traffic safety risks. An increase in the number of lanes in the transition area will result in increased driving workloads and vehicle speeds. Based on the changes in vehicle deceleration points and driving workloads, the affected area of the transition area was measured. When the speed limit was 60 km/h, the upstream affected areas of the transition areas with four, three, and two lanes were 1000 m, 850 m, and 700 m, and the downstream affected areas were 450 m, 400 m, and 350 m. These research results can provide a reference for improving traffic organization and guaranteeing safety in freeway work zones.  
work zone; transition area; driving behavior; driving workload; naturalistic driving +
Footnote †: journal: Applied  
0000-0002-4000-3300][PERSON]
## 5 Conclusions  
The cost-reducing techniques of RES components used in micro-grids are mainly PV systems and WTs, and these provide new possibilities for clean, reliable, and affordable systems in micro-grid configurations. New hybrid micro-grid systems can quickly and cost-effectively be implemented in remote locations, thus, opening new opportunities to investigate the new concepts as alternatives to conventional power systems.  
ESS components, particularly batteries based on Li-ion or sodium technologies, are identified as a key technology for the integration of intermittent RES. This, in turn, introduces major challenges to the control system for the appropriate management of this resource.  
The integration of an energy management system in a future micro-grid, for optimizing future aggregators' assets, should be coordinated through an advanced ICT data hub platform that allows DSOs and aggregators to deal with innovative aspects of their distribution network operation and planning with smart energy storage solutions. This would include V2G integration, power electronics-based interface with bi-directional power control of DER and ESS, and integrating IoT (Internet of Things) into the Smart Grids.  
With reference to a new and improved energy grid, this paper first reviewed trends in RES, power converters, and control strategies, and analyzed trends in battery energy storage and the relevant issues in battery charging and BMS. An alternative micro-grid architecture was then proposed to overcome the lack of flexibility of the classic energy grid. By mixing DC and AC sources, the hybrid micro-grid shows an alternative architecture where the use of bi-directional EV chargers may allow the creation of a micro-grid directly interconnecting all the nodes with bi-directional energy flows: Main grid node, RES node, energy storage nodes, both on-board the vehicle and inside the micro-grid structure. The proposed model is also sustained by the new products emerging in the market, such as new solar inverters that include are appearing [2], where a local energy storage for the RES and hence can enable bidirectional power flows from/towards the RES.  
Research: S.S., R.S., and L.M.-P. Writing: S.S., R.S., and L.M.-P.  
This work was partially supported by Tuscany Region under \"Progetto SUMA\" and partially supported by CrossLab project, University of Pisa, funded by MIUR \"Department of Excellence\" program.  
Discussions with [PERSON], [PERSON], [PERSON] from MIT, US under the MIT-UNIPI seed fund program are gratefully acknowledged.  
The authors declare no conflict of interest.
# Energy Efficient Underlaid D2D Communication for 5G Applications
## 3 Proposed Modified Derivative Algorithm
Figure 4 shows the energy efficiency of D2D users with the reference density of cellular users by using the derivative algorithm. We assumed an uplink environment where 5 multiple bands of the spectrum with a bandwidth 20 MHz are shared by cellular and device users. We can observe that the EE decreases with the increase in the reference density of cellular users if the D2D user's reference distances of 15 m, 20 m, and 25 m are fixed. We are aware that the severity of the channel fading worsens with distance, causing a fall in signal-to-interference ratio. As a result, the average sum rate declines, which lowers the EE. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline K (no of bands) & 5 \\ Wi (bandwidth of ith band) & 20 MHz \\ \(\alpha\) (Path loss exponent) & 4 \\ \(\mathrm{\SIUnitSymbolMicro c}\),i (cellular probability for ith band) & 0.1 \\ \(\mathrm{\SIUnitSymbolMicro c}\),i (D2D probability for ith band) & 0.1 \\ \(\mathrm{\SIUnitSymbolMicro c}\),i (cellular threshold for ith band) & 0 dB \\ \(\mathrm{\SIUnitSymbolMicro c}\),i (D2D threshold for ith band) & 0 dB \\ \(\left[\mathrm{R_{c0,01}},\ \mathrm{R_{c0,02}},\ldots,\ \mathrm{R_{c0,05}}\right]\) & [50,60,70,80,90] m \\ \(\left[\mathrm{R_{d0,00,1}},\ \mathrm{R_{d0,02}},\ldots,\mathrm{R_{d0,05}}\right]\) & [10,20,30,20,10] m \\ \(\left[\mathrm{\SIUnitSymbolMicro c},\ \mathrm{\SIUnitSymbolMicro c},\ \mathrm{\SIUnitSymbolMicro c},\ \mathrm{\SIUnitSymbolMicro c},\ \mathrm{\SIUnitSymbolMicro c},\ \mathrm{\SIUnitSymbolMicro c}\right]\) & [10,10,10,10] \(\times\mathrm{\SIUnitSymbolMicro c}\),ref \\ \(\left[\mathrm{\SIUnitSymbolMicro c}\mathrm{\SIUnitSymbolMicro c},\ \mathrm{\SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c },\mathrm{\SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c},\mathrm{ \SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c },\mathrm{\SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c},\mathrm{ \SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c},\mathrm{\SIUnitSymbolMicro c },\mathrm{\SIUnitSymbolMicro c},\mathrm{\  
Table 2 presents the main simulation parameters by using the modified derivative algorithm. Figure 5 shows the energy efficiency of D2D users versus the reference density of D2D users by using the modified derivative algorithm. We assumed an uplink environment where 5 multiple bands of the spectrum with a bandwidth 25 MHz are shared by cellular and device users and the total transmission power of D2D users is 65 mW, \(\lambda_{\text{d,ref}}=10^{-4}\), \(\lambda_{\text{c,ref}}=10^{-5}\). As the D2D users increase, the EE rises and then it slowly decreases if the cellular user's transmission power is 325 mW, 375 mW, and 425 mW are fixed. This is a result of the increasing distortion brought on by cellular transmission. We also observed that the EE decreases with the increase in cellular power because the increase in cellular power creates interference.
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 2 NVIS Characteristics
### Ordinary and Extraordinary Waves  
NVIS communications are completely dependent on the ionosphere and its behavior to the Sun. As the ionization of the upper layers of the ionosphere is strongly dependent on the Sun's radiation, the ionosphere varies both daily and seasonally.  
Because the energy from the Sun varies both daily and seasonally, the ionosphere is never static. The Sun, for instance, has times of extreme activity that causes storms and severe disruption to the propagation. Moreover, the ionosphere's behavior is affected by even more parameters, such as the height and the mixture of gasses [17].  
This chemistry that was just mentioned also directly affects the rate of ionization, just as the density of atoms and the intensity of radiation do. Some chemical reactions take place quickly, causing high ionization, while others are slower, resulting in less ionization [10].  
All the ionization that was explained illustrates the ionosphere's interaction with radio waves. When a radio wave reaches the ionosphere, the electric field in the wave forces the electrons in the ionosphere into oscillation at the same frequency as the radio wave. Some of the radiofrequency energy is released due to this resonant oscillation, on which the oscillating electrons may either re-combine, thereby losing the radio energy, or re-radiate the original wave energy (refraction) [17].  
The refraction phenomenon is fundamental in all ionospheric links, and it occurs because ionized molecules present free electrons that are able to vibrate in response to the radio signals reaching them. The behavior of each of these moving electrons resembles small dipole antennas. Radio waves are bent away from areas of high ionization because these areas have a lower refractive index. This bending effect reduces as the radio wave frequency increases, until a point where it suddenly stops, letting the waves pass on through (this value is known as the critical frequency) [17].  
As a result to the electron's vibration, it is quite normal for a radio wave to have its polarization changed by the ionosphere. Furthermore, waves of different polarization can be refracted in different amounts. This leads to their return to Earth in two different rays: the ordinary and extraordinary rays, both with distinct properties [10]. Just as with reflections from other layers of the atmosphere or at other times, these rays can have different phases, amplitudes, frequencies, and times of arrival [17]. These different properties can be used in order to improve telecommunication links, as polarization diversity techniques are an option in ionospheric channels.
## 2 Related Work
### Quantum Satellite Networks  
The quantum satellite network is an effective way to achieve QKD in the intercontinental domain. Moreover, as more and more quantum satellites are planned to be launched and interconnected [14], the research on the construction and application of quantum satellite networks is inevitable. Similar to the ground optical fiber QKD network, the quantum satellites can be used as trusted relays to form a large-scale quantum network. The problem of optimizing the orbits of the quantum satellites was studied in [24]. In [25], a point-to-multipoint quantum satellite relay scheme was proposed. The modeling research of quantum satellite constellations was conducted in [26]. Most of the routing schemes in existing research studies are virtual topology-based routing schemes, which treat the dynamic physical topology as a static one over a small time interval. However, they only make efforts to overcome the mobility of the satellite instead of increasing the efficiency of secret-key provisioning.
## 2 A Voltage Ride-Through (VRT) Control Model for Energy Storage Systems
### Dynamic Trajectory Feature Extraction  
To achieve precise identification of control parameters, key electrical response data are analyzed, from which the most sensitive feature indicators are selected as the core components of the optimization objective function.  
1. Positive Sequence Voltage Feature  
The voltage recovery time is selected as a characteristic, defined as the time required for the voltage to rise from the fault point to 90% of the steady-state value.  
\[T_{\text{rise}}=t(U_{1}=0.9U_{n})-t_{\text{fault}} \tag{11}\]  
Its features are used to measure the response speed of the voltage loop PI controller and the reactive power support mechanism.  
Active Power Feature  
The power fluctuation rate is extracted to reflect the controller's active regulation capability during the early stages of the fault.  
\[R_{P}=\max\left|\frac{dP(t)}{dt}\right|_{t\in[l_{0},l_{0}+100\text{ms}]} \tag{12}\]  
This feature is used to assess the response ability of the active power reference regulation loop to sudden disturbances.  
3. Reactive Power Feature  
The reactive power response delay time represents the action response time of the HVRT/LVRT controller.  
\[\Delta T_{q}=t(I_{q}>\gamma I_{q,ss})-t_{\text{fault}} \tag{13}\]  
In the equation, \(l_{q,ss}\) represents the steady-state value of the reactive current. The parameter \(\gamma\) is an empirical proportional coefficient, typically set in the range of 0.05 to 0.10, representing the effective response when the deviation from the reference value exceeds 5% to 10%. This feature reflects the delay time in initiating reactive power support by the HVRT/LVRT control logic.  
4. Active Current Feature  
The overshoot amount of current is extracted to characterize the control precision and stability of the d-axis current loop.  
\[\Delta I_{d,\text{over}}=\max I_{d}(t)-I_{d,ss} \tag{14}\]  
In the equation, \(I_{d,ss}\) represents the steady-state value of the active current. This feature is used to evaluate the transient overshoot phenomenon in the current inner loop PI regulation process.  
5. Reactive Current Feature  
The initial slope is selected to evaluate the reactive power support capability in the early stage of voltage disturbance.  
\[S_{Iq}=\frac{I_{q}(t_{2})-I_{q}(t_{1})}{t_{2}-t_{1}},\;t_{1}=t_{\text{fault}}, \;t_{2}=t_{1}+20\;\text{ms} \tag{15}\]  
The initial response slope is closely related to the reactive power support coefficients \(K_{1}\) and \(K_{2}\).  
6. Power Tracking Error  
The active and reactive power error is used to regulate the accuracy of the PQ controller.  
\[\text{MSE}_{P,Q}=\frac{1}{T}\int_{t_{0}}^{t_{0}+T}\Bigl{(}P,Q^{\text{sim}}(t)- P,Q^{\text{meas}}(t)\Bigr{)}^{2}dt \tag{16}\]  
To account for the waveform quality during steady-state operation, the Total Harmonic Distortion (THD) is introduced as a penalty term. It is defined as follows:  
\[\text{THD}_{i}=\frac{\sqrt{\sum_{t=2}^{50}U_{h}^{2}}}{U_{1}} \tag{17}\]  
where \(U_{h}\) represents the RMS value of the h-th harmonic.
# PPP and PPP-AR Kinematic Post-Processed Performance of GPS-Only, Galileo-Only and Multi-GNSS
## 3 Results
### Some Station Examples
This happens mainly because more satellite measurements are participating in the LSE solutions. It is interesting to observe that even though the Galileo-only solution is not better than the GPS-only solution (but it is of the same order), when using both systems the combined solution is improved with respect to the GPS only one. This means that adding Galileo ameliorates the overall performance of positioning both in PPP as well as in PPP-AR mode. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NYA2** & **Mode** & **East (mm)** & **North (mm)** & **Up (mm)** \\ \hline Galileo & PPP & 4.9 & 5.0 & 13.9 \\  & PPP-AR & 2.8 & 2.9 & 15.5 \\ \hline GPS & PPP & 4.2 & 4.1 & 16.0 \\  & PPP-AR & 2.4 & 2.2 & 11.7 \\ \hline GPS + Galileo & PPP & 3.3 & 3.1 & 10.3 \\  & PPP-AR & 2.5 & 2.1 & 9.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 1-\(\sigma\) values PPP and PPP-AR for NYA2 station. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BRUX** & **Mode** & **East (mm)** & **North (mm)** & **Up (mm)** \\ \hline Galileo & PPP & 4.7 & 4.6 & 11.7 \\  & PPP-AR & 2.6 & 2.9 & 10.3 \\ \hline GPS & PPP & 4.7 & 4.1 & 9.2 \\  & PPP-AR & 2.4 & 3.4 & 8.5 \\ \hline GPS + Galileo & PPP & 3.4 & 2.7 & 9.1 \\  & PPP-AR & 2.1 & 2.4 & 7.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 1-\(\sigma\) values PPP and PPP-AR for BRUX station. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CAS1** & **Mode** & **East (mm)** & **North (mm)** & **Up (mm)** \\ \hline Galileo & PPP & 7.1 & 6.8 & 16.6 \\  & PPP-AR & 4.2 & 5.2 & 15.6 \\ \hline GPS & PPP & 6.4 & 6.8 & 15.2 \\  & PPP-AR & 3.8 & 5.2 & 14.3 \\ \hline GPS + Galileo & PPP & 4.6 & 4.5 & 11.3 \\  & PPP-AR & 3.1 & 3.7 & 10.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 1-\(\sigma\) values PPP and PPP-AR for CAS1 station.