esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 6 Conclusions  
In general, UAV routing is a novel topic. There has been a very limited number of research contributions published on UAV routing compared to the VRP and vessel routing. This paper identifies different VRP approaches to solve UAV routing, and most of them consist of variants of VRP and TSP. This paper provides a detailed overview of UAV routing literature with a general mathematical formulation for the UAVRP and presents a UAV routing taxonomy covering four areas of VRP-based UAV routing, TSP-based UAV routing, UAV scheduling problems, and UAV routing in wireless networks. The four areas of taxonomy are done considering the common attributes from the literature and the proposed taxonomy is applied to 54 recent papers.  
In majority of the literature on UAV routing the kinematics are neglected and the UAVs' flight dynamics are neglected or simplified using assumptions. It is worth noting that a UAV's performance is highly influenced by the payload it is carrying (weight and dimensions) and the weather conditions in which it is operating. Both significantly influence the fuel consumption of the UAV and, thus, they must be included when modeling the UAVRP. Such models will lead to computationally expensive formulations with more realistic solutions. There is seldom research done for heterogeneous fleet of UAVs due to the complexity and the majority of existing literature focuses on homogeneous fleet of UAVs. This problem should be further derived by the non-linearity of the fuel consumption model, and it is also worth noting that the existing literature does give importance to stochastic conditions. Thus, modeling energy consumption must be further investigated by giving more focus to the literature. These models and their integration in the UAVRP seem like particularly relevant avenues of future research and development of efficient frameworks for solving UAVRPs addressing the challenges presented in this study need to be answered.  
Conceptualization, A.T., G.B, Z.B., and P.N.; methodology, A.T and G.B.; validation, P.N., G.B., and Z.B.; formal analysis, A.T. and P.N.; data curation, A.T., G.B, and P.N.; writing--original draft preparation, A.T.; writing--review and editing, A.T, Z.B., and G.B.; supervision, G.B. and P.N.; project administration, A.T.; funding acquisition, P.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors declare no conflict of interest.
# Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances
## 4 Results
### Performance and Comparative Analysis  
In this subsection, the advantages of the proposal of this paper, the D1-NN classifier, are experimentally shown. It is necessary to carry out a systematic comparison of the performance of the new classifier with the classifiers of the state of the art. For this purpose, eight datasets related to various aspects inherent to personal finance have been selected. The results obtained by the D1-NN are compared against some of the most important classifiers of the state of the art. The accuracy is computed as in Equation (3). The results are shown in Table 3.  
The results obtained are very promising and are discussed in the next section.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Conceptual Basis** \\ \hline Naive Bayes & Bayesian theory \\ kNN & Instance-based \\ Logistic & Statistic-based \\ MLP & Artificial Neural Networks \\ SVM & Finding a kernel-based hyperplane \\ AdaBoost & Ensemble of classifiers \\ \hline \end{tabular}
\end{table}
Table 2: Algorithms against which the D1-NN classifier will be compared.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Datasets** & **Naïve Bayes** & **Logistic** & **kNN** & **MLP** & **SVM** & **AdaBoost** & **D1-NN** \\ \hline Australian credit approval & 77.10 & **86.52** & 80.72 & 84.34 & 55.50 & 86.08 & 82.89 \\ Bank & 76.70 & 80.08 & 85.58 & 85.33 & 60.34 & 83.98 & **86.56** \\ Bank additional & 76.80 & 85.76 & 86.25 & 88.01 & 76.63 & 86.58 & **91.38** \\ Bankote authentication & 75.79 & **99.12** & 80.57 & 84.49 & 55.36 & 84.63 & 83.18 \\ Credit approval & 83.89 & 86.52 & 99.85 & **100.00** & **100.00** & 94.09 & 99.92 \\ German credit data & 71.78 & **77.00** & 73.58 & 71.61 & 63.37 & 75.55 & 71.69 \\ Iranian credit & 50.72 & 75.93 & 91.34 & 88.37 & 61.59 & 85.59 & **95.13** \\ Qualitative bankruptcy & 98.00 & 99.20 & **99.60** & 99.20 & **99.60** & **99.60** & **99.60** \\
**Average Accuracy** & 76.35 & 86.26 & 87.19 & 87.67 & 71.55 & 87.01 & **88.79** \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy values (in %) obtained by the compared classifiers (best values in bold).
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 4 Research Motivation  
Due to the non-uniformity of user load data, a single global model is insufficient to meet personalized user requirements. This paper considers using clustering algorithms to aggregate clients with similar data distributions, group electricity users into different clusters, and learn personalized models for each cluster. Providing suitable differentiatedmodels for each user can effectively prevent client drift, thereby enhancing prediction accuracy and model specialization.  
Federated clustering can be divided into one-time clustering and iterative clustering based on timing. Ref. [25] adopted a one-time clustering method, which requires specifying the number of clusters \(K\) in advance. IFCA uses iterative clustering without the need to predefine \(K\); however, it employs a centralized clustering algorithm, resulting in a significant increase in computational overhead at the central server. CFL also adopts iterative clustering, requiring substantial computational resources to confirm the cluster identities of clients.  
To address the issues present in the aforementioned federated clustering methods, this paper constructs a complex network and considers the modularity function. Based on the classical Louvain algorithm for community detection in complex networks, we propose a new federated clustering method that can effectively protect user data privacy, does not require pre-specifying the number of clusters \(K\), and achieves a well-performing differentially private federated clustering model with lower computational cost.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 5 Future Directions
### Kernel Sizes  
The development of compositionality has lead to a theory from which lower bounds on the size of the smallest possible kernel of a problem can be derived (under reasonable complexity assumptions). The spectrum (cf. [6]) here reaches from polynomial-sized kernels (e.g., for any \(q\geq 3\) and \(\varepsilon>0\) the \(q\)-SAT problem parameterized by the number of variables \(n\) has no \(O(n^{q-\varepsilon})\)-sized kernel) to exponential-sized kernels (e.g., the Steiner Tree problem parameterized by the number of terminals does not admit any polynomial-sized kernel despite being FPT).  
For approximate kernels, only a small number of publications exist, and the few known results fall into two categories:  
* A polynomial-sized approximate kernelization scheme (PSAKS) exists, i.e., for any \(\varepsilon>0\) there is a \((1+\varepsilon)\)-approximate kernelization algorithm that computes a \((1+\varepsilon)\)-approximate kernel of size polynomial in the parameter \(k\). For example, the Steiner Tree problem admits a PSAKS for both the parameterization in the number of terminals [18] and in the number of Steiner vertices in the optimum [202], even though neither of these two parameters admits a polynomial-sized (exact) kernel.
* A lower bound excluding any approximation factor for polynomial-sized kernels exists. For example, the Longest Path problem parameterized by the maximum path length has no \(\alpha\)-approximate polynomial-sized kernel for any \(\alpha\)[18], despite being FPT for this parameter [6].  
Hence again the intermediate cases, for which tight constant or logarithmic approximation factors can be proved for polynomial-sized kernels, are missing. Studying approximate kernelization algorithms however is of undeniable importance to the field of parameterized approximation algorithms, as witnessed by the importance of exact kernelization to fixed-parameter tractability.
## 3 Results and Discussion
### Selection of Adsorption Solvent  
The choice of solvent is important for the template-MIP interactions. Solvents of different polarities were investigated, as shown in Figure 3. In all solvents--i.e., chloroform, acetonitrile, and pH 2.5 and pH 9.0 of 0.1M sodium phosphate buffer solutions--IBA showed better affinity on the MIPs than on the NIPs. Nonpolar or acidic buffer solvents were observed to be better for IBA adsorption on the polymers. This result once again indicates that hydrogen bonding is the dominant interaction in the specific binding of the MIP system herein described. The highest adsorption (increase in the fluorescence intensity) was observed in a buffer of pH 2.5. In addition, the binding difference of IBA between the MIPs and NIPs was the most pronounced in this solvent, followed by the binding difference in chloroform. Because adsorption capacity is a very important factor in surface imprinting, the buffer solution is also favored for chip coating in sample analysis.  
Figure 2: Chip fluorescence intensity for the different monomers used in the polymer coatings (5 mg\(\cdot\)L\({}^{-1}\) IBA solution in chloroform, \(\lambda\)ex/em = 294/370 nm).
## 3 Model Verification
### Data Trends of Steam-CO\({}_{2}\) Mixture Condensation  
At a total pressure of 1 atm, Figure 4a shows the variation trend in the \(h_{x}\) of the steam-CO\({}_{2}\) mixture condensation along the horizontal plate under different flow rates and inlet CO\({}_{2}\) percentages, at a subcooling rate of 7 K. Figure 4b shows the accumulation of liquid film on the surface of a horizontal plate under different flow rate conditions and \(\alpha_{\infty}=5\%\). The data results show that the liquid film builds and the \(h_{x}\) drops sharply at the leading edge of the horizontal plate. However, for the downstream function, both the film thickness and \(h_{x}\) curves become flattened along the plate. The CO\({}_{2}\) percentage is a significant factor influencing the heat transfer coefficient. While there is a distinguishable effect of mixed gas velocity (in the calculation range) on \(\delta\), an increase in the velocity can reduce the accumulation of condensation liquid, thus reducing the thickness of the liquid film. With a decrease in the CO\({}_{2}\) percentage and an increase in the gas velocity, the heat transfer coefficient on the surface of the horizontal plate is enhanced. The simulation data trend of condensation on the horizontal plate can be expected.  
Figure 4: Data trends of steam–CO\({}_{2}\) condensation on the flat plate. (**a**) Variation in \(h_{x}\) along the horizontal plate; (**b**) Variation in \(\delta\) along the horizontal plate; (**c**) Condensation film thickness and gas–liquid interface velocity along the vertical plate.  
Figure 3: Comparison between the boundary layer model data and data from [PERSON] et al. [27] study (\(P=101.325\) kPa, \(\alpha_{\infty}=34.3\%\), \(T_{\infty}=367.74\) K).  
Figure 4c shows the variation trend of the liquid film thickness and flow velocity of the gas-liquid interface along the vertical plate with different inlet and infinity CO2 percentages. The curve trends in Figure 4 are consistent with the existing study of the mixed gas condensation, further indicating that it is better to use the boundary layer model to analyze the features of the steam-CO2 mixture condensation on the horizontal and vertical plate surface.
## 4 Rockburst Risk Level Classification Model
### Rockburst Risk Level Classification Model Optimization  
The parameters of the primary RF model are optimized and adjusted. Different values of essential parameters of the model affect the results of rockburst risk level prediction. To improve the accuracy of the predictive classification of the model, particle swarm optimization (PSO) [32; 33] is used to tune the number of trees (n_estimators) and the maximum depth of the tree (max_depth) among the parameters of the RF model. The PSO algorithm treats each particle in the generated initial population as a feasible solution. The direction and distance of the particle motion during the population motion are determined according to its velocity. Under the condition that the motion of each particle closely follows the current optimal solution, the optimal solution is obtained from the ordered spatially feasible solution by continuously updating its information, as in Figure 10.  
Figure 10: PSO optimization algorithm flow.  
Figure 9: Comparison of three classification model indicators.  
The PSO optimization algorithm has the advantages of easy implementation, fast convergence, and high accuracy, so it is attracting increasing attention and has produced significant results in solving practical optimization problems. In the tuning process, the number of particles in the particle swarm is set to 100, the maximum number of iterations is 30, the inertia factor is set to 0.8, and the mean value of the six\(-\)fold cross\(-\)validation training accuracy is used as the final evaluation metric. The iterative tuning of the RF model parameters is shown in Figure 11.  
After parameter tuning, the accuracy of the RF model for rockburst risk level prediction is increased by 1.62%, and the predicted rockburst risk level at time t + 1 is shown in Table 5. The rockburst risk level at t + 1 is none, which can be used as a basis for judgment and provide a reference for coal mine safety production.
## 4 Joint Preprocessing Techniques for MU-Network MIMO  
As mentioned previously, the serious multi-user interference is inevitable in MU-network MIMO. To control this, joint preprocessing among cooperative BSs is necessary. With per-sector antenna power constraint, we derive various joint preprocessing schemes for OFDMA-based MU-network MIMO. They include MU joint time-division multiple access (MU-JTDMA), MU joint zero-forcing (MU-JZF), and MU joint minimum mean-square error (MU-JMMSE). Both MU-JZF and MU-JJMMSE are superposition-coding schemes and MU-JTDMA is an orthogonal one.  
Recall that the signal vector sent to CEU \(u\) from all 3 coordinated BSs is the \(3\Big{|}\Im^{(u)}\Big{|}N_{t}\times 1\) aggregate signal vector \(\mathbf{X}^{(u)}\equiv\Big{[}\mathbf{X}^{(0,u)^{T}},\mathbf{X}^{(1,u)^{T}}, \mathbf{X}^{(2,u)^{T}}\Big{]}^{T}\). Accordingly, the preprocessing matrix \(\mathbf{F}^{(u)}\) in (11) can be decomposed as  
\[\mathbf{F}^{(u)}\equiv\Big{[}\mathbf{F}^{(0,u)^{T}},\mathbf{F}^{(1,u)^{T}}, \mathbf{F}^{(2,u)^{T}}\Big{]}^{T}\in\mathbb{C}^{3|\Im^{(u)}|N_{T}\times K^{(u)}}, \tag{17}\]  
where BS \(b\) uses sub-matrix \(\mathbf{F}^{(p,u)}\in\mathbb{C}^{|\Im^{(u)}|N_{T}\times K^{(u)}}\) to pre-code data vector \(\mathbf{d}^{(u)}\), i.e., \(\mathbf{X}^{(b,u)}=\mathbf{F}^{(b,u)}\mathbf{d}^{(u)}\). Then the total power required by BS \(b\) to serve all the \(N_{CEU}\) CEUs is given by  
\[T^{(b)}=\mathrm{E}\Big{\{}\sum_{u=0}^{N_{CEU}-1}\text{tr}\Big{[}\mathbf{X}^{(b,u)}\mathbf{X}^{(b,u)^{H}}\Big{]}\Big{\}}=\sum_{u=0}^{N_{CEU}-1}\text{tr} \Big{[}\mathbf{F}^{(b,u)}\mathbf{R}^{(u)}\mathbf{F}^{(b,u)^{H}}\Big{]}, \tag{18}\]  
where \(\mathrm{E}[\cdot]\) is the statistical expectation operator, \(\text{tr}[\cdot]\) denotes the trace of a square matrix, the superscript \(H\) denotes Hermitian transpose and \(\mathbf{R}^{(u)}\equiv\mathrm{E}\Big{[}\mathbf{d}^{(u)}\mathbf{d}^{(u)^{H}} \Big{]}\) is the covariance matrix of \(\mathbf{d}^{(u)}\) with zero-mean uncorrelated data symbols.
## 4 The Energy Balance Problem
### Further research on energy balance  
In [59] the authors propose a generalized data propagation algorithm, which allows to jump over bottleneck nodes. They investigate whether hops to intermediate slices help. They consider mixed propagation (i.e. only single hops and direct to sink transmissions) and interestingly show that mixed strategies beat every other possible strategy (wrt optimizing the network lifespan). Indeed, they prove that lifespan is maximized by a mixed data propagation algorithm. They then use this fact to propose am optimal distributed data propagation algorithm. Their method uses a LP maximizing the generalized flow, subject to certain constraints (detection rates, energy and flow constraints). They rigorously prove that if there exists an energy-balanced mixed flow it maximizes the mixed-flow and it also maximizes the generalized-flow. Thus, they can maximize the generalized-flow problem using a mixed-flow and a local property (energy-balance). Their algorithm produces a mixed flow and at the same time balances energy, while its stability is shown using Markov Chain methods.  
In [58] the authors consider that nodes can send messages directly to the sink. They propose an algorithm to compute the optimal solution (in terms of lifetime), i.e. to compute the ejection probabilities. They interestingly show that when there exists an energy balanced solution, it is optimal. When it is not possible to balance energy, the algorithm still finds an optimal solution. They give a necessary and sufficient condition for a solution to be optimal.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 3 Underwater SLAM: Background and Overview
### Importance of Underwater Wireless Sensor Networks (UWSNs)  
UWSNs have emerged as a critical enabler for advanced underwater SLAM systems. UWSNs facilitate distributed sensing, communication, and data sharing among multiple AUVs and static sensor nodes, enhancing the robustness and scalability of SLAM in large or complex environments [5]. By leveraging UWSNs, SLAM systems can overcome limitations such as limited sensor range and communication constraints, enabling cooperative SLAM where multiple agents collaboratively build and update maps [41].  
UWSNs also support the integration of heterogeneous sensor data, such as acoustic, optical, and inertial measurements, improving the accuracy and reliability of SLAM in challenging conditions [42]. Furthermore, UWSNs enable real-time data exchange and processing, which is essential for dynamic environments where rapid adaptation is required [5].  
Given the growing importance of UWSNs in underwater SLAM, we propose a new classification framework that categorizes SLAM techniques based on their communication and networking considerations. This framework highlights the role of UWSNs in advancing underwater SLAM and provides a foundation for future research in this area.
## 2 Materials and Methods
### Experiment Design
#### 2.2.2 Spray-Deposition Measurements
After vibration and elution, a sample portion of the wash effluent was filtered through a 0.22 um membrane and the filtered wash effluent was poured into a cuvette to measure the absorbance value by a UV2100 ultraviolet and visible spectrophotometer (LabTech, Co. Ltd., Beijing, China) at an absorption wavelength of 514 nm. Spray deposits were quantified by comparison with similarly determined dye concentrations from spray tank samples and the area of the respective samples. The data were expressed as a quantity of dye (ug) deposited per unit area of the sample (cm\({}^{2}\)) or quantity of dye (ug) deposited per plant. Note that throughout the text whenever the term \"deposition\" is used, it refers to the mass of dye (which would correspond to amount of active ingredient), not the mass of total spray mix, deposited on specified sampling surface. The distribution uniformity of the spray deposition in the canopy was analyzed using the value corresponding to the coefficient of variation (CV), calculated as the quotient between the standard deviation and the average of the spray deposits on the crop. In addition, for each application, the recovery rate was calculated using Equation (1):  
\[\text{R}=(\text{D}\times\text{P}/\text{A})\times 10^{6} \tag{1}\]  
where R is the recovery rate (%), D is the average deposited tracer solution per plant (ug/plant), P is the plant density (4.1 \(\times\) 10\({}^{6}\) plant/ha), A is the additive amount of allure red (450 g/ha), and 10\({}^{6}\) is the unit conversion factor. In the laboratory, the WSPs were scanned at a resolution of 600 dpi with a scanner. After that, imagery software DepositScan [23] (USDA, USA) was utilized to extract droplet deposits in the digital image and analyzed the droplet size, number of spray deposits and the area of coverage. The Volume Median Diameter (VMD) is a key index for reflecting the droplet size, which was used in this study. The climatic conditions were recorded using a Kestrel 5500 digital meteorograph (Loftopia, LLC, USA), which recorded temperatures of 18.3-22.4 \({}^{\circ}\)C, a relative humidity of 46.3-53.7% and wind velocities of 3.6-10.8 km/h.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 8 Applications & Potential Alternatives
### Applications
#### 8.1.1 OPAs for Free-Space Communications  
Integrated OPAs provide non-mechanical beam steering. It is attractive to free-space communications for chip-scale size, light weight, and low power consumption. It is a promising option for free-space satellite communications with constrained size, weight, and power [91]. Compared with transmission over optical fibers, OPA-based free-space transmissions do not degrade the quality of tens of Gb/s signals [51].  
A hybrid PIC composed of a SiO\({}_{2}\) planar lightwave circuit together with a 3D waveguide PIC was demonstrated for 15 orbital angular momentum (OAM) states [47]. A 20 Gb/s quadrature phase shift keyed signal for free-space space-division-multiplexing coherent optical transmission link was built. The capability for error-free transmission at an optical signal-to-noise ratio (OSNR) \(>\) 14 dB was shown for individual OAM beam multiplexing/demultiplexing. For two OAM states, an OSNR of \(>\)18 dB was achieved by the hybrid PIC [47]. An integrated OPA is a compact and low-cost solution to realize indoor optical wireless communication with limited mobility. A Si-based EF OPA was experimentally demonstrated in 12.5 Gb/s data transmission over 1.4 m free space distance [49]. A static 50 m lens-free point-to-point optical communication link at a data rate of 10 Gb/s was shown by two passive OPAs. A similar structure composed of an active OPA transmitter and multiple OPA receivers placed 1 m apart enabled a point-to-multipoint communication link at a 1 Gb/s data rate [11]. Another OPA consisting of 64-element EO p-i-n phase shifters and TO tunable n-i-n grating radiators was used for error-free transmission. The OPA with an FOV of 46.0\({}^{\circ}\)\(\times\) 10.2\({}^{\circ}\) and a beamwidth of 0.7\({}^{\circ}\)\(\times\) 0.9\({}^{\circ}\) was built for a 32 Gb/s data rate over 3 m [51].