esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this work, the Tb\({}^{3+}\)-Yb\({}^{3+}\) couple is used, with Yb\({}^{3+}\) as the emitter and Tb\({}^{3+}\) as the sensitizer. The matrix in which the rare-earth ions are embedded is also important; a comparison of glass and glass ceramics will be discussed below. Based on previous studies of our group, the binary composition 70 SiO\({}_{2}\)-30 HfO\({}_{2}\) (silica hafnia) has been demonstrated to be an optimal matrix for the Tb\({}^{3+}\)-Yb\({}^{3+}\) couple. Glass-ceramic samples, in particular, are characterized by low-energy phonons which significantly reduce non-radiative transitions. It has been observed that DC layers have been tested on PV cells, but the results have been contrasting. In one study, [PERSON] et al. reported a 7% improvement in efficiency for a silicon solar cell covered with 1% Tb\({}^{3+}\) doped tellurite (TeO\({}_{2}\)-ZnO) glass layer [17]. However, an enhancement of only 1% was observed for a GaP solar cell covered with the same TeO\({}_{2}\)-ZnO glass but co-doped with 1% Tb\({}^{3+}\)-5% Yb\({}^{3+}\). For a phosphate glass doped with 0.5% Tb\({}^{3+}\)- 0.5% Yb\({}^{3+}\), the reported PV cell efficiency improvement was 0.34% [18]. Some studies have shown that the efficiency increases when using samples doped with Tb\({}^{3+}\) only but decreases when increasing the concentration of Tb\({}^{3+}\). However, the purpose of this process is to enhance the efficiency of solar cells. This decrease in efficiency is attributed to either self-absorption and energy transfer between Yb\({}^{3+}\) ions [17] or to the concentration quenching of Yb\({}^{3+}\) ions for a molar concentration higher than 2% [19]. In order to reach a high quantum efficiency of down-conversion (\(\eta\) QE), we investigate the efficient NIR down-conversion using the couple Tb\({}^{3+}\)/Yb\({}^{3+}\) embedded in the 70 SiO\({}_{2}\)-30 HfO\({}_{2}\) matrix. Two 70 SiO\({}_{2}\)-30 HfO\({}_{2}\) samples with the same molar concentration of rare earths (3% Tb\({}^{3+}\) and 12% Yb\({}^{3+}\)) grown at 900 \({}^{\circ}\)C for glass and at 1000 \({}^{\circ}\)C for glass-ceramic were investigated via photoluminescence emission (PL), photoluminescence excitation  
Figure 1: Solar spectrum indicating the bandgap and twice the band gap of silicon. High energy and low energy photons are converted, respectively, by down-conversion and up-conversion processes in order to fit the absorption band of silicon. Reproduced from [20]. Figure 2: Energy band diagram of Tb\({}^{3+}\) and Yb\({}^{3+}\) ions. After the absorption of a blue photon by Tb\({}^{3+}\), the process of energy transfer between a Tb\({}^{3+}\) ion and two Yb\({}^{3+}\) ions can produce two NIR photons suitable for silicon bandgap of 1.1 eV [21]. (PLE), as well as time resolved photoluminescence (TRPL) with calculation of the decay lifetimes as a function of the Yb\({}^{3+}\) concentration in our previous studies [20,22]. The decay lifetime allowed us to calculate the effective quantum efficiency for these samples which ranges from 123% to 179%.
# Assessing Cyber Challenges of Maritime Navigation
## 3 Findings
### Cyber Trends and Challenges
#### 3.3.8 Hybrid Threats  
In recent years, the West and the world's democratic societies have become increasingly exposed to hybrid threats. Hybrid threats are multi-faceted subversive or coercive activities that aim to undermine a state, frustrate its decision making processes, erode the trust in its institutions, stay below a threshold of detectability, and remain difficult to attribute. A threat is hybrid if it is part of a concerted set of mutually reinforcing actions. Hybrid threat campaigns are long-term and of varying intensity over time [70; 116]. As a critical domain for economy, resources, trade, transport, security, and defense, the maritime domain, including ports, finds itself a target of hybrid threats [105]. The fact that much of the (maritime) critical infrastructure is privately owned complicates the construction of a strong and coordinated response to hybrid threats [117; 118].  
Cyberattacks are one of the main vectors of hybrid threats [117; 119], and GNSS spoofing would also seem to be an attractive attack vector [118]. Foreign direct investments to gain ownership of critical infrastructure or technology (e.g., ports, hardware and software companies that supply the maritime industry) are part of the arsenal [119; 120].  
Since 2016, the EU and NATO have been cooperating on hybrid threats and cybersecurity [121], with these two topics prominently figuring in a common set of proposals [122; 123]. In relation to that cooperation, the European Centre of Excellence for Countering Hybrid Threats (Hybrid CoE) was set up in April 2017 in Helsinki to encourage strategic dialogue and carry out research and analysis on hybrid threats [124]. To this end, common exercises and workshops are carried out [125]. Following a closer look at hybrid threats versus sea lines of communication, the Hybrid CoE recently published a Handbook on Maritime Hybrid Threats [126].  
Due to their low-level and dispersed nature, hybrid threats can only be fully recognized by bringing together the information about occurrences of disruptions that individually may seem minor but are part of a larger whole, thereby creating situational awareness [70]. The sharing of information on disruptions is therefore crucial, just like it is the case for cyberattacks outside the hybrid context. For adequate business continuity, operators should develop a resilience against hybrid attacks. The EU plans to identify sectoral hybrid resilience baselines [70]--one sector would be the maritime. Sectorial approaches, including maritime, are proposed in several other EU policies [127; 128]. Risk analysis and enhancing of the overall resilience of EU critical maritime infrastructure with regard to cyber and hybrid threats, among others, are slated for action in the updated EU Maritime Security Strategy Action Plan [64]. A proper defense against hybrid threats requires all-of-society awareness and resilience [118; 129; 130].
# Constant Envelope Modulation Techniques for Limited Power Millimeter Wave Links
## 6 Summary and Conclusions  
Small PAPR modulation schemes are being used for better power efficiency. In CE-OFDM, the carrier is phase-modulated with the OFDM signaling to generate a constant envelope waveform. CE-MTO is a new suggested technique for constant envelope modulation, which is based on orthogonal waveforms in the time domain, and with the implementation expected to be simpler than that of the CE-OFDM. An analytical expression for the SER performance degradation caused by the co-channel interference in different scenarios is presented. It is shown that the SER decreases in the presence of interference when the modulation index is increased. The SER grows the higher the constellation order.  
Conceptualization, Y.P. and M.P.; Formal analysis, Y.B.; Investigation, Y.B.; Writing--original draft, Y.B.; Writing--review & editing, Y.P. and M.P.  
This research received no external funding.  
The authors declare no conflict of interest.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum Encoder
#### 3.3.3 Phase Gate for Complex Modulation  
In MIMO communication systems, the use of complex baseband modulation requires incorporating phase information into the quantum states. To achieve this, a phase gate (\(S\)), defined in Equation (7), is applied to the superposition states generated. This operation enables the integration of phase components essential for representing complex modulated signals.  
\[S=\begin{bmatrix}1&0\\ 0&i\end{bmatrix} \tag{7}\]  
The combined operation (Hadamard + phase) produces the following quantum states as in Equations (8) and (9).  
\[\psi_{0}=SH\ket{0}=\frac{1}{\sqrt{2}}(\ket{0}+i\ket{1}) \tag{8}\]  
\[\psi_{1}=SH\ket{1}=\frac{1}{\sqrt{2}}(\ket{0}-i\ket{1}) \tag{9}\]  
This encoding strategy is fundamental for enabling complex-valued quantum modulation, which is essential for high-fidelity quantum MIMO communication systems. It allows each qubit to carry more information, analogous to modulated carriers in classical communication systems.
## 2 Data Description
### Modeling with RF, GBR, and XGBoost  
In this study, RF, GBR, and XGBoost models were selected due to their ensemble learning capabilities and ability to handle complex data patterns, non-linear relationships, and diverse input features which are essential for predicting CO\({}_{2}\) DC in brine systems. In RF there is no need for feature normalization, it can handle both numerical and categorical data [42]. RF reduces overfitting by averaging multiple decision trees trained on random data subsets, providing robust predictions [43]. GBR and XGBoost build models sequentially by adding multiple weak learners, each correcting errors from the previous iteration. This approach captures complex patterns while reducing bias, as each new weak learner incrementally improves model performance. XGBoost further improves efficiency and accuracy through advanced regularization and optimization techniques [44]. The main aim of this study is to compare the performance of RF, GBR, and XGBoost on the given dataset, and to determine which of the algorithms yields the highest predictive performance for the CO\({}_{2}\) DC in brine system. The dataset was randomly split into two subsets: a training set (80%) for developing the model and a testing set (20%) for the model validation. Hyperparameter tuning was performed for each model to optimize performance. The independent testing set was used to evaluate the model's accuracy on unseen data, providing a reliable measure of its generalizable predictive capability.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 2 Algorithms of Pedestrian Detection from Images
### SSD Algorithm  
A single shot multibox detector (SSD) [23] is one of the newest state-of-the-art algorithms. The algorithm's advantage is based on its ability to detect objects by using a single neural network under various image resolutions. This is attained through the creation of feature maps for a multitude of resolutions, which are subsequently combined to provide maximum robustness. Additionally, it employs a mechanism for the accurate determination of bounding boxes. The algorithm predicts directional offsets for bounding boxes during the class prediction phase. The algorithm offers competitive accuracy and speed. However, SSD also faces several problems, which are common among many similar algorithms, i.e., small object detection. This is further amplified by the need for correcting the bounding boxes during the prediction phase. SSD is nevertheless a highly efficient and popular algorithm.  
Even though SSDs are widely used in experimental testing, the YOLO algorithm is proven to be the better solution, as confirmed by [24]. The decision to use the YOLO algorithm stems from the combined efficiency, robustness, and ease of configuring the algorithm compared to the other discussed options.  
Research on the detection of grain from UAV imagery was conducted by [PERSON], [PERSON], and [PERSON] [25]; the authors compared SSD to YOLOv4 and reported the latter to be superior. Another research [26] study evaluated SSD and faster R-CNN algorithms for human detection in aerial thermal images. The authors concluded that SSD methods are suitable for real-time applications, while faster R-CNN is better in terms of accuracy.
## 5 Results
### Methods Comparison
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Autor** & **Dataset** & **Input Data** & **Features Extracted** & **Environment Considerations** & **Limitations** \\ \hline [PERSON] et al. [13] & Doesn’t Apply & Video imagers & License plate region & 2 cameras calibration & Calibration of the cameras affect the results. \\ \hline [PERSON] et al. [41] & Does not apply & Video imagers & Limits of plate, logo and light of vehicle & Compare license plate, light, logo to matching vehicles & Needs two industrial cameras with high resolution. \\ \hline [PERSON] et al. [43] & Does not apply & Video imagers & Characters of license plate & Image rectification & If the license plate is not legible or too small it has detection problems. \\ \hline [PERSON] et al. [23] & 4 dataset & Video images & Coordinates of the license plate corners, number, and type and the color of the vehicle & Camera should have its back to the vehicles and higher height. & If the license plate is not legible or too small it has detection problems. \\ \hline [PERSON] et al. [28] & Doesn’t Apply & Video images & Pixels traveled by the vehicle & Uses homography to perspective transform & Camera calibration parameters must be manually adjusted and monitored. \\ \hline [PERSON] et al. [29] & Doesn’t Apply & Input signal and output signal & Does not extract features to avoid occlusions & The high of the camera it only makes its detections. \\ \hline [PERSON] et al. [30] & Doesn’t Apply & Video imagers & Distance & Better detections on day & Shadows affect final performance. \\ \hline [PERSON] et al. [22] & COCO dataset & Video imagers & Vehicle’s coordinates & Distance is obtained by geometric estimation. & Change in size of detections affect velocity estimation. \\ \hline [PERSON] et al. [33] & VeblepedDataset & RGB images and 532 short videos & Optical flow & Distance & Camera calibration using coordinate system & Lack of quality data. \\ \hline [PERSON] et al. [35] & 1074 sequences in freeway traffic & Video images & Vehicle Tracks, Depth and Motion & Does not have considerations & Processing time for feature extraction. \\ \hline [PERSON] et al. [36] & KITTI & Video images & Coordinates of vehicle & Does not have considerations & Use a GPU for processing \\ \hline [PERSON] et al. [37] & KITTI & Video images & Coordinates of vehicle & Does not have considerations & The detection thresholds must be manually calibrated. \\ \hline [PERSON] et al. [38] & KTH data set, KITTI & Video images & Distance on pixels & Does not have considerations & If the image moves, parked vehicles can be detected as moving. \\ \hline This work & COCO dataset and Own dataset & Video images & Distance and time & Does not have considerations & The videos must be analize manually to separate the bad samples. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Input data and Considerations between state-of-the-art works and our proposal.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 4 Conclusions
The datasets analyzed during the current study are available in the National Tibetan Plateau Data Center repository, [[http://data.tpdc.ac.cn/zh-hans/data/df40346a-0202-4](http://data.tpdc.ac.cn/zh-hans/data/df40346a-0202-4) ed2-bb07-b65 dfcda9368/]([http://data.tpdc.ac.cn/zh-hans/data/df40346a-0202-4](http://data.tpdc.ac.cn/zh-hans/data/df40346a-0202-4) ed2-bb07-b65 dfcda9368/) (accessed on 10 August 2020). The datasets analyzed during the current study are available in the National Snow and Ice Data Center (NSIDC), [[https://nsidc.org/data/SPL3](https://nsidc.org/data/SPL3) SMP/versions/7]([https://nsidc.org/data/SPL3](https://nsidc.org/data/SPL3) SMP/versions/7) (accessed on 22 December 2021). [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] 2020. SMAP L3 radiometer global daily 36 km EASE-grid soil moisture, version 7. [Indicate subset used]. Boulder, CO, USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. [[https://doi.org/10.5067/HH4](https://doi.org/10.5067/HH4) SZ2 PXPS6A]([https://doi.org/10.5067/HH4](https://doi.org/10.5067/HH4) SZ2 PXPS6A) (accessed on 23 December 2021). The datasets analyzed during the current study are available in the ECMWF Reanalysis v5--Land, [[https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5-land](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5-land)]([https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5-land](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5-land)) (accessed on 6 January 2021) [25]. The authors acknowledge [PERSON] in the Shanghai Astronomical Observatory, Chinese Academy of Sciences, and [PERSON] in Nanjing University of Information Science and Technology for their help during the data processing. The authors declare no conflict of interest.
# Swarm Intelligence Techniques for Mobile Wireless Charging
## 6 Simulation Results and Analysis
### Network Lifetime  
This is the extended lifetime of the network for different rounds at a particular time. Figure 17 shows the network operational lifetime prediction of the routing protocols. As can be seen, the IEEABR outperformed other routing protocols by showing the highest lifetime. The network is evaluated by considering the number of rounds until the first node has utilized all its energy. The purpose of this evaluation is to show the improvement and efficacy of SI-based techniques in saving energy and prolonging the network lifetime. The results show that the IEEABR achieved the longest network lifetime, followed by the BeeSensor and then by the FFR, thus making the IEEABR suitable for target tracking application scenarios. Unlike other existing schemes, SI-based techniques employing multiple MCs lead to a reduction in the transmission range of sensor-nodes, as well as their energy consumption, thereby resulting in an improved network lifetime. The figure further shows the ability of the IEEABR to support almost 50% of the additional rounds at the first stage of the network. The maximum support strength is reached as the node density increases.  
Figure 18 shows the charging time of the MCs per unit cycle with different numbers of sensor-nodes. As shown, the IEEABR outperformed the other routing protocols. In general, the result shows the efficacy of SI-based techniques for wireless mobile charging in a dense sensor network environment. The BeeSensor is observed to have the highest charging time  
among the protocols. This performance may be attributed to its reactive method of route discovery, which tries to recalculate routes each time there is a need for packet transmission to the sink node. This, invariably, creates delays and, consequently, high charging times.
# Advanced Smartphone-Based Identification of Transport Modes: Resilience under GNSS-Based Attacks
## 3 Deceiving the Ticketing System  
In this section, we explain the spoofing attacks we prepared for the trajectories depicted in Figures 2 and 3 and we present two complementary methods to detect the deceit.  
Usually, the methods of identifying transport modes that mainly rely on the GNSS position and velocity provided by the smartphone are prone to confuse transport modes that have similar velocities and move in close spaces with similar trajectories. A well-known challenging case is differentiating buses and bicycles, since the bicycle lanes are usually adjacent and parallel to the bus lanes, as shown in Figure 6. The accuracy of the GNSS position is degraded in urban scenarios due to multiple paths in urban canyons or scattering originated by the tree leaves, as shown in Figure 6. Therefore, the delivered position is in many cases not accurate enough to distinguish the bus from the bicycle lanes.  
With our recorded journey in Munich shown in Figure 2, we aim at recreating the aforementioned scenario. Therefore, we spoofed the GNSS's legitimate navigation signal to make the e-ticketing system believe the passenger did not step on a bus (yellow line in Figure 2) in Sendlinger Tor, but after leaving the tram she took the bicycle and was driving for a couple of minutes towards Viktualienmarkt over the bicycle lane parallel to the bus lane. Then, the passenger finished her journey. In this case, the bus ticket from Sendlinger Tor to Viktualienmarkt should not be included in the consolidated bill. Figure 7 shows the modified journey.  
Figure 6: Urban scenario showing the adjacent bus and bicycle lanes.  
Another challenging scenario for the methods for identifying transport modes that mainly rely on GNSS are the stations and the underground transport modes, such as the subway. In these scenarios, the GNSS reception is either highly degraded or not available. With our recorded journey in Paris shown in Figure 3, we aim at deceiving the e-ticketing system by making it believe the passenger did not commute in Montparnasse from Metro 4 to Metro 6 and continued until Trocadero, but she finished her journey in Montparnasse, went out of the station and walked until her destiny. In this case, the subway ticket corresponding to Metro 6 should not be included in the consolidated bill. Figure 8 shows the modified journey.
# Validation and Parametric Study of a Soil-Structure Interaction Nonlinear Simplified Model
## 2 Improved Penzien Model
### Calculation Process of Improved Penzien Model  
To develop a computational program for a simplified model using MATLAB, the process involves the following steps: (1) Utilizing ANSYS software to construct the free field model for seismic analysis, extracting the parameters and responses of each soil layer at every moment. (2) The shear modulus G and damping ratio D, obtained in the previous step, are employed to calculate the stiffness and damping of the equivalent soil and the pile-soil interaction stiffness and damping in the simplified model. (3) The mass and stiffness matrices of the structural system are formed by combining the mass and stiffness of the superstructure, pile, and equivalent soil. The damping of each subpart is then computed to create the damping matrix of the structural system. (4) Implement the dynamic time-history method within MATLAB to establish a nonlinear simplified computational model for soil-structure interaction. (5) Inputting selected seismic accelerations, solving motion equations, and conducting a seismic analysis of the simplified soil-structure interaction model. The calculation flowchart is shown in Figure 1.  
Figure 1: Flowchart of the MATLAB calculation process for the improved Penzien model.