esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Cases Analysis
### Analysis of the Algorithm Parameters  
Given the paramount influence of parameters on algorithm performance, it is necessary to calibrate the parameters for each algorithm. For the proposed IMOAHLO algorithm, the parameters to be calibrated are the random learning probability (\(pr\)), the probability of individual learning (\(pi\)), the random learning decay rate (\(k_{pr}\)), and the individual learning decay rate (\(k_{pi}\)). The four parameters have the following levels: \(pr\) (0.4, 0.6, 0.8, 1.0), \(pi(0.8,1.0)\), \(k_{pr}\)(0.6, 0.8, 1.0), and \(k_{pi}\)(0.4, 0.6, 0.8). Orthogonal experiments were conducted to select the optimal parameter combination. Table 1 presents the orthogonal experiment parameter groups and the results.  
To determine the optimal parameter combination, experiments were conducted using hypervolume (HV) as the evaluation metric. HV represents the volume of the obtained Pareto front. The calculation formula is shown in Equation (24):  
\[HV(P)=\delta\Big{(}\bigcup_{i=1}^{N}v_{i}\Big{)} \tag{24}\]  
where \(v_{i}\) represents the hypervolume formed by the \(i\)-th solution in the given solution set \(P=\{x_{1},x_{2},x_{3},\ldots,x_{N}\}\) and the reference point. The larger the HV value, the better the overall performance of the algorithm. Due to the difficulty of calculating HV, this paper approximates the volume of the Pareto front obtained by the algorithm through Monte Carlo simulation. Each set of parameters is run 20 times under 20 cases. The test results for different parameter groups are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & \(pr\) & \(pi\) & \(k_{pr}\) & \(k_{pi}\) & **Mean HV** \\ \hline
1 & 0.4 & 0.8 & 0.6 & 0.4 & 0.393747 \\
2 & 0.4 & 1.0 & 0.8 & 0.6 & 0.393249 \\
3 & 0.4 & 0.8 & 1.0 & 0.8 & 0.394111 \\
4 & 0.4 & 1.0 & 0.6 & 0.4 & 0.392992 \\
5 & 0.6 & 0.8 & 0.8 & 0.8 & 0.398204 \\
6 & 0.6 & 1.0 & 0.6 & 0.4 & 0.398448 \\
7 & 0.6 & 0.8 & 0.6 & 0.4 & 0.403563 \\
8 & 0.6 & 1.0 & 1.0 & 0.6 & 0.397383 \\
9 & 0.8 & 0.8 & 1.0 & 0.4 & 0.403339 \\
10 & 0.8 & 1.0 & 0.6 & 0.8 & 0.407305 \\
11 & 0.8 & 0.8 & 0.6 & 0.6 & 0.405940 \\
12 & 0.8 & 1.0 & 0.8 & 0.4 & 0.404230 \\
13 & 1.0 & 0.8 & 0.6 & 0.6 & 0.405762 \\
1 It can be seen that the optimal parameter combination for the proposed algorithm is group 10 (\(pr\) = 0.8, \(pi\) = 1.0, \(k_{pr}\) = 0.6, and \(k_{pr}\) = 0.8). Under these parameters, the algorithm's performance is the best, with an average HV value of 0.407305. The same experimental method was used to determine the parameters of the other three algorithms. Table 2 summarizes the optimal parameters after calibration for all algorithms.
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 4 Design of Trajectory Tracking Control Strategy Considering Load Transfer  
### Overall Control Strategy Design  
Aiming at the phenomenon of load transfer caused by turning driving, pavement slope, acceleration and deceleration, or pits and deceleration zones on the pavement during the operation of heavy-duty AGV, this paper proposes a heavy-duty AGV trajectory tracking control strategy considering load transfer. The lateral load transfer control adopts an IMPC controller considering lateral acceleration optimization, and the longitudinal load transfer control adopts a driving torque proportional distribution controller. The structure of the control system is shown in Figure 7.  
The control system shown in Figure 7 consists of three parts:  
1. An MPC controller for optimizing lateral acceleration based on the kinematic model. The improved MPC controller receives the state reference quantities (\(x_{r},y_{r},\theta_{r}\)) output by the reference trajectory, the control reference quantities (\(v_{r}\), \(\delta_{fr}\), \(\delta_{rr}\)), and the actual pose (\(x,y\), \(\theta\)) of the AGV. A cost function that minimizes both the lateral acceleration term and the tracking error term is designed, along with constraints on the maximum and minimum values of the control variables and their increments, as well as the equality constraint in Equation (9). A prediction model is established to output the control variables and perform feedback correction so as to achieve AGV trajectory tracking while reducing the lateral load transfer;
2. A velocity controller for calculating the total driving torque. The PID velocity controller receives the desired velocity \(v_{0}\) output by the MPC controller and the actual velocity \(v\) of the AGV, thereby computing and outputting the total driving torque \(T_{d}\) of the AGV;
3. A torque distribution controller for allocating the total driving torque to each motor and transferring it to the vehicle model. After receiving the total driving torque calculated by the PID velocity controller, the torque distribution controller allocates the total driving torque into the input torques \(T_{df}\) and \(T_{dr}\) of the front and rear drive  
Figure 7: The structure diagram of the control system.  
motors according to the vertical load proportion allocation method, thereby driving the AGV movement.
## 4 Results and Discussion
### Research Limitations
#### 4.3.1 Data Collection and Processing  
* Time range of data: The dataset utilized in this research spans from 2012 to 2020; however, changes due to urbanization processes and socio-economic factors may suggest that longer-term data could better reflect trends. Future studies should consider employing datasets with extended timeframes to enhance the robustness of the models.
* Data quality and timeliness: While efforts were made to ensure data quality, real-world applications may still be influenced by various factors affecting data timeliness and accuracy. For instance, nighttime light data can be impacted by weather conditions or seasonal variations. Therefore, it is crucial to continuously improve methods for data collection and processing to ensure both accuracy and timeliness.
* Expansion of data dimensions: This study primarily focused on the five key factors; however, other potential influencing elements such as changes in residents' lifestyles or demographic age structures may also significantly impact waste generation. Future research could further explore these aspects and incorporate them into the models to enhance predictive accuracy.
# Incremental Formula-Based Fix Localization
## 6 Related Work
### Mutation-Based Fault Localization  
Mutation-based fault localization (MBFL) [8] is a recent direction that utilizes mutation analysis in fault localization. These techniques first use a set of syntactic change operations (i.e., mutation operations) to mutate the program code in order to generate several variant programs, called mutants. They then run these mutations with test cases and measure how the test execution results change when a code element is mutated. Based on this information, MBFL techniques statistically infer program elements that are highly relevant to the fault. A limitation of MBFL techniques is the huge mutation execution cost [9] because they need to generate a large number of mutants, combine them, and run these mutants with many test cases.
# Food Waste as Feedstock for Anaerobic Mono-Digestion Process
## 3 Performance Profile of Single-Stage Anaerobic Mono-Digestion Process
### Organic Loading Rate  
OLR is defined as the amount of substrate to be added with the digester volume and time [85]. Roughly speaking, it is a key parameter influencing both the stability and performance as well as the cost of the AD process [153]. An increase in the OLR up to a most suitable point may result in an increase in the AD process performance. In contrast, too high OLR values may lead to an accumulation of VFAs in the system, causing in the process inhibition and a significant reduction in the CH\({}_{4}\) content [119; 153; 154]. In a review paper by [PERSON] [88], it was indicated that the long-term mono-digestion of FW is typically limited to OLR values below 2.5 g VS/L/d. However, the analysis of data available in the literature allowed to demonstrate that values of OLR applied during this process were in the range from 0.0667 g VS/L/d [54] to 20.1 g VS/L/d [124] (Table 2).  
However, [PERSON] et al. studied [39] the AD process of the FW from a waste management company (Table 1) under thermophilic conditions (50 \(\pm\) 2 \({}^{\circ}\)C) with the application of two values of initial OLR (Table 1). Interestingly, it has been determined that for an OLR of 6.8 and 10.5 g VS/L, the average methane yield was approximately 425 and 445 mL/g VS, respectively. Hence, it was clearly indicated that this parameter did not have a significant impact on the process performance. It is important to mention that the impact of OLR on the methane yield during the single-stage anaerobic mono-digestion process of FW was reported in [129]. In the above-mentioned study, the hollow fiber anaerobic membrane bioreactor (HF-AnMBR) was operated at mesophilic conditions and a pH around 7.2 (Table 2). In turn, the applied values of OLR were equal to 2.43, 4.86, 7.29, 9.72 and 14.58 g COD/L/d. It has been documented that under an OLR in the range from 2.43 to 9.72 g COD/L/d, the CH\({}_{4}\) content in biogas was stable (between 58.9 \(\pm\) 0.3 and 60.9 \(\pm\) 0.2%). However, further increasing this parameter to 14.58 g-COD/L/d led to a significant drop of the pH to 5.3. Consequently, a decrease in the methane content (34.05%) was noted. These results indicated that increasing the OLR resulted in the inhibition of the AD process.
# Machine Learning Techniques for Non-Terrestrial Networks
## 5 Machine Learning General Aspects
### Machine Learning Methods Taxonomy
#### 5.1.3 Reinforcement Learning  
The previous discussed techniques (i.e., supervised and unsupervised learning) are also sometimes called _offline_ learning methods. As a matter of fact, in supervised and unsupervised learning, the training phase takes place upfront, when the machine learning algorithm is disconnected from the environment. Reinforcement learning (RL) instead is applied to an agent, which interacts with the environment. At each time step, the agent receives observations from the environment and must choose an action which should bring it closer to the main goal. The environment, in response to the action, gives a reward which tells the agent how good the taken action was. This kind of processes can be mathematically described by Markov decision processes (MDPs), assuming that the environment is fully observable. Conversely, partial observable Markov decision processes (POMDPs) are used when the agent cannot directly observe the underlying environment state. Different algorithms are used in this field. One of the most important is Q-learning, where the maximum reward for a set of action is used as the learning factor. A recent advance of this algorithm is deep Q-networks (DQNs), where, as the name suggests, the Q-learning technique is empowered by the adoption of a DNN. DQNs fall into the deep reinforcement learning (DRL) techniques. State action reward state action (SARSA) is another algorithm based on Q-Learning, where the next action decision is not dependent on the past rewards and states. The name comes from the quintuple used for the state update, which is composed by the initial state-action, the observed reward after choosing the new state-action pair. Given that DQNs make use of neural network models to predict the next correct action, the entire RL task can be further classified in model-free methods (e.g., Q-Learning, SARSA) and model-based methods (e.g., deep Q-Networks). In NTNs, RL can be used to optimize resource allocation, employ non-orthogonal multiple access (NOMA) methods, dynamic power allocation and computation offloading/caching to name a few.
# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 4 Experiments and Results
### Qualitative Results  
This section presents the reference images (ground truth) and the spectral responses from the different models. For clarity, we focus on four specific wavelengths (420, 500, 670, 720) instead of the 31 bands available. This presentation focuses on three categories of images from the TokyoTech database: Chartres, Character, and Tshirts2, selected randomly.  
Figures 15-17 show the spectral responses generated by the models and the ground truths.  
We observed that the fitted DCRN model provides more accurate spectral responses than the fitted VDSR and LapSRN models, thus confirming previous quantitative results.  
Figure 14: Presentation of the learning and validation curves of the LapSRN model for the loss, PSNR, and SSIM metrics.  
Figure 15: Presentation of the Chartres image, from the ground truth to the spectral response of the adapted VDSR, DCRN, and LapSRN models.
## Abbreviations \begin{tabular}{l l}
**Symbols** & \\ \(\Delta V\) & the voltage change \\ \(\Delta P\) & the active power change \\ \(\Delta Q\) & the reactive power change \\ \(V\) & voltage \\ \(\theta\) & phase angle \\ \(\Delta V_{j}\) & the voltage variation of node \(j\) \\ \(\Delta P_{i}\) & the active power change of node \(i\) \\ \(\Delta Q_{i}\) & the reactive power change of node \(i\) \\ \(\lambda_{ij}\) & the sensitivity of the active power change of node \(i\) to the voltage of node \(j\) \\ \(\gamma_{ij}\) & the sensitivity of the reactive power change of node \(i\) to the voltage of node \(j\) \\ \(\delta_{ji}\) & the sensitivity of power changes at node \(i\) to the voltage at node \(j\) \\ \(\omega_{1}\) & the sensitivity weight coefficients for active voltage \\ \(\omega_{2}\) & the sensitivity weight coefficients for reactive voltage \\ \(X^{*}\) & the ideal point \\ \(X_{i}\) & the actual value of the \(i\)th evaluated object \\ \(y_{i}\) & the Euclidean distance of the \(i\)th evaluated object \\ \(\omega_{j}\) & the weight coefficient of the \(j\)th evaluation indicator \\ \(x_{ij}\) & the calculated value of the \(j\)th evaluation indicator for the \(i\)th evaluated object \\ \(x_{j}^{*}\) & the ideal point for the \(j\)th evaluation metric \\ \(\varphi_{i}\) & the power factor angle of load growth \\ \(m_{1}\) & the active power growth coefficient \\ \(m_{2}\) & the reactive power growth coefficient \\ \(f_{1}\) & the annual comprehensive cost of SOP and energy storage \\ \(f_{2}\) & the load balancing degree \\ \(f_{1}^{\rm raw}\) & the normalized values of \(f_{1}\) \\ \(f_{2}^{\rm raw}\) & the normalized values of \(f_{2}\) \\ \(\alpha\) & the weight of \(f_{1}\) in the objective function \\ \(\beta\) & the weight of \(f_{2}\) in the objective function \\ \(f_{1}^{\rm max}\) & the maximum values obtained when \(f_{1}\) is solved separately \\ \(f_{2}^{\rm max}\) & the maximum values obtained when \(f_{2}\) is solved separately \\ \(f_{2}^{\rm min}\) & the minimum values obtained when solving \(f_{1}\) separately \\ \(C_{\rm SOP}^{\rm min}\) & the investment cost of SOP \\ \end{tabular}
# Kernel Density Estimation on the Siegel Space with an Application to Radar Processing +
## Appendix A Demonstration of Theorem 1  
Let \((M,g)\) be a Riemannian manifold, let \(C\) be a compact subset of \(M\) and let \(U\) be a relatively compact open subset of \(M\) containing \(C\). Then, there is a compact Riemannian manifold \((M^{\prime},g^{\prime})\) such that \(U\) is an open subset of \(M^{\prime}\), the inclusion \(i:U\hookrightarrow M^{\prime}\) is a diffeomorphism onto its image and \(g^{\prime}=g\) on \(\overline{U}\).  
Proof.: We can assume that \(M\) is not compact. Let \(f:M\to\mathbb{R}\) be a smooth function on \(M\) which tends to \(+\infty\) at infinity. Since \(\overline{U}\) is compact, \(f^{-1}()-\infty,a[)\) contains \(\overline{U}\) for \(a\) large enough. By Sard Theorem, there exists a value \(a\in\mathbb{R}\) such that \(f^{-1}(a)\) contains no critical point of \(f\) and such that \(f^{-1}()-\infty,a[)\) contains \(\overline{U}\). It follows that \(N=f^{-1}()-\infty,a])\) is a submanifold with boundary of \(M\). Since \(f\) tends to \(+\infty\) at infinity, \(N\) is compact as well as its boundary \(\partial N=f^{-1}(\{a\})\).  
Call \(M^{\prime}\) the double of \(N\). It is a compact manifold which contains \(N\) such that the inclusion \(i:N\hookrightarrow M^{\prime}\) is a diffeomorphism onto its image (see [39], Theorem 5.9 and Definition 5.10 ). Choose any metric \(g_{0}\) on \(M^{\prime}\). Consider two open subsets \(W_{1}\) and \(W_{2}\) in \(M^{\prime}\) and two smooth functions \(f_{1},f_{2}:M^{\prime}\to[0,1]\) such that  
\[\overline{U}\subset W_{1}\subset\overline{W}_{1}\subset W_{2}\subset\overline{ W}_{2}\subset\operatorname{int}N,\]  
the interior of \(N\),  
\[f_{1}(x)=1\]  
on \(\overline{W}_{1}\), vanishes outside of \(W_{2}\), and  
\[f_{2}(x)=1\]outside \(W_{1}\), and vanishes in \(\overline{U}\). Define \(g^{\prime}\) on \(M^{\prime}\) by  
\[g^{\prime}=f_{1}g+f_{2}g_{0}\]  
on \(N\) and  
\[g^{\prime}=f_{2}g_{0}\]  
outside of \(N\). Since \(f_{1}+f_{2}>0\), \(g^{\prime}\) is positive definite everywhere on \(M^{\prime}\). Since \(f_{1}\) vanishes outside of \(W_{2}\), \(g^{\prime}\) is smooth on \(M^{\prime}\). Finally, since \(f_{1}=1\) and \(f_{2}=0\) on \(\overline{U}\), \(g^{\prime}=g\) on \(\overline{U}\).  
We can now prove Theorem 1. Let \(X\) be a random variable as in Theorem 1. Following the notations of the theorem and the lemma, let \(U=\big{\{}x\in\mathcal{M},d(x,C)<r_{inj}\big{\}}\). \(U\) is open, relatively compact and contains \(C\). Let \((\mathcal{M}^{\prime},g^{\prime})\) be as in the lemma. Let \(\hat{f}\) and \(\hat{f}^{\prime}\) be the kernel density estimators defined on \(M\) and \(M^{\prime}\), respectively. Theorem 3.1 of [1] provides the desired results for \(\hat{f}^{\prime}\). For \(r\leq r_{inj}\), the support and the values on the support of \(\hat{f}^{\prime}\) and \(\hat{f}\) coincide. Thus, the desired result also holds for \(\hat{f}\).
## Circular Polarizer and Circularly Polarized Patch Antenna Design
### Circularly Polarized Patch Antennas Design  
The right-hand circularly polarized patch antenna (RHCPA) was constructed with the circular polarizer illustrated above and an elliptical patch antenna (EPA), mainly consisting of an elliptical radiator with a ground placed on the bottom side of the substrate. They are in a stacked arrangement and separated by an air gap. The detailed configurations of the RHCPA and the LHCPA are shown in Figure 6a-d. Note that the dual bottom splits are offset from the center at a distance of L3 = 3.5 mm. A probe-fed manner is employed with the feeding point located on the long axial of the elliptical radiator and offset from its center so that an LP wave can be radiated from the EPA. The DSSRs are coupled to be resonated in the near field of the driven patch (EPA), and they collaboratively create orthogonal modes of approximately equal amplitude and a relative phase shift of 90\({}^{\circ}\) for CP characteristics. Also, the strong coupling with the EPA brings a low profile property for the proposed antennas. In addition, four Teflon screws are mounted at the four corners of the substrates to support the polarizer. The dimensions associated with the configurations were optimized for RHCP characteristics, and are listed in Table 1. It was worth mentioning that the dimensions (such as semicircular radius R1 and R5 and elliptic axis length L2 and W1) could be easily scaled to fit other frequency band applications, such as UHF and GPS band.  
As with the circular polarizer analyzed previously, reversing the offset of the dual bottom splits of the DSSRs leads to the other sensing circular polarization. Hence, an alternative configuration of DSSRs with the proposed antennas was considered, as shown in Figure 6d, for which the dual bottom splits are reversed at equal offset distance of L3 = 3.5 mm. Thus, a second proposed antenna with such DSSRs keeping the same EPA and superstrate slab dimensions as the RHCPA was obtained and termed left-hand circularly polarized patch antenna (LHCPA). The corresponding characteristics will be demonstrated as follows.  
Figure 5: Magnetic field distributions for the polarizer validation from the waveguide model in Figure 4a. The surface magnetic field distributions on the top side of the model at 2.584 GHz are obtained for four different phase intervals: (**a**) 0\({}^{\circ}\); (**b**) 90\({}^{\circ}\); (**c**) 180\({}^{\circ}\); and (**d**) 270\({}^{\circ}\).
## 2 Materials and Methods
### Methodology
#### 2.4.2 Design Vector and Objective Function Formulation  
Once the physical environment of the RC has been defined, the problem is formulated as a combinatorial multi-objective optimization, aiming to determine the optimal spatial configuration of the workstations. The goal is to simultaneously minimize the total occupied area and the physical interferences between components.  
Each workstation is encoded as a gene within a continuous design vector. Since this is a real-valued representation, each gene takes coordinates \((x_{i}y_{i})\) within the allowed domain of the workspace:  
\[Gen_{i}=\ (x_{1},y_{1})\in[0,\ L]\ \times\ [0,W] \tag{5}\]  
where \(L\) and \(W\) are the dimensions of the available workspace. A complete individual (i.e., a candidate solution) is defined as  
Figure 4: Geometric modeling of workstations, robot base, and defined operating area for the spatial configuration of the RC.  
\[Individual=\{Gen_{1},\ Gen_{2},\ \ldots,\ Gen_{n}\} \tag{6}\]  
This modeling generates an initial population randomly distributed according to a uniform distribution. Based on this encoding, the algorithm evaluates two objective functions:  
* Minimization of the bounding rectangle area that contains all workstations: \[f_{area}=(X_{max}-X_{min})\cdot(X_{max}-X_{min})\] (7)
* Minimization of the number of interferences (geometric overlaps) between workstations: \[f_{interference}=\ \sum_{i,j}\bigl{(}x_{i\ max}\geq x_{j\ min}\wedge x_{j\ min} \geq x_{i\ min}\wedge y_{i\ max}\geq y_{j\ min}\wedge y_{j\ min}\geq y_{i\ min} \bigr{)}\] (8)  
These objectives are combined in a weighted composite function:  
\[f_{comb}=\ w_{1}\cdot f_{area}+w_{2}\cdot f_{interference} \tag{9}\]  
where weights \(w_{1}\) and \(w_{2}\) allow adjusting the relative importance of each objective, prioritizing in this case the elimination of physical interferences.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 6 Performance Evaluation and Discussions
### Throughput Results  
#### 6.2.1 Random Node Distribution Results  
Similar to the observations for the packet delivery ratio (PDR), the impact of dropping attacks on the throughput exhibits a distinct pattern across network distributions, as seen in Figures 10 and 11.  
The random network experiences a noticeable decrease in throughput when under a dropping attack. Throughput drops from a baseline of 22,371,978 bits per second to 17,200,434 bits per second, representing a decline of approximately 23%.  
Figure 9: PDR results’ uniform distribution for all attacks.