esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 3 Path Generation Algorithm for Graders  
### Characteristics of Graders  
Graders play a crucial role in road construction by spreading and leveling aggregate [23; 24; 25]. The grader consists of a main frame with the driver's seat at the center, an attachment device and supporting work power device at the front, a power transmission device at the rear for delivering driving power, and control devices for managing power distribution throughout various parts [26]. Typically, graders are equipped with blades used to spread evenly or transport materials. Unlike other equipment, graders can rotate and tilt their blades in multiple directions. This capability allows the grader to navigate small obstacles within the work area and enhances the precision of its tasks.  
A standard grader with a blade usually has a blade width greater than that of the vehicle body (see Figure 1(a)). When generating a work path, it is essential to account for this by ensuring that the equipment is positioned at a safe distance from the edges of the work area to ensure safety. Additionally, because graders have a larger turning radius than other earthmoving equipment, they require a substantial non-working area to change direction. Figure 1(b) illustrates how the turning radius is measured, with the front wheel serving as the center of the turning circle. Therefore, it is necessary to select appropriate turning and line-changing methods within the designated nonworking area and generate the work path accordingly [26].  
Figure 2: Motor grader specifications.
## 2 Models for SOFC and their Verification Degree  
In [1; 2], the modelling principle for the thermal subsystem of SOFCs was described. It is based on general techniques presented, for example, in [11], and is adjusted to fit the actual SOFC test rig. The overall procedure can be divided into three general steps. The first is to describe the thermal behaviour of the corresponding SOFC subsystem by balancing the here considered thermal energy of internal heat generation, heat conduction and heat flows across the system boundary due to gas supply, heat convection and radiation. In the second step, this system model is discretised in the space coordinates with the help of the finite volume method to obtain a set of ODEs for the temperature of the stack which are coupled with the dynamics of the gas preheaters. From a control perspective, the preheaters represent the actuators that are required to influence the operating temperature despite variable electric power generation by the SOFC stack. These ODEs are used as the basis for the subsequent simulation and control of the SOFC stack. In contrast to the methodology in [12], where the system model is simplified even further by a linearisation, the approach described in [1; 2] leads to system representations that are valid for large operating domains by accounting for the most relevant nonlinear phenomena. In the third step, the parameters of the developed ODE models are identified using least squares minimization (or, in case of interval methods, global optimisation) with the measured data from the SOFC test rig. Further control-oriented models for SOFCs, for example, those for designing model-based state estimation procedures, can be found in [13; 14].  
In this paper, we assume that the structure of the ODE-based SOFC models is already given and that their parameters need to be identified on the basis of experimental data. To analyse their reliability, we apply the principles developed in [6; 15; 16] for verification and validation assessment of complex processes with the help of result verification. These principles originate from the corresponding traditional studies in the areas of computational fluid dynamics and biomechanics [5; 17]. In this section, we first give a brief overview of the modelling principles behind the SOFC test rig. After that, we outline the features of methods with result verification. Next, we show how a verification hierarchy can be defined for a system model implemented using a computer. Finally, we analyse the ODE-based SOFC models with respect to their verification degree. This analysis gives a better understanding of the model quality and points out possible simplifications.
## 6 Performance Evaluation
### Reliability in the Network  
Several techniques are used in industrial technologies to ensure reliable wireless communication, such as re-transmission, channel hopping, and multipath routing. The re-transmission scheme depends on the re-transmission of failed packets. In case of errors, this scheme incurs significant communication overhead as well as additional latency in delivering the packets. In multipath routing technique, each node has multiple next hops to forward the packet. When interference causes disruption of communication between a node and its next hop, an alternative path can be used to transport data [7]. Channel hopping and re-transmission schemes are used in the data link layer of D-MSR and WirelessHART. The multipath routing technique is deployed in the WirelessHART standard. In this section, we first evaluate the packet delivery ratio in case of internal interference as well as the effect of re-transmission capability. Next, we assess the behavior of D-MSR and the WirelessHART routing mechanism in terms of reliability in case of extensive external interference. To this end, we set up a number of experiments to evaluate the performance of data delivery in case of lossy networks.
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 3 System Model and Problem Formulation
### Laplacian Spectral Gap  
Let \(A(G)=[a_{ij}]\) be the adjacency matrix of a network \(G(V,E)\) where \(a_{ij}=1\) if there is an edge from station \(i\) to station \(j\) and \(a_{ij}=0\) otherwise. Let \(D(G)\) be the degree matrix \(D(G)=[d_{ij}]\) of \(G\) which is a diagonal matrix with \(d_{ij}=|N(i)|\) if \(i=j\) and, \(d_{ij}=0\) otherwise. From these two matrices, we can compute the Laplacian Matrix of the network \(L(G)\) as \(D(G)-A(G)\)[40]. Since a simple graph can represent the networks under consideration (links are bidirectional), \(L(G)=D(G)-A(G)\) is a symmetric, positive semidefinite matrix that is also known as the combinatorial Laplacian matrix or Kirchoff matrix. For this type of network, \(L(G)\) has a set of \(n=|V|\) real eigenvalues \(\lambda_{1}\), \(\lambda_{2}\),\(\omega_{|V|}\) where the second smallest eigenvalue (\(\lambda_{2}\)) is called the _Laplacian Spectral Gap_, _LSG_[40] of the network \(G\). A large value of LSG implies a higher expansion, meaning that the network is generally well connected [20]. This type of graph is associated with _Expander Graphs_, a family of sparse graphs with strong connectivity properties [41].
## 1 Introduction
Despite these advancements, most SIF downscaling studies have been applied at large spatial scales (e.g., global), making it challenging to apply them to smaller regional areas. As one of the world's major food crops, the widespread cultivation of wheat has a significant impact on the global agricultural economy [26]. The growth cycle of winter wheat is closely related to climate change, and accurate monitoring of its growth status is crucial for improving yield and quality [27]. In recent years, SIF has demonstrated a unique potential for application in crop yield estimation [28; 29]. Therefore, real-time monitoring and prediction of winter wheat growth can be effectively improved by downscaled SIF data, which can provide a scientific basis for farmers to make more accurate decisions in the planting process, thus optimizing agricultural management and improving yields [30]. To solve the above problems, this study uses MODIS data with 1 km spatial resolution as driving variables, combines these variables with TROPOMI enhanced SIF (eSIF) with a resolution of 0.05\({}^{\circ}\) through resampling, utilizes a variety of models to train the training sample dataset, compares the accuracy metrics of the model calculations, and selects the optimal algorithmic model to construct the downscale model to improve the accuracy of downscaled SIF and invert the 1 km resolution SIF products in Shandong Province. In addition, we verified the consistency of 1 km resolution SIF with other SIF satellite products from the perspective of a time series, verified the reliability of 1 km SIF from the standpoint of the SIF-GPP vegetation mechanism relationship, and finally, applied 1 km SIF to analyze the growth of winter wheat in Shandong Province. In this study, we aimed to improve the spatial resolution of SIF data to make it more applicable to small-scale agricultural areas, thereby improving the ability to monitor and manage winter wheat cultivation. This improvement is particularly important in areas such as pest and disease control, yield estimation, and crop growth monitoring. The downscaled SIF data provide more accurate and regionalized SIF information, which can help to better manage winter wheat growth in Shandong Province [31].
## 5 Methodology
### Experimental Setup  
Figure 6 shows the experimental setup used for testing the OCC link performance considering different parameters. For the transmitter, we used an SMD 1 mm \(\times\) 0.5 mm LED (20 mA) powered and controlled by a waveform generator. This configuration enabled the LED to blink with configurable light offsets (A) [Lux] and switching frequency (\(f_{\text{t}}\)) [Hz] values [27]. The purpose of using a small LED was to avoid stimulating a large region at the receiver. This allowed the stimulated pixels to maintain a reset state for a short period, making them capable of detecting high-frequency transmitted data, as explained in Section 3.2.  
To assess the viability of n-pulse modulation, we conducted experiments in a dark room, using a distance of 200 mm. The use of a dark room aimed to prevent the stimulation of other regions by external light sources at the receiver, which could have introduced non-carrying data at the output bus, saturating it and potentially affecting the overall system performance. In addition, the stimulation of a large number of pixels would have increased the negative effects of the refractory period.  
For the receiver side, we used a _DVXplorer_ event camera from _intivation_ (Zurich, Switzerland). Its main parameters are depicted in Table 1. A lens with \(f\)/1.6 and 4-12 mm focal length was mounted on the camera during the experiments.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Spatial Resolution & 640 \(\times\) 840 & Pixels \\ Temporal Resolution & 60–200 & \(\upmu\)s \\ Max Throughput & 165 & MEPS \\ Typical Latency & \(<\)1 & ms \\ Dynamic Range & 90 (3–100 k lux) & dB \\ Pixel pitch & 9 & \(\upmu\)m \\ Connectors & USB 3.0 C port \\ Power Consumption & \(<\)140 mA-5 VDC \\ Sensor technology & 90 nm BSI CIS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the DVS camera.  
Figure 6: Experimental setup showing an LED as a transmitter controlled by the function generator and an event camera as the receiver. With this configuration, various parameters of both the transmitter and the receiver could be adjusted to conduct experiments with different values representing different scenarios.
## 4 Experiments
### Ablation Study  
To analyze the effects of each component in SA-Net, we perform detailed ablation studies on the PhotoTourism dataset. We divide our network into the following components: confidence distribution encoder (CDE), Cluster-based Graph Mapping block (CGM), FPS Aggregation Dispatch Block (FAD) and Chamfer Loss (CL). We ablate the structures within our network to demonstrate the importance of each component. The result of the ablation study is shown in Table 3. We conduct our tests on 6000 images from PhotoTourism, maintaining the same experimental setup and parameters as in Table 1. A neural network with only the Con. module is used as the baseline method to verify the improvement in accuracy brought by the other network modules. Based on the baseline, we retrain four groups of networks: CDE, CDE+CGM, CDE+FAD+CL, and the full version of the network. The CDE+CGM combination improves by 0.03 at @\(5^{\circ}\) compared to the baseline, while the CDE+FAD+CL combination shows more significant improvements at @10\({}^{\circ}\) and @20\({}^{\circ}\). The full network achieves the best performance across all metrics.  
Meanwhile, we posit that increasing the number of sampling preferences does not inherently yield better results. To test this hypothesis, we conduct ablation experiments with varying values of \(L\), reporting the F1 scores, epipolar error, and inference time (see Table 4). Our findings indicate that when the number of sampling preferences is too small, the model is prone to becoming trapped in local optima. Conversely, when the number of preferences is too large, the sampling per preference becomes insufficient, resulting in a notable decline in overall accuracy.
## 3 Results and Discussion
### Central Composite Design
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **DF** & **Mean Square** & **F Value** & **\(p\)-Value** & \\ \hline Model & 21.23 & 10 & 2.120 & 377.52 & \textless{}0.0001 & *** \\ A-water temperature & 0.3775 & 1 & 0.3775 & 67.13 & \textless{}0.0001 & *** \\ B-Coffee-to-water ratio & 20.08 & 1 & 20.08 & 3570.88 & \textless{}0.0001 & *** \\ C-coffee mesh size & 0.6530 & 1 & 0.6530 & 116.12 & \textless{}0.0001 & *** \\ D-extraction time & 0.0000 & 1 & 0.0000 & 0.00 & 1.00 & \\ \hline AB & 0.0482 & 1 & 0.0482 & 8.57 & 0.0086 & ** \\ AC & 0.0025 & 1 & 0.0025 & 0.4372 & 0.5164 & \\ AD & 0.0113 & 1 & 0.0113 & 2.01 & 0.1727 & \\ BC & 0.0482 & 1 & 0.0482 & 8.57 & 0.0086 & ** \\ BD & 0.0041 & 1 & 0.0041 & 0.7227 & 0.4058 & \\ CD & 0.0041 & 1 & 0.0041 & 0.7227 & 0.4058 & \\ \hline Residual & 0.1068 & 19 & 0.0056 & & & Not \\ Lack of fit & 0.0821 & 14 & 0.0059 & 1.18 & 0.4594 & significant \\ Pure error & 0.0248 & 5 & 0.0050 & & & Not \\ \hline Standard deviation & 0.0750 & & & \(\text{R}^{2}\) & 0.9950 \\ Mean & 2.46 & & Adjusted \(\text{R}^{2}\) & 0.9924 \\ C.V. (\%) & 3.05 & & Predicted \(\text{R}^{2}\) & 0.9897 \\  & & & Adequate precision & 62.4579 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result of ANOVA for the 2 FI model identifying factors and pairwise interactions significantly influencing TDS. The R\({}^{2}\), adjusted R\({}^{2}\), and predicted R\({}^{2}\) values were calculated to be 0.9950, 0.9924, and 0.9897 with an adequate precision of 62.4579, showing an adequate signal to navigate the design space. The present model showed a LOF F-value of 1.18, implying that the lack of fit was not significant. Hence, the model could be used for the prediction.
# A Novel Approach for Emotion Recognition Based on EEG Signal Using Deep Learning
## 3 Material and Methods
### Feature Extraction Methods
#### 3.2.1 Empirical Mode Decomposition  
EMD was first introduced at the end of the 1990s. This method is based on analyzing the original signal into a subset of signals repeatedly and in different patterns depending on separate spectral bands [30,31]. This algorithm is used a lot, although it does not contain an accurate mathematical equation, and it faces difficulties in separated similar frequencies. As this algorithm repeatedly detects the minimum and maximum limits in the signal, it also removes the average of the envelopes as a low pass center line [31]. The power of this algorithm has decreased since it has become highly dependent on methods of extreme point finding, interpolation of extreme points into carrier envelopes, results of neuroscience experiments, electro diagrams, and sea-surface height readings in analyzing the signal [30]. Despite this mathematical deficiency, this algorithm had a great impact on signal analysis and has been applied a lot in biomedical signal processing. Studies, such as signal analysis in sound engineering [32], climate analysis [33], and epilepsy detection [34], can be given as examples. Due to its success in biomedical fields, the EMD method was used in this study to decomposes a signal \(x(t)\) into a series of IMFs, as in Equation (1) [35].  
\[x(t)=\sum_{j=0}^{i}I_{mode}(j)+I_{res} \tag{1}\]  
where \(I_{mode}(j)\) represents IMF, \(I_{res}\) specifies a low-order polynomial component. After applying this equation, a limited number of intrinsic mode functions will be produced  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Game Symbol** & **Stimuli Type** & **Discrete Model** & **Dimensional Model** \\ \hline G1 & Boring & Negative emotion & LANV zone \\ G2 & Calm & Positive emotion & LAPV zone \\ G3 & Horror & Negative emotion & HANV zone \\ G4 & Funny & Positive emotion & HAPV zone \\ \hline \hline \end{tabular}
\end{table}
Table 1: The stimuli that used in the GAMEEMO data set.  
nearly perpendicular to each other. Figure 3 shows the IMFs of the EEG signal used in this study, obtained by EMD.
## 3 Results and Discussion
### Anthellmitic Activity  
In the present study, the effect of the synthesized benzimidazole derivatives on the viability of _T. spiralis_ ML was evaluated in comparison to albendazole and ivermectin, following the standard procedure [1; 3]. The results, presented in Table 1, showed that the tested benzimidazoles reduced the viability of parasitic larvae in a time- and dose-dependent manner.  
The benzimidazole-thiophene compounds exhibited high larvicidal activity, confirming our initial expectations. The methanamine analogue **4a** resulted in a significant reduction (\(p\leq 0.001\)) in the mean number of viable larvae count, with an efficacy of 54.5% at a concentration of 50 \(\mu\)g/mL after 24 h of incubation compared to the control group (untreated larvae), which had a mean viable larvae count of 97.6 \(\pm\) 1.14. This efficacy is higher than that observed for ivermectin (45.5%) and albendazole (10.5%) under these experimental conditions (\(p\leq 0.05\) versus albendazole). The in vitro study also revealed a significant reduction (\(p\leq 0.001\) versus albendazole) in the average number of _T. spiralis_ ML after 48 h of incubation with 100 \(\mu\)g/mL of compound **4a**, with an efficacy of 80.5%.  
Figure 2: Absorption spectra of **6a** in DMSO solution (\(5\times 10^{-5}\) mol/L) after UV light in a SUNTEST instrument.  
The introduction of an alkyl substituent at the first position of the benzimidazole nucleus (**4b** and **4c**) leads to a slight decrease in activity. For example, compound **4c** (R = C\({}_{2}\)H\({}_{5}\)) showed lower activity against _T. spiralis_ ML (73.4% at 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL after 48h) compared to that of its methyl derivative **4b** (R = CH\({}_{3}\)) (74.0%). It is interesting to note that the addition of a nitrogen atom to the linker that connects the benzimidazole and thiophene parts of the molecule (compound **4a**, -N=CH- vs. compound **6a**, -NH-N=CH-) results in a 23% reduction in anthelmitic activity. Compound **4a** was distinguished for 80.5% efficacy at a concentration of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (\(p\leq 0.0001\) versus albendazole), while derivative **6a** showed a 57.5% larvicidal effect (\(p\leq 0.001\) versus albendazole) after 48-h exposure at the same concentration (see Table 1). The benzimidazoloyl-2-hydrazones of furan-2-carboxaldehyde and pyridine-4-carboxaldehyde (compounds **6b** and **6c**), on the other hand, showed less pronounced effects against the parasites than a thiophene derivative **6a**, but were comparable to those of the clinically approved antelminic albendazole. These results are consistent with findings from our previous antiparasitic study on benzimidazole compounds bearing a heterocyclic moiety [3]. They pave the way for further in-depth studies regarding the anti-_T. spiralis_ activity of benzimidazole-thiophene Schiff base compounds.
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 3 System Model
In practical applications, the multiplication factor \(m_{1}\) of the channel when the weather condition is different can be determined according to Table 1. The power of the transmission signal can be expressed as  
\[S=(\frac{A}{m_{1}})^{2}. \tag{18}\]The average SNR for Ka-band fading channels can be expressed as  
\[SNR =10\log_{10}(\frac{S}{N}) \tag{19}\] \[=10\log_{10}(\frac{A^{2}}{m_{1}^{2}\delta^{2}})\.\]
## 2 Methods
### System Model  
As shown in Figure 1, we consider a millimeter-wave massive MIMO system designed for UAV AG communications. In this system, the GS acts as the receiver and is equipped with \(N_{\text{r}}\) receiving (Rx) antennas. \(N_{\text{t}}\) UAVs are considered as transmitters, and each UAV is equipped with a single transmitting (Tx) antenna.  
As depicted in Figure 2, the uncoded bits \(\mathbf{b}_{n}\) for the \(n\)-th transmitter (\(n=1,2,\ldots,N_{\text{t}}\)) are individually modulated into transmitted symbols, denoted as \(s_{n,k}\) for the \(n\)-th transmitter (\(n=1,2,\ldots,N_{\text{t}}\)) and the \(k\)-th subcarrier (\(k=1,2,\ldots,N_{\text{c}}\)). Here, \(N_{\text{c}}\) represents the total number of subcarriers for each UAV. The symbols \(s_{n,k}\) belong to the finite set of constellation points denoted as \(\chi\), which can be defined using quadrature amplitude modulation (QAM) or phase shift keying (PSK) schemes, following standard practices. All UAVs share the same \(\chi\), and \(s_{n,k}\) is randomly selected from \(\chi\). For simplicity, we assume that \(s_{n,k}\) represents the symbol transmitted to the antenna of each UAV, without considering the specific processes of the inverse fast Fourier transform (IFFT) or cyclic prefix (CP) insertion in the time domain. The transmitted symbols propagate through the wireless channel \(\mathbf{H}_{n,k}\). The received signal vector \(\mathbf{y}_{k}\) for the \(k\)-th subcarrier is given as  
\[\mathbf{y}_{k}=\mathbf{H}_{k}\mathbf{s}_{k}+\mathbf{n}_{k}, \tag{1}\]  
where \(\mathbf{s}_{k}=[s_{1,k},\dots,s_{N,k}]^{\mathsf{T}}\in\mathbb{C}^{N_{k}\times 1}\), \(\mathbf{H}_{k}\in\mathbb{C}^{N_{k}\times N_{k}}\), \(\mathbf{y}_{k}\in\mathbb{C}^{N_{k}\times 1}\) and \(\mathbf{n}_{k}\in\mathbb{C}^{N_{k}\times 1}\sim\mathcal{N}_{\mathbb{C}}\big{(}0, \sigma_{\mathbf{n}}^{2}\mathbf{I}_{N_{k}}\big{)}\) is the complex Gaussian noise vector with \(\sigma_{\mathbf{n}}^{2}\) as the noise variance.  
Figure 1: A typical UAV AG propagation scenario in an urban area. UAVs transmit signals which the GS then receives. Some scatterers are present in the propagation environment. There may be line-of-sight (LOS) paths or non-light-of-sight (NLOS) paths for UAVs in different locations.  
Figure 2: A block diagram illustrating a MIMO system for UAV AG communications. The detector relies on the estimated channel state information (CSI) to operate.  
To enhance understanding, the definitions of key notations used in this article are shown in Table 1.