esa-sceva/satcom-chunk-collection huggingface dataset:
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Device Discovery
#### 7.1.4 Discovery Messages Frequencies  
Device synchronizations and the frequencies of discoveries are considered the common challenges that need to be well-addressed [8]. The amount of discovery messages has a direct impact to the performance of D2D users. Even though there are discovery messages with prespecified designs and structures, the distribution of devices can lead to significant interference to the network for other devices. On the contrary, the information about the neighboring devices will become stale when the number of discovery messages is very low. To mitigate this issue, an appropriate scheduling scheme can be proposed to minimize the discovery message frequencies.
## 3 Challenges
### Sensing Techniques  
One of the main objectives of imbedding a CR in a wireless sensor is to utilize the unused licensed spectrum opportunistically. Here, opportunistically means the SUs should protect the accessing right of the PUs whenever necessary. The interference of SUs to PU depends on the sensing accuracy of SUs. If SUs can sense the channels with high accuracy, interference with the PU decreases. Depending on the sensing technique, there is a tradeoff between the sensing delay and sensing accuracy. The technique that takes a long sensing time has more accuracy with the cost of delays and vice versa. Basically, there are two types of sensing techniques: (a) signal processing techniques; and (b) cooperative sensing techniques. As shown in Figure 5, signal processing sensing techniques for CR-WSNs can be divided further into matched filter detection, energy detection, cyclostationary feature detection and some other techniques. Similarly, cooperative sensing can be divided further into centralized spectrum sensing, decentralized spectrum sensing and hybrid spectrum-sensing techniques.
## 2 Materials and Methods  
### PM\({}_{2.5}\) Measurement  
To analyze the mass concentration and chemical composition of atmospheric PM\({}_{2.5}\), sampling was conducted on the rooftop of a four-story building at Mokpo National University's Dorim Campus (latitude: 34.9133, longitude: 126.4373). The sampling period spanned from 13 February 2023 at 12:00 AM to 23 February 2023 at 9:00 AM. Figure 1 shows the measurement location, a rural area with small residential and agricultural areas nearby and no industrial facilities. Real-time PM\({}_{2.5}\) mass concentrations were measured using a beta-ray analyzer (Model 5014i Beta, Thermo Fisher Inc., Waltham, MA, USA) at a flow rate of 16.7 L min\({}^{-1}\).  
PM\({}_{2.5}\) samples were collected at 3 h intervals using quartz filters (Pallflex 2500 QAT-UP, Pall Corp., New York, NY, USA) and Teflon filters, with flow rates of 500 L/min and 42 L/min, respectively. A total of 160 samples were collected using both quartz and Teflon filters during the 10-day sampling period. Before sampling, the quartz filters underwent combustion pretreatment at 450 \({}^{\circ}\)C for 6 h to remove impurities and minimize the presence of carbonaceous components in blank samples. Analysis of water-soluble ionic components was conducted on samples collected from quartz filters. The filters were subjected to ultrasonic extraction with 10 mL of distilled water for 2 h. Anion (using a Metrohm 930 Switzerland, Metrosep A Supp 150/4.0 column with 3.7 mM Na\({}_{2}\)CO\({}_{3}\) & 1.0 mM NaHCO\({}_{3}\)) and cation (using a Metrohm 930 Switzerland, Metrosep C4-250/4.0 column with 5 mM HNO\({}_{3}\)) ion chromatography were employed for quantitative analysis. The collected quartz filters were punched into 1.5 cm\({}^{2}\) pieces for carbonaceous component analysis. OC and elemental carbon (EC) were quantified using a carbon analyzer (Lab-based OCEC Carbon Aerosol Analyzer, Sunset Laboratory Inc., Tigard, OR, USA)based on the Thermal-Optical Transmittance (TOT) method following the NIOSH 5040 protocol. X-ray fluorescence spectrometry (XRF, X-ray fluorescence spectrometry, QUANTX, Thermo Scientific, Waltham, MA, USA) was utilized to analyze the K component in the samples employed in this study. Levoglucosan and mannosan were used to analyze BB's influence at the measurement site. In this study, Liquid Chromatography/Quadrupole Time-of-Flight (LC/QTOF) (1290 Infinity II LC, Agilent Technologies, Santa Clara, California, USA; X500r QTOF, SCIEX, Toronto, ON, Canada) coupled with a UK-Amino (UKA26U, Imtakt Unison) column was used to quantify levoglucosan and mannosan. It was briefly used, and an analysis was performed using a mobile phase consisting of 0.1% formic acid (15%) and acetonitrile (85%) at a flow rate of 0.4 mL min\({}^{-1}\)([PERSON] and [PERSON], 2016). Ultimately, levoglucosan and mannosan ([M + HCOO]\({}^{-}\)) were separated and analyzed at 2.85 and 2.63 min retention times, respectively. For all samples, the initial calibration curve's consistency was maintained within 5% by injecting standard samples every 10 sample analyses, and precision within 3% was confirmed through duplicate analysis [22; 23; 24].
## 4 Client-Edge Pairing Based on Similarity and Heterogeneous Computing Capabilities
By assigning clients with similar model structures to the same edge server, the algorithm reduces intra-group heterogeneity and enhances the quality of local aggregations, thereby improving the stability of global convergence. Moreover, the sorting of clients by computing capability ensures that low-resource devices are only assigned lightweight update tasks, enabling timely synchronization and reducing training latency caused by stragglers. The computational complexity of Algorithm 1 mainly consists of three parts: singular value decomposition (SVD) of each client's model, pairwise similarity computation, and client grouping with sorting. We let \(K\) denote the number of clients and \(d\) the dimensionality of the model parameters. Then, the overall time complexity is \(\mathcal{O}\left(Kd^{3}+K^{2}d+K\log K\right)\), where \(\mathcal{O}\left(Kd^{3}\right)\) arises from SVD operations for \(K\) clients, \(\mathcal{O}\left(K^{2}d\right)\) comes from computing pairwise similarities, and \(\mathcal{O}\left(K\log K\right)\) results from sorting clients based on their computing capabilities. In practice, the computational cost can be reduced by employing low-rankapproximations or truncated SVD, which significantly speeds up the feature extraction step and enhances scalability for large-scale deployments.
### EU Climate Goals  
Table 4 lists the GHG emission objectives of the EU. The adoption of EVs is due to a combination of political, economic, environmental, and technological factors. Politically, stringent environmental objectives such as the Kyoto Protocol, the EU's 2020 Climate and Energy Package, the Paris Agreement, the European Green Deal, and the EU's \"Fit for 55\" Package, all include reductions in GHG emissions, promoting EV adoption [18; 19; 20]. Financial subsidies and tax breaks also make EVs more affordable, stimulating market growth in countries such as Norway, Germany, and China.  
The EU's strategic plan includes achieving a 100% zero-emission vehicle fleet in urban areas by 2050 [5].  
Beyond 2030, the disparity between the objectives and the expected impact of current and planned measures is pronounced. Considering the measures that have been adopted and those that are currently planned, net emissions are expected to be reduced by 60% compared to 1990 levels by 2040 and reduced by 64% by 2050 [21].  
Figure 4 shows the carbon intensity of electricity production in some countries from 1990 to 2030. Sweden has emerged as a hub for future big factories, with the intention of increasing the European battery production capacity and reducing the dependence on non-EU sources [22].  
Of the EU countries, as shown in Figure 4, Portugal is the only one that, in 2023, was already below the target to be achieved by 2030, corresponding to a 55% (see Table 4) reduction in the 1990 emission level. So, in this case, it makes sense to review, for Portugal, the targets proposed for GHG emissions presented in Table 4. It must be noted that the low GHG emissions in France are due to nuclear energy, due to renewable energy, namely hydraulic energy, in Norway, while in Sweden, they are due to both nuclear energy and renewable energy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Year**} & **The Target for GHG Emissions in the EU** & **Results and Forecasts (Reference Year: 1990)** \\ \hline \multirow{2}{*}{1990} & Reference year for most EU & \\  & climate objectives. & \\ \hline \multirow{2}{*}{2000} & No formal, binding EU-level target for & \\  & this year. & \\ \hline \multirow{2}{*}{2012} & Kyoto Protocol of 1997 to reduce GHG & \\  & emissions by 8% (compared to 1990 & \\  & levels) between 2008 and 2012. & \\ \hline \multirow{2}{*}{2020} & A 20\% reduction compared to 1990 & \\  & levels in 2007 under the EU 2020 & Reduction of 24\% \\  & “Climate and Energy” package. & \\ \hline \multirow{2}{*}{2030} & A 55\% reduction compared to 1990 & \\  & levels, in line with the “Fit for 55” & \\  & objective achieved in 2021. & \\ \hline \multirow{2}{*}{2050} & Net-zero emissions target. & Reduction of 65\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: GHG emission objectives in the EU from 1990 to 2050 [18; 19; 20].
## 3 Experimental and Numerical Simulation Setup
### Experimental Setups
#### 3.1.1 Flow Pattern Observation  
In order to study the flow fields above the bridge deck near the pylon zone, the tufts are used for the visualization of the flow characteristics. One end of the tuft is fixed to the steel wireline, and the other end can float freely under the crosswind. In addition, the wireline is stretched along the longitudinal direction and anchored at two different heights above the bridge deck.
## 4 Results and Discussion
### Cost, Energy, and Throughput Analysis
#### 4.5.3 Energy Consumption  
FDM consumed 0.75-0.85 kWh per part, compared to 1.0-1.1 kWh for SLA. This difference, while modest on a per-part basis, becomes significant in high-volume production.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Design of the Status-Byte-Assisted RDMA Transmission Mechanism
#### 3.2.5 Data-Receiving Module  
The data-receiving module determines whether there are available receiving memory blocks by checking the status byte stored in the third status byte memory block. If there are available receiving memory blocks, the data-receiving process is executed.  
Figure 7 shows the flowchart of the data-receiving process. Specifically, the steps of the data-receiving process are as follows:  
1. Check the status of the receiving memory blocks: The receiving thread checks the third status byte memory block to determine the status of the \(j\)-th receiving memory block. If the \(j\)-th receiving memory block is empty (status is 0), it means no data have arrived, and the thread sleeps for a fixed time before querying again;
2. Perform the data processing operation: If the \(j\)-th receiving memory block contains data (status is 1), the receiving thread performs the data processing operation. Before executing the data processing operation, the receiving thread extracts the contents of the first predetermined position in the \(j\)-th receiving memory block to obtain the device number and then extracts the contents of the second predetermined position to obtain the packet number;
3. Update the status byte: After completing the data processing operation, the receiving thread updates the status byte in the third status byte memory block to indicate that the \(j\)-th receiving memory block is empty.  
Figure 6: Flowchart of the data-sending process.  
Figure 7: Flowchart of the data-receiving process.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 3 Experiments of Estimating Significant Wave Height from SAR with Long Integration Times  
In order to verify the effectiveness of the proposed method, experiments of estimating significant wave height from SAR with long integration times will be carried out in this section. Two representative data are selected. One is the case where the waves are invisible, which cannot be directly used to estimate significant wave height by current methods. The other is the case where there are two ocean wave systems in the real ocean surface. However, only a weak ocean wave system can be observed in the SAR image. If no processing is performed, only an ocean wave system can be achieved estimating significant wave height. The experimental results of these two cases are given below.
# Enhanced Feature Selection via Hierarchical Concept Modeling
## 5 Results and Discussion
### Comparison of Performances
The flexibility in adapting this model to other classifiers makes it a versatile approach across different machine learning tasks. Figure 11: Comparison of proposed feature selection using DT on each level and others with DT-SVM-ANN classifiers. This hierarchical feature selection model is particularly beneficial in applications requiring efficient and accurate feature reduction. For example, in medical diagnosis systems, where high-dimensional data (e.g., from genetic or imaging data) can impact computational efficiency, this model enables the reduction of features without sacrificing diagnostic accuracy. Another practical use case is in financial analysis for credit scoring, where a subset of relevant features can streamline decision-making processes. Additionally, environmental monitoring systems, where IoT data often contains redundant attributes, can benefit from this model's ability to filter and retain only the most informative features. However, one interesting limitation is class imbalance. Class imbalance can impact the performance of feature selection methods, as features correlated with the minority class may be underrepresented or overlooked during selection. To mitigate this, we employed re-sampling based on using cross-validation metrics to ensure balanced representation across classes. However, this is still a limitation of this study. Our analysis indicates that while the hierarchical feature selection method performs robustly in balanced datasets, its effectiveness in imbalanced datasets improves with re-sampling techniques. Future work could explore integrating class-sensitive feature scoring directly into the hierarchical selection framework, ensuring equitable consideration of features across all classes. Moreover, we plan to address key challenges, such as multicollinearity and class imbalance, through established techniques like variance inflation factor (VIF) analysis, SMOTE for balancing classes, and comparison with advanced feature selection methods. Future studies will involve comprehensive testing on both benchmark and synthetic datasets to validate the efficacy of the hierarchical framework. Figure 12: Comparison of proposed feature selection using FCA in each level to others on using DT-SVM-ANN classifiers.
## 2 Results and Discussion
### pH and Thermal Stability of Immobilized Lacase  
The optimal pH of a lactose immobilization reaction system varies according to the charge in the enzymatic protein and the support used [43]. As shown in Figure 4a, and consistent with previous studies, free lactose showed maximum activity at a pH of 2.5 and rapidly decreased activity as the pH increased, and it was almost completely inactivated under neutral conditions [44]. In general, anionic supports attract protons from a given solution, which results in a higher concentration of H\({}^{+}\) in the diffusion layer of an immobilized enzyme than in the external solution. This is why the pH of the external solution should be higher [45]. In the present study, the amino-functionalized PILs were positively charged (+1.7-+5.8 eV); however, the optimal pH value of the immobilized lactose still was higher than that of free lactose. This can be attributed to the imidazolium skeleton in the PILs being positively charged, and the fact that part of the OH\({}^{-}\) was enriched in the solution. Therefore, there was more available H\({}^{+}\) near the lacase at the other end of the cross-linking arm. Further, this is why the optimum pH of the PIL (2)-NH\({}_{2}\) -GA-Lac system was increased slightly. Additionally, the activity-pH curve of most of the immobilized enzymes was bell-shaped, and compared with that of the free lactose, the bell-shaped curve of the immobilized lactose, especially PIL (2)-NH\({}_{2}\), was flatter. Even in the buffer solution of pH 7, PIL (2)-NH\({}_{2}\) was able to retain more than 45% activity.  
Figure 4: Effect of (**a**) pH and (**b**) temperature on the activity of free lactose and PIL–NH\({}_{2}\)–GA–Lac. (**c**) Storage stability of free and immobilized lactose and (**d**) reusability of the immobilized lactose. Conditions: ABTS (100 mM), PIL–NH\({}_{2}\)–GA–Lac (10 mg/mL), pH = 3 (**b–d**), and 25 \({}^{\circ}\)C (**a,c,d**).  
Figure 4b shows that the relative activities of both the free and immobilized increase were strongly temperature-dependent. The thermal stability of PIL-NH\({}_{2}\)-GA-Lac was improved, and its activity decreased more slowly than that of free lactose. In particular, PIL (2)-NH\({}_{2}\)-GA-Lac maintained more than 70% activity at 60 \({}^{\circ}\)C. This may be attributed to the PIL supports' providing covalently attached sites and confined space for the lactose, as well as the inactive active center's being preserved by its binding with the ligand, resulting in the decreased formation of the thermo-unfolded enzyme. Therefore, higher temperatures (above 40 \({}^{\circ}\)C) were required to break down the intermolecular interactions and to inactivate the enzyme [46]. In addition, some reports have proved that PILs have a good heat insulation effect, so when the temperature of the external solution rises, the surface and internal temperature of the support will be relatively low, which protects the lactose from being inactivated by high temperatures [47].
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### Unmanned Traffic Management (UTM)  
It is envisioned that urban airspace will be congested with several types of autonomous aerial vehicles, consequently leading to complex air traffic management. Existing human centric, monopolistic and centralized air traffic control systems are not feasible to cope with UAVs congestion and cannot provide immediate legal solutions for autonomous aerial missions [65]. The aforementioned elements became the rationale for dedicated UTM systems to strategically de-conflict multiple UAV flights in the shared and segregated proximity. UTM systems are fully autonomous, where controllers can decide trajectories, traffic density and risk calculations. UTM has proven its stature in a wide variety of higher-tier applications including surveillance, communication, agriculture, aerial mapping and remote sensing. It has the capability to operate in both LoS and beyond the visual LoS (BVLoS) links. The existing UTM systems operate on very-low-level (VLL) airspace along with offering BVLoS links. Most UTM systems have similar concepts of operations and objectives. Various UTM systems are based on federated, distributed and service-oriented architecture [66]. Three levels of connectivity based on ground stations, airborne vehicles and satellites are required for communication in UTM systems. UTM systems offer following potentials features:  
* Safe and secure UAV missions,
* Flexible operations for several types of UAVs,
* Provides real-time monitoring based on integrated sensors,
* High level control providing prediction of other piloted UAVs,
* A key unifying element of reliance on a more automated and competitive system.
* Operates on national and international standardizations.
## 3 Theoretical Analysis
### Obtaining Path-Based Timing Constraints  
The CAPCOM method considers the cell delay without considering the path and uses the average delay of the cell as a parameter. However, the specific impact of the cell on a path needs to be considered. For example, in Figure 4, the delays of timing arc \(A\)-\(to\)-\(O\) and timing arc \(B\)-\(to\)-\(O\) are different. A significant difference exists between the cell delay on the path through pin \(A\) and the path through pin \(B\).  
On the other hand, CAPCOM only distinguishes paths into critical paths and non-critical paths. The method in this paper can obtain more accurate assignment results from distributing threshold voltages against path constraints. The path constraint is given by  
\[\sum_{j\in CIP_{i}}(\alpha_{j}*d_{C_{(i,j)}}+d_{N_{(i,j)}})+t_{s_{i}}\leq t_{r_ {i}} \tag{2}\]  
where \(i\) refers to a particular path, \(CIP_{i}\) is the set of cells in path \(i\), \(\alpha_{j}\) is the timing derate of the cell \(j\), \(d_{C_{(i,j)}}\) is cell \(j\)'s delay in the path \(i\), \(d_{N_{(i,j)}}\) is the net delay which connected to cell \(j\)'s output pin in path \(i\), \(t_{s}\) is the library setup time, \(t_{r}\) is the required time.
## 2 Equivalent Range Model for GEO-LEO BiSAR
### Equivalent Range Model for GEO-LEO BiSAR  
Inspecting (10), the bistatic range \(R_{bi}(t)\) is the sum of a fourth-order polynomial and a square-root term, which is difficult to obtain an explicit expression of the 2-D spectrum using principle of stationary phase. Therefore, in this section, an equivalent bistatic range model based on CoT-ESRM (BiCoT-ESRM) is proposed to accurately represent the range history of GEO-LEO BiSAR in sliding-spotlight mode, which is given by  
\[R_{bi}^{Eq}(t) =R_{T08}+\left(k_{T4s}+\Delta k_{err}^{4}\right)t^{4}\] \[+\sqrt{R_{R0}^{2}+\left(2\sin\frac{\omega_{e}t}{2}R_{e}\right)^{ 2}-4R_{R0}\sin\frac{\omega_{e}t}{2}R_{e}\sin\theta_{e}} \tag{11}\]  
where \(\omega_{e}\), \(R_{e}\) and \(\theta_{e}\) are the bistatic equivalent parameters, which can be obtained by letting the first-, second- and third-order expanding coefficients of (11) equal to those of (10). The detailed derivations of \(\omega_{e}\), \(R_{e}\) and \(\theta_{e}\) are given in the Appendix A.2. \(\Delta k_{err}^{4}\) is the coefficient of a fourth-order term to compensate for the residual fourth-order range error, which can be calculated by the difference between the fourth-order expanding coefficients of the square-root term of (10) and that of (11). The bistatic range error introduced by BiCoT-ESRM for GEO-LEO BiSAR within 18 s azimuth time is illustrated in Figures 3 and 4, where two state-of-the-art range models for GEO bistatic SAR are investigated for comparison, including the NSGRM in [32] and RMOSE in [24]. The simulation parameters are given in Table 1. Please note that the original RMOSE does not take into account the'stop-and-go' assumption error, which will bring significant error. In this comparison, we implement a modified version of RMOSE by deriving the 'non-stop-and-go' delay of RMOSE based on the method in this paper. It can be observed from Figure 4 that the maximum bistatic range error of BiCoT-ESRM is approximately \(8\times 10^{-4}\) m and the corresponding phase error is far less than \(\pi/4\) within 18 s synthetic aperture time, which can be ignored in sliding-spotlight GEO-LEO BiSAR. In contrast, the range errors of NSGRM and RMOSE are much larger than one range cell, which will bring significant range and phase errors during imaging processing. Please note that the bistatic range delay at beam center crossing time \(\tau_{d}(0)\) is dependent on the expanding coefficients of the receiver range model, i.e., \(a_{1}\) and \(a_{2}\). Therefore, the range error of 'non-stop-and-go' RMOSE in Figure 3 is not zero due to the different range model adopted for LEO receiver. The main advantages of BiCoT-ESRM compared with the competing range models are three-fold. First, the fourth-order polynomial is used to model the range history of GEO transmitter, which is more accurate than the second-order polynomial employed by NSGRM and RMOSE, especially in sliding-spotlight mode. Second, the CoT-ESRM can accurately represent the curved trajectory of the LEO receiver within long synthetic aperture