esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparations for Galileo PRS in Poland
## 6 Conclusions for Future PRS Tests
The methods of GNSS OS signal jamming and spoofing are widely known and available nowadays [17], but due to the resilience and robustness of the Galileo PRS service, attacking its signals will definitely be much more difficult. The increasing risk of intentional interference events that could affect GNSS services and the receivers' performance is driving the necessity to develop national systems for GNSS threats detection. In the case of the Polish system, the low-level system monitor will be based on the time and spectral analysis of the frequency bands dedicated to GNSS systems and detection of unexpected jamming signals. On the other hand, the high-level system monitor will be based on monitoring the quality, integrity and availability of specific GNSS services in order to identify the presence of spoofing signals. The first components of such a system (equipped with sophisticated laboratory measurement equipment)--the GNSS Galileo Mobile Measurement System (Polish acronym: MSP2G)--has just been developed by the NIT in cooperation with the Chancellor of the Prime Minister, which financed the project. On the basis of the experience gained during the MSP2G functional tests, further components (probes) of the national GNSS threats detection system will be developed, utilizing software-defined radio platforms dedicated to various specific applications. **Author Contributions:** Conceptualization, K.B. and R.N. ; methodology, K.B., A.L. and R.N. ; software, R.N. ; validation, K.B. and A.L. ; writing--original draft preparation, R.N. ; writing--review and editing, K.B. and A.L. ; visualization, R.N. ; supervision, K.B. All authors have read and agreed to the published version of the manuscript. **Funding:** The jamming test results presented in the article were carried out as part of the PRS Pilot Project for the Demonstration (3 PfD) project, cofounded from the European Union Agency for the Space Programme under Joint Test Activities (grant no. GSA/GRANT/03/2016/3 PfD), and the funds reserved for science in the years 2018-2022, granted for the purpose of a co-financing international project by the Polish Ministry of Science and Higher Education (grant no. 5026/GSA/2019/2). **Data Availability Statement:** The measurement data presented in the manuscript are not publicly available. **Conflicts of Interest:** The authors declare no conflict of interest.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 5 Proposed Method  
Here, in this research, collision-aware routing using multi-objective SOA (CARMOSOA) is designed to decrease the additional consumption of energy at each node. At preliminary circumstances, every node is produced through comparable energy bases.  
For the duration of transmission, energy is condensed for every node and determined by the progression. Figure 3 shows the overall block diagram for the energy-efficient WSN-IoT.  
Step 1: At first, the sensor node is deployed.  
Step 2: Then the multi-tier-based clustering model is created along with the sensor nodes.  
Step 3: After that, the process of the fitness function is calculated based on the proposed MOSOA.  
Step 4: Then the process of CH selection occurs and progress the routing over WSN-IoT based on MOSOA.  
Step 5: After the clustering/routing process, the transmitted data is analyzed (to determine whether it is collision free or not).
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 5 Use Cases and Applications
### Intelligent Transport Systems  
The field of transportation is experiencing transformative growth through integrating information and communication technologies into ITS. Innovations such as autonomous driving, remote driving, collision prevention, driver assistance, and mobility management actively enhance road safety and curtail accidents. Using sensors permits these systems to gather and analyze environmental data to enact essential measures. Additionally, this data is exchanged with neighboring vehicles and infrastructure through communication technologies like IEEE 802.11p and C-V2X, further advancing the ITS landscape. In vehicular networks, battery-powered IoT sensors and devices play a crucial role by transmitting active radio signals for data exchange. The increasing scale of transportation systems worldwide anticipates a substantial proliferation of sensors, leading to heightened demand for frequent data transmission.  
Consequently, the forthcoming ITS landscape necessitates communication radios with power efficiency and advanced resource management capabilities. Using a symbiotic scenario, intelligent transportation systems (ITS) can be elevated, leveraging the cooperative potential of C-V2X and IEEE 802.11p networks, promoting communication efficiency regarding spectrum utilization and energy consumption [91]. Vehicles will possess genuine AI-driven capabilities, learning from real-world experiences facilitated by the truly real-time communication prowess of 6G. Integrating such communications becomes paramount within the framework of 6G, as they lay the foundation for ensuring high-reliability and low-latency interactions and the secure exchange of massive driving data and the surrounding environment [98]. Applications such as autonomous driving, vehicle-to-vehicle (V2V), and V2X or integrating numerous sensors on upcoming vehicle models may lead to a collective data rate of 1 Gb/s. This data capacity can be harnessed for V2V and V2X interactions.
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 4 Proposed Routing Protocol
### Cluster Formation
#### 4.4.1 Node Move-In  
The Node Move-In protocol outlines how a node can join an existing cluster or form a new one in our proposed clustering mechanism. When a node, denoted as \(UN_{j}\), detects a beacon from a neighboring Central Node (CN), it initiates the joining process. Here is how it aligns with our clustering approach:  
**Node Identification and Preparation:** The joining UAV node (\(UN_{j}\)) identifies free channels and prepares its Accessible Channels List (ACL). It then scans for beacons on these channels, allowing it to identify neighboring clusters and nodes. **Broadcasting and Sharing:** Upon detecting neighboring clusters, \(UN_{j}\) broadcasts its neighbor and channel lists. This information exchange facilitates cluster formation and connectivity assessment. **Graph Construction and CNRF Calculation:**\(UN_{j}\) constructs bipartite and maximum edge biclique graphs based on the received information. It then calculates the Central Node Resolution Factor (CNRF) to evaluate connectivity. **Cluster Joining:** If neighboring CNs are detected, \(UN_{j}\) attempts to join the cluster. It selects the CN with the highest CNRF value or the lowest ID if CNRF values are identical. \(UN_{j}\) then compares its CNRF with the selected CN and requests to join as a Cluster Member (CM). **Cluster Formation:** If \(UN_{j}\)'s CNRF is higher than the selected CN's, and its neighboring node set matches the CN's CM list, \(UN_{j}\) becomes the new CN, and the existing CN becomes the Secondary CN (SCN). Otherwise, \(UN_{j}\) joins as a CM or attempts to form a new cluster if no suitable cluster is found. **Further Considerations:** If \(UN_{j}\) provides better connectivity than the existing Nearest Node (NN) with a neighboring cluster, it becomes the NN. Additionally, \(UN_{j}\) can declare itself as CN and form a new cluster if joining existing clusters fails. This protocol ensures flawless integration of nodes into clusters, optimizing network organization and communication efficiency. From Algorithm 3, The joining UAV Node (\(UN_{j}\)) starts to scan its span and prepares the \(ACL_{j}\) for \(UN_{n}\). \(UN_{j}\) then broadcasts \(ACL_{j}\) and \(UN_{n}\) and create biclique graph (\(G_{ci}\)) based on bipartite graph (\(G_{pi}\)) and calculates \(CNRF_{j}\). then it shares the value with neighboring \(UN_{s}\). After that, it checks if the joining node belongs to the neighboring node or not, if the value of the joining node's channel is greater than 1 then it again checks if the joining accessible channel list intersects with the central node's ACL and is greater than 2. The \(UN_{j}\) then joins \(CN_{j}\) as CM. Otherwise, \(CN_{j}\) rejects the joining request from \(UN_{j}\) and tries to join with other \(CN\)s. However, If \(CN_{j}\) fails to join any cluster it makes its cluster and states CN.
## 1 Introduction
### Research Aim  
The aim of this study is twofold: (1) to evaluate the use of early energy relative to late reverberation and of the global energy to judge the absolute distance to a virtual sound source; (2) to investigate the influence of two environmental characteristics, the visual spatial boundary and the volume of the environment, on the auditory distance perception of a virtual sound source.  
An experiment was conducted with online listening tests in which participants had to evaluate the distance of virtual sound sources produced by different rendering methods. The choice of the rendering methods was made to design stimuli categories in which the availability and reproduction quality of acoustic cues are different. Two different distance-rendering models have been evaluated in contrast to a method based on actual measurements of spatial room impulse responses (SRIR).  
A first model, labeled \"envelope-based model\", was used to investigate whether energetic discrepancies between the model and measurements influence distance perception. Its evaluation enabled the investigation of whether a portion of the early energy of an impulse response can be considered as fused with the direct sound to establish an early-to-late energy ratio as a relevant distance cue.  
A second model, labeled \"intensity-based model\", was designed to accurately reproduce intensity while maintaining reverberation-related cues constant with distance. The perceptual evaluation of this model provided insight into participants' acoustic cues weighting strategies, which included intensity and reverberation-related cues.  
The experiment gathered two pools of 60 normal-hearing adult participants, each performing the experiment with a different inclusion criterion on the distance between the participant and the facing wall. The analysis of the differences between both groups' results was made to investigate the influence of this visual spatial boundary condition on auditory distance perception. Participants of both groups had to report the dimensions of the room in which they were during the experiment. The volume estimated from this report was considered as a primary descriptor of the quantity of reverberation in the room. As participants were each accustomed to the specific acoustic properties of the room where they were running the experiment, it was decided to study, through each participant's reported room volume and reported distances for each distance-rendering method, the influence of a possible acoustic familiarity on auditory distance perception.
# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
## 3 Results and Discussions
### Turbulence Parameter Prediction  
This subsection discusses the proposed model's performance in predicting the \(C_{n}^{2}\) parameter, with the results depicted in Figure 3i-I. Firstly, we examine the model's performance in predicting the \(C_{n}^{2}\) parameter when no other impairments are present in the system. Figure 3i illustrates the boxplot of the prediction, indicating high accuracy. The obtained R-squared and RMSE metrics were 1 and 0.1 m\({}^{-2/3}\), respectively. Next, we evaluated the model's performance in predicting the \(C_{n}^{2}\) parameter when the system was subject to 500 ps/nm CD impairment. The results in Figure 3j indicate that the prediction performance was similar to that in Figure 3i when there was no CD impairment. The achieved R-squared and RMSE metrics were 0.03 and 0.1 m\({}^{-2/3}\), respectively. Figure 3k reports the \(C_{n}^{2}\) parameter prediction when the channel is subject to ASE noise defined by 12 dB OSNR. In this scenario, we observed that the model demonstrated high accuracy in predicting the harsh \(C_{n}^{2}\) parameter, defined by high \(C_{n}^{2}\) values. However, when the \(C_{n}^{2}\) parameter values were low, the impact of ASE noise on the signal was high, leading to difficulty in predicting the OSNR parameter. The obtained R-squared and RMSE metrics were 0.84 and 0.57 m\({}^{-2/3}\), respectively. Finally, we evaluated the proposed model's capability in predicting the \(C_{n}^{2}\) parameter when the system was subject to harsh conditions defined by 12 dB OSNR and 500 ps/nm CD. We obtained similar results to those in Figure 3k because the dominance of ASE noise complicates the model's capability of predicting the \(C_{n}^{2}\) parameter. In general, the model demonstrated high accuracy in predicting the \(C_{n}^{2}\) parameter when no ASE noise was present or under severely turbulent channels. However, when strong ASE noise was added to the system, the model's capability decreased significantly in predicting low \(C_{n}^{2}\) values. Table 2 below summarizes the performance of the proposed model in predicting the various parameters in terms of R-squared and RMSE metrics.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 6 Performance Evaluation
### Region-Level Simulation Environment  
In the region-level simulation environment, we consider an urban area that can be divided into 100 different regions. In each region, we assume that the bidirectional traffic flows are independent from each other, and follow a poison distribution with \(\lambda^{e}\) for the eastbound traffic flow and \(\lambda^{w}\) for the westbound traffic flow. Thus, we can have a group of parameters as \(\{\lambda^{e}_{1},\lambda^{e}_{2},...,\lambda^{e}_{100}\}\) for the eastbound traffic flow and \(\{\lambda^{w}_{1},\lambda^{w}_{2},...,\lambda^{w}_{100}\}\) for the westbound traffic flow, which demonstrate the vehicle density for each area. Furthermore, we assume that the parameters \(\{\lambda_{1},\lambda_{2},...,\lambda_{100}\}\) follow a normal distribution with expectation \(\mu\) and variance \(\sigma\). As the variance \(\sigma\) increases, the vehicle density tend to be non-uniformly distributed among different areas. By default, we can set the coverage ratio in each area as 0.005 for V2V communications and 0.5 for V2I communications. The details of the parameter configuration are presented in Table 1.  
Furthermore, we assume 1000 different content replicas with Zipf distribution to simulate the various request demands. The popularity (or request rate) of the i-th replica is \(\eta_{i}=i^{-s}/\sum_{k=1}^{M}k^{-s}\). The value of parameter \(s\) is usually in the range of \(0.271\leq s\leq 1\), according to previous application services. Here, we can consider different skewness to simulate popularity distribution of replicas. We present the popularity distribution of the top-30 replicas with different parameter \(s\) setup in Figure 8. Without loss of generality, each RSU has a uniform storage capacity for content replication.  
Figure 7: Distributed online replication.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 5 Conclusions
Hence, distributed computing environments such as high-performance computing (HPC) platforms could boost computational efficiency for large-scale deployments, while periodic re-optimization incorporating new or updated geospatial data would enable periodic updating of optimization estimates as urban environments evolve over time. Therefore, the presented method will be highly useful for solar energy practitioners in the era of rapid urbanization and proliferation of solar energy, aiding in the mitigation of anthropocentric climate change and enabling a more sustainable living. Conceptualization, N.L. ; methodology, N.L. ; software, N.L. ; validation, N.L. and M.B. ; investigation, S.S. and K.S. ; data curation, S.S. and K.S. ; writing--original draft, N.L. ; writing--review and editing, S.S., K.S., G.S., D.M., B.Z., and M.B. ; visualization, N.L. ; supervision, G.S., D.M., and B.Z. ; funding acquisition, N.L. and D.M. All authors have read and agreed to the published version of the manuscript. The authors acknowledge financial support from the Slovenian Research and Innovation Agency (Research Project No. J7-50095 and Research Core Funding Nos. P2-0041 and P2-0115). Not applicable. The input VHR data used are publicly available through the Google Maps service (data providers Google, Airbus, CNES / Airbus, Maxar Technologies 2024) and the open LiDAR data of Slovenia through the Slovenian Environmental Agency LIDAR portal ([[https://gis.arso.gov.si/evode/profile.aspx?id=atlas_voda_Lidar@Arso](https://gis.arso.gov.si/evode/profile.aspx?id=atlas_voda_Lidar@Arso)]([https://gis.arso.gov.si/evode/profile.aspx?id=atlas_voda_Lidar@Arso](https://gis.arso.gov.si/evode/profile.aspx?id=atlas_voda_Lidar@Arso)), accessed: 1 June 2024). The agency also provides long-term global and diffuse irradiance measurements for Slovenia, available at its open web archive ([[https://meteo.arso.gov.si/met/sl/archive/](https://meteo.arso.gov.si/met/sl/archive/)]([https://meteo.arso.gov.si/met/sl/archive/](https://meteo.arso.gov.si/met/sl/archive/)), accessed: 5 June 2024). The created georeferenced ground truth dataset with annotated module masks and the trained deep learning models will be available on request. The authors thank the VHR imagery data providers Google, Airbus, CNES/Airbus, and Maxar Technologies 2024 and the Slovenian Environmental Agency for providing the long-term irradiance measurements and classified LiDAR data. The authors declare that they have no conflicts of interest.
###### Abstract  
In the search for novel anticancer complex, transition metal complexes of Schiff base derived from 2-aminopyrazine and salicaldehyde were successfully synthesized and characterized based on elemental analyses, spectroscopic and thermal analysis. The IR spectra showed the ligand is a tridentate chelator with O, N and N atoms. donor sites in the Zn(II), Co(II), Ni(II), and Mn(II) complexes. Contrary, it behaved a bidentate chelator in the Cu complex by O and N. Molar ratio data revealed that the ligand to metal ratio was 1:2 for Co(II) Cu(II) and Zn(II) while it was 1:1 for Mn(II) and Ni(II) complexes. The obtained complexes have the formulae [M(L)\({}_{2}\)] (where M = Co(II) and Zn(II), [M(L)Cl(2H\({}_{2}\)O)]-H\({}_{2}\)O (where M = Ni(II) and Mn(II) were octahedral geometry). The computational studies were performed by DFT calculations to compare the optimized geometries with the experimental values. The interaction of these complexes with calf thymus DNA (CT-DNA) was investigated by UV-Vis spectroscopic technique. Molecular docking studies were against three protein cancer to investigate the binding ability of the new compounds. The anticancer activity was studied with different cell lines and the IC\({}_{50}\) of the Cu(II) complex for (HOP-62) showed a practical result. The LD\({}_{50}\) values of Zn(II) and Co(II) complexes showed that they are non-toxic at doses up to 370 mg/kg.  
transition metal complexes; Schiff base; DFT; molecular docking; DNA binding +
Footnote †: journal: Applied Sciences
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs
### Drone Movement
#### 4.1.4 Overall Movement  
The overall force acting on a node is then the weighted sum of the three contributing forces, as defined above. In addition, we normalize \(fx\) and \(fy\) for each force.  
Mathematical Modeling of the Overall Force Governing a Node's Movement  
\[\begin{split} fx_{i}&=w^{\prime}\frac{f^{\prime}_{x_ {i}}}{\sqrt{(f^{\prime}_{x_{i}})^{2}+(f^{\prime}_{y_{i}})^{2}}}+w^{\prime \prime}\frac{f^{\prime\prime}_{y_{i}}}{\sqrt{(f^{\prime\prime}_{x_{i}})^{2}+ (f^{\prime\prime}_{y_{i}})^{2}}}+w^{\prime\prime\prime}\frac{f^{\prime\prime \prime}_{y_{i}}}{\sqrt{(f^{\prime\prime\prime}_{x_{i}})^{2}+(f^{\prime\prime \prime}_{y_{i}})^{2}}}\\ fy_{i}&=w^{\prime}\frac{f^{\prime}_{y_{i}}}{\sqrt{( f^{\prime}_{x_{i}})^{2}+(f^{\prime}_{y_{i}})^{2}}}+w^{\prime\prime}\frac{f^{ \prime\prime}_{y_{i}}}{\sqrt{(f^{\prime\prime}_{x_{i}})^{2}+(f^{\prime\prime \prime}_{y_{i}})^{2}}}+w^{\prime\prime\prime}\frac{f^{\prime\prime\prime}_{y_{ i}}}{\sqrt{(f^{\prime\prime\prime}_{x_{i}})^{2}+(f^{\prime\prime\prime}_{y_{i}})^{2}}} \end{split} \tag{8}\]  
We found values of 0.25, 0.5 and 1 for \(w^{\prime}\), \(w^{\prime\prime}\) and \(w^{\prime\prime\prime}\), respectively, to work best in our numerical experiment. These values prioritize the influence of the parent node on the resulting virtual force, which is only modulated by the two other forces (attraction by the clients, and repulsion by other nodes). This tends to ensure network cohesion (e.g., by preventing a group of clients from pulling a node so far from its parent that it would result in mediocre link quality between them). NB: these values have not been the subject of a systematic optimum search and are still open for fine-tuning.
## 5 Conclusions  
This work presents two learning models, LSTM and ANFIS, to forecast solar energy radiation in west-central Jordan. The proposed ML models process meteorological data for the last five years, downloaded from the Meteobleu site (Table 1). Many new parameters, not yet studied before in the literature, were considered for solar radiation forecasting in our study. A PCC algorithm is used to indicate the most influencing parameters correlated with solar radiation to facilitate the training process with LSTM and ANFIS.  
An important result of the proposed work is that new parameters greatly influence solar radiation which have not been previously investigated in other studies everywhere, such as direct short-wave radiation, diffuse short-wave radiation, temperature, sunshine duration and evapotranspiration. According to obtained results, these parameters remarkably influence solar radiation, differently depending on the seasons. Also, our study affirms that the LSTM is the best model for solar radiation forecasting when the PCC is not high (i.e., in the range 0.5-0.8). In contrast, the ANFIS model gives lower RMSE values concerning first-class parameters with a high correlation with the solar radiation (i.e., PCC values between 0.95 and 1) (Table 4). In total 24 meteorological parameters have been analyzed, a very large set; the results showed that the influence of each parameter varies significantly according to the season; altogether this we believe is an important result not yet reported in the literature. Summarizing the experimental results reported in Section 3 for the LSTM and ANFIS models, we obtained RMSE values in the range 0.04-0.8, MSE in the range 0.0016-0.64 and MAE between 0.034 and 0.86, very competitive values compared to the existing literature as reported in the comparative Table 5. We believe the model can be improved by building a local weather station, which provides meteorological records every 10-15 min. In addition, we plan to apply the methodology by exploiting information from another region, the city of El Kerak in the south of Jordan, with specific meteorological features different from west-central Jordan.  
This research received no external funding.  
Not applicable. Informed Consent Statement: Informed consent was obtained from all involved subjects. Data Availability Statement: Data of our study are available upon request.  
The authors declare no conflict of interest.