esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application to Multiple Quadcopter Systems  
In several special cases of the formation control of MASs, a predefined spot of each agent can be considered as a time-varying function, leading to time-varying formation control. The formation shape established by each time-varying agent position is able to be flexibly changed in many different real applications, such as obstacle avoidance and the cooperative work in carrying payloads. Quadcopters, which are one of the most active classes of UAVs, have two pairs of motors to adjust their altitude and attitude. The reasons why quadcopters have been widely researched in many recent studies are that their practical models shrink in experiments for safety, and they have enough actuators to control their rigid body states including position and orientation [33; 34; 35; 36]. In considering the formation control of multiple quadcopters, each agent can be treated as a point-mass system, and the outer loop dynamic model is described as a second-order dynamics.  
To verify the proposed method in the control of the multi-agent system, the simulation results of six unmanned aerial vehicles (quadcopters) with the dynamic event-triggered controllers were carried out. The investigated systems were able to form an anticipated shape and asymptotically track a predefined trajectory. In this scenario, each tracking reference of the \(i\)th quadcopter was a combination of the predefined formation flight trajectory \(r(t)\) and the individual time-varying position in the formation \(f_{p,i}(t)\). It was supposed that \(r(t)\) was only available to the first quadcopter, while the others did not know \(r(t)\), and all quadcopters possessed their time-varying position \(f_{p,i}(t)\). The proposed algorithm will be applied to the outer loop (double integrator) (2) before calculating the thrust force and the desired attitude for the inner loop (40). By making use of the lower controller in the inner loop, the thrust force \(U_{1i}(t)\) and rotation moments \(U_{2i}(t),\ U_{3i}(t)\), and \(U_{4i}(t)\) control each quadcopter system modeled in (37) and (39). Following the predefined formation flight, the proposed control algorithm and dynamic event-triggered mechanism were designed to guarantee the exponential convergence of formation tracking errors.
## 6 Simulation and Case Study
### Simulation of Multiple Temporal Stages of Frequency Control
#### 6.1.1 Simulation of IR and PFR  
The IR and PFR for a step increase of load (0.05 pu) at 1 s is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Value** & **Unit** & **Parameter** & **Value** & **Unit** \\ \hline \(V_{dc}\) & 800 & V & \(H\) & 5 & pu \\ \(V_{ac}\) & 0.22 & kV & \(D\) & 1 & pu \\ \(L\) & 10 & mH & Percentage \(R\) & 5 & pu \\ C & 350 & μF & Voltage droop & 3\% & pu \\ \(f\) & 50 & Hz & \(\tau\) & 20 & s \\ \(f_{punn}\) & 5000 & Hz & Load step & 0.05 & pu \\ VAbase & 15,000 & W & \(K_{I}\) & 10 & - \\ \hline \end{tabular}
\end{table}
Table 2: Parameters in simulation.  
Figure 7: Simulation of IR and PFR.  
From Figure 7, compared to the response of grid-supporting VSI, the peak time (at which the nadir is reached) is delayed by the inertia effect. The power increment in the PFR loop gradually and partially restores the frequency. Consequently, the emulation of the characteristics of the composite frequency response of SGs is achieved.
## 4 Types of Catalysts Used in Transesterification
### Homogeneous Catalysts
#### 4.1.1 Homogeneous Alkaline Catalysts  
These catalysts are suitable for oils having <2% free fatty acid (FFA) content for transesterification reaction and oils should be pre-treated when FFA content is >2%. FFA content of animal fat is 5-30% and 2-7% in cooking oil; Trap grease is a low quality raw material has FFA content up to 100%. When homogeneous alkaline catalysts are added to such feedstock oils, soap and water is produced as a result of reaction between free fatty acids and catalyst (Equation (3))  
\[\text{RCOOH }+\text{ KOH }\rightarrow\text{ RCOO}^{-}\text{K}^{+}+\text{ H}_{2}\text{O} \tag{3}\]
Notable homogeneous alkaline catalysts used for transesterification involve NaOC4H9, KOH, KOCH3, NaOC2H5, NaOH, Na2O2, NaOCH3. The selection of catalysts is a key factor in determining the fuel performance. Potassium hydroxide and sodium hydroxide lead to the formation of water that reduces efficiency of biodiesel, but when transesterification is achieved by converting them into potassium thermoxide and sodium thermoxide increases the performance of biodiesel as water is not formed. Biodiesel synthesized from two feedstocks corn oil and soybean oil. The alkali-based homogeneous catalysts were NaOH  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Catalyst** & **Raw Material** & **Methanol to Oil** & **Catalyst** & **Time** & **Temp.** & **Yield of** \\  & **Moilar Ratio** & **Conc.** & **(min)** & \({}^{\circ}\)C & **Biodiesel** \\ \hline NaOH & Frying oil & 7.5:1 & 0.5 & 30 & 50 & 96 \\ KOH & Vegetable oil & 6:1 & 1 & 25 & 40 & 51–87 \\ KOH & Tallow & 6:1 & 1 & 180 & 65 & \(\sim\)83.6 \\ NaOH & Sunflower & 6:1 & 1 & 120 & 60 & \(\sim\)97 \\ NaOCH3 & oil & 6:1 & 0.75 & 90 & 65 & \(\sim\)97 \\ NaOCH3 & Soybean oil & 6:1 & 1.35 & 240 & 23 & \(\sim\)97 \\ KOH & Roselle oil & 8:1 & 1.5 & 60 & 60 & \(\sim\)99 \\ \hline \hline \multicolumn{6}{c}{**Homogeneous Acid Catalysts**} \\ \hline H2 SO4 & _Zanthoxylum_ & 24:1 & 2 & 01:20 & 60 & 98 \\ C2 HF3O2 & Soybean oil & 20:1 & 2M & 05:00 & 120 & 98 \\ H2 SO4 & Beef Tallow & 30:1 & 2.5 & 24:00 & 60 & 98 \\ H2 SO4 & Tobacco oil & 18:1 & 3 & 00:25 & 120 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of homogeneous base and acid catalysts used for biodiesel synthesis [25].  
Figure 4: Classification of Catalysts for transesterification.  
and KOH and a binary mixture of both (NaOH/KOH). The process of biodiesel synthesis was performed by using an of oil/alcohol molar ratio (1:6), catalyst concentration (0.5% of oil taken), reaction time (2 h), reaction temperature (65-70 \({}^{\circ}\)C) with constant stirring. The results depict higher conversion yield of soybean oil compared to maize oil where a lower yield was the result of lesser linolenic acid formation. Binary mixture of catalysts showed maximum yield and thus authors concluded that medium contains high concentration of ethoxide ion that gives high biodiesel yield [25].
# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 1 Introduction
[PERSON] et al. utilized a patch localization proxy task and subsequently applied it to a segmentation task. The segmentation accuracy was superior to training from scratch or using other pretext tasks [19]. Similarly, in target detection tasks based on sonar images, we can also design a proxy task to help the detection model learn more useful feature representations for downstream tasks, and by improving the network's ability to distinguish between target and background features, we can reduce false alarms [20]. In summary, the field of underwater detection currently lacks a universal deep learning detection method with a low false alarm rate. Therefore, this paper addresses this research gap by proposing a self-training low false alarm rate training strategy based on sonar images, starting from the actual data and training methods. This training strategy does not require the introduction of external datasets or modifications to the deep learning model but achieves a reduction in false alarm rates during detection by setting up proxy tasks to more deeply learn target and background features.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 5 Conclusions  
In this study, we utilized NDVI, EVI, GOSIF, GPP, and drought indices to assess the spatiotemporal evolution patterns of grassland dynamics and the effect of drought events on grassland ecosystems in Inner Mongolia of China. This study shows that NDVI, EVI, GOSIF, and GPP exhibited significant increasing trends from 2000 to 2020 in 25.83%, 21.18%, 22.65%, and 48.13% of the grassland areas, respectively. Compared with NDVI and EVI, the responses of GOSIF to drought was more intense, and it better reflected the impact of drought events on grassland photosynthesis in 2007 and 2017. The reductions in NDVI, EVI, GOSIF, and GPP intensified as drought severity levels increased. The influence of drought events on NDVI, EVI, GOSIF, and GPP reductions in different grassland types under extreme drought conditions was obviously different, and the effects of different drought levels in the same grassland type were also different. These findings underscore the critical importance of effectively managing land carbon cycling and addressing the challenges posed by climate change, particularly in regions like Inner Mongolia where grassland ecosystems play a vital role.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/atmos14111613/s1](https://www.mdpi.com/article/10.3390/atmos14111613/s1)]([https://www.mdpi.com/article/10.3390/atmos14111613/s1](https://www.mdpi.com/article/10.3390/atmos14111613/s1)), Figure S1: The correlation coefficient between CCI-SM and in-situ data in Inner Mongolia from 2000 to 2010.  
Conceptualization, A.Z. and X.Z.; methodology, A.Z.; software, R.X.; validation, A.Z., R.X. and L.Z.; formal analysis, R.X.; investigation, R.X.; resources, X.Z.; data curation, A.Z.; writing--original draft preparation, L.Z.; writing--review and editing, A.Z.; visualization, A.Z.; supervision, A.Z.; project administration, X.Z.; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2021 YFB3901201; the National Natural Science Foundation of Hebei Province,China, grant number D2022402030; and the Guangdong Basic and Applied Basic Research Foundation, grant number 2021A1515110769.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All of the data used in this study are publicly available. The data presented in this study are available in Section 2.2.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix A 0/1 Multi-objective Knapsack Problem Formulation  
The \(0/1\) multi-objective knapsack problem from [35] is formulated in the following way. Let \(n\) be the number of knapsacks and \(m\) the number of items. Let \(p_{i,j}\) be the profit of item \(j\) according to knapsack \(i\), \(w_{i,j}\) the weight of item \(j\) according to knapsack \(i\), and \(c_{i}\) the capacity of knapsack \(i\). The problem is to find a vector \(\vec{x}=(x_{1},x_{2},\ldots,x_{m})\in\{0,1\}^{m}\) such that  
\[\forall i\in\{1,2,\ldots,n\}:\sum_{j=1}^{m}w_{i,j}x_{j}\leq c_{i} \tag{1}\]and for which \(\vec{f}(\vec{x})=(f_{1}(\vec{x}),f_{2}(\vec{x}),\ldots,f_{n}(\vec{x}))\) is maximum, where  
\[f_{i}(\vec{x})=\sum_{j=1}^{m}p_{ij}x_{j} \tag{10}\]  
and \(x_{j}=1\) iff item \(j\) is selected.  
We choose uncorrelated profits \(p_{i,j}\) and weights \(w_{i,j}\) as random integers in the interval \([10,100]\). The knapsack capacities are set to half of the total of the corresponding item weights:  
\[c_{i}=0.5\sum_{j=1}^{m}w_{i,j}. \tag{11}\]  
A particular vector \(\vec{x}\) generated randomly or via a genetic procedure can violate the capacity restriction of Equation (12) for a knapsack. In this case, following [35], we adopt a greedy repair method. This method removes items from a particular solution until all capacity constraints are fulfilled. The items are removed in the ascending order of maximum profit/weight ratio \(q_{i}\), which is given by the following equation:  
\[q_{j}=\max_{i=1}^{n}\left\{\frac{p_{i,j}}{w_{i,j}}\right\}. \tag{12}\]  
In this way, the items with lower profit per weight unit \(q_{j}\) are removed first. The aim of this procedure is to fulfill all capacity constraints while reducing the overall profit as little as possible.
# Lateral-Torsional Buckling of Cantilever Steel Beams under 2 Types of Complex Loads
###### Abstract  
Cantilever steel beams are an essential structural element in civil engineering fields such as bridges and buildings. However, there is very little research on the critical moment (\(M_{\mathrm{cr}}\)) of cantilever beams subjected to a concentrated load (CL) or a combination of concentrated load and uniformly distributed load (CUDL) when the concentrated load is not limited to the free end. Therefore, the focus of the current paper is the calculation of \(M_{\mathrm{cr}}\) for cantilever steel beams under CL and CUDL. This paper proposes a program and a simple closed-form solution for \(M_{\mathrm{cr}}\) that are applicable to the elastic buckling analysis of cantilever I-beams under CL and CUDL. Based on the Rayleigh-Ritz method, a matrix equation and the corresponding procedure about \(M_{\mathrm{cr}}\) under CL and CUDL are derived by using infinite trigonometric series for the buckling deformation functions. The value of \(M_{\mathrm{cr}}\) and the corresponding mode of buckling can be obtained efficiently by considering the symmetry of the section, the ratio of two load values and the load action position. Experimental results and finite element calculations validate the numerical solutions of the procedure. A closed-form solution for \(M_{\mathrm{cr}}\) is derived according to the assumption of a small torsion angle and the specific values of each coefficient in the closed-form solution of \(M_{\mathrm{cr}}\) are calculated by the proposed procedure. The results show that the procedure and closed-form solution for \(M_{\mathrm{cr}}\) presented in this paper have a high degree of accuracy in calculating the \(M_{\mathrm{cr}}\) of the cantilever beam under CL and CUDL. The deviations between the results calculated by the proposed procedure and data from existing literature are less than 8%. These conclusions are capable of solving the calculation problem of \(M_{\mathrm{cr}}\) for cantilever beams under CL or CUDL, which are both significant load cases in engineering. The study provides a reference for the design of cantilever steel beams.  
cantilever steel beams; lateral-torsional buckling; total potential energy equation; combined load; critical moment +
Footnote †: journal: Applied Sciences
## 1 Introduction
Hence, in the present work, the minimum-loss incidence and deviation angles in axial-flow splittered compressors will be modeled as the additional consideration of SVs. The modeling is based on a database established using the widely applied and validated quasi-three dimensional codes MISES [8]. Moreover, in order to consider the effects of inflow angle, inlet Mach number, camber angle, solidity, blade maximum thickness, and even three-dimensional flow in blade passage (i.e., axial velocity density ratio, AVDR), the model will be corrected using the methods employed in some classical models for conventional blade profiles [7; 9; 10; 11]. In order to establish the database with representative blades, which should be designed with geometries in reasonable ranges, three highly loaded blades were first investigated by varying circumferential position and local incidence of SVs. Then, based on the optimal geometry arrangement, the profile performance at full incidence range was computed for two-dimensional (2D) blades of different camber angle, solidity, and chord length of SVs so as to produce a basic database. This basic database contains the basic correlations of reference minimum-loss incidence and deviation angles. Subsequently, additional analyses of the corrections for orientation parameter, maximum thickness-chord ratio, inlet Mach number, and AVDR were performed to modify the basic correlations. The model was also employed to guide the blade design for a subsonic axial-flow rotor with SVs.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 1 Introduction
This symbiotic relationship between fog generation and removal enhances the overall performance of the system, as the generated fog can effectively test and validate the limits of the dehazing network. In this study, we propose and validate a novel approach to fog generation and removal tailored for surveillance applications. Our research focuses on training a fog generation network, utilizing a state-of-the-art dehazing neural network as its foundation [8], to produce synthetic foggy images. These images are then used to evaluate and compare the performance of various fog generation techniques--including simple fog [17], dark channel-based fog [7], and depth-aware fog [18]--against our proposed network-based method [16]. Unlike existing fog generation methods based on physical models or depth priors, our approach uniquely leverages a high-performance dehazing network (GridNet) in reverse to synthesize fog that is better aligned with modern dehazing pipelines. This network-driven synthesis strategy produces more realistic and structurally consistent fog patterns, enabling more effective evaluation and improvement of dehazing performance. Our experiments demonstrate that the proposed fog generation network, informed by the best-performing dehazing architecture, achieves superior results, as evidenced by quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Through this comparative analysis, we confirm that a network-driven fog synthesis approach not only enhances the realism of generated fog but also maximizes the effectiveness of fog removal, offering a robust solution for real-world surveillance systems [1]. The main contributions of this paper are as follows:* We propose a neural network-based fog generation model built upon the GridNet architecture, enabling realistic and context-aware synthetic fog for surveillance imagery. * We design a training strategy that incorporates perceptual and dark channel consistency losses to enhance the visual quality of the generated fog. * We perform comparative evaluations with existing fog generation techniques using standardized dehazing networks and demonstrate superior restoration performance in terms of PSNR and SSIM. * We validate the practical applicability of our approach in real-world surveillance scenarios, highlighting its effectiveness under adverse weather conditions.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## Abbreviations
### Proposed Estimator of the Transition Matrix Particularized to Disaggregated Data  
Note that if we apply the results from Section 4.4 to the case where \(M=1\) (which is equivalent to considering that the data have been disaggregated and then concatenated), we have that \(y_{t}=x_{t}=\delta_{j}\) for some \(j\in\{1,\ldots,n\}\). In this case, (10) becomes the following:  
\[\widehat{\Pi} =\arg\min_{\Pi}\sum_{t=0}^{T-1}\|x_{t+1}-\Pi\cdot x_{t}\|^{2}= \arg\min_{\Pi}\sum_{i=1}^{N}\sum_{j=1}^{N}M_{ji}\|\delta_{j}-\Pi\cdot\delta_{ i}\|^{2}\] \[=\arg\min_{\Pi}\sum_{i=1}^{N}\sum_{j=1}^{N}M_{ji}\|\delta_{j}-\Pi _{i}\|^{2},\] (2) subject to \[\Pi\geq 0,\text{ and }\mathbf{1}_{N}\cdot\Pi=\mathbf{1}_{N}\]  
so that the overall optimization problem can be disclosed in the following separate problems:  
\[\begin{split}\widehat{\Pi}_{i}=\arg\min_{\Pi_{i}}\sum_{j=1}^{N}M_ {ji}&\|\delta_{j}-\Pi_{i}\|^{2}=\arg\min_{\Pi_{i}}\sum_{j=1}^{N}M_{ ji}\Big{(}(1-\Pi_{ji})^{2}+\sum_{k\
eq j}\Pi_{ki}^{2}\Big{)},\\ &\text{subject to }\Pi_{i}\geq 0,\text{ and }\mathbf{1}_{N}\cdot\Pi_{i}=1.\end{split} \tag{3}\]Applying the Karush-Kuhn-Tucker conditions, we have that \(\widehat{\Pi}_{i}\) is the solution of the following set of equations:  
\[\begin{split}&\frac{\partial}{\partial\Pi_{pi}}\Bigg{[}\sum_{j=1}^{N}M_{ ji}\Big{(}(1-\Pi_{ji})^{2}+\sum_{k\
eq j}^{N}\Pi_{k}^{2}\Big{)}+\lambda_{i}(\sum_{l=1}^{N} \Pi_{ji}-1)\Bigg{]}+\lambda_{i}-\mu_{pi}\\ &=2M_{pi}(\Pi_{pi}-1)+\sum_{j\
eq p}^{N}2M_{ji}\Pi_{pi}+\lambda_{ i}-\mu_{pi}\\ &=2\Big{[}M_{pi}(\Pi_{pi}-1)+(M_{i}-M_{pi})\Pi_{pi}\Big{]}+ \lambda_{i}-\mu_{pi}\\ &=2\Big{[}M_{i}\Pi_{pi}-M_{pi}\Big{]}+\lambda_{i}-\mu_{pi}=0,\ p=1, \ldots,N\end{split} \tag{10}\]  
together with the following conditions:  
\[\widehat{\Pi}\geq 0, \tag{11}\]  
\[\mathbf{1}_{N}\cdot\widehat{\Pi}=\mathbf{1}_{N} \tag{12}\]  
\[\mu_{ij}\geq 0,\ i,j\in\{1,\ldots,N\} \tag{13}\]  
\[\mu_{ij}\widehat{\pi}_{ij}=0,\ i,j\in\{1,\ldots,N\} \tag{14}\]  
The solutions of these equations are given by the following:  
\[\widehat{\Pi}_{pi}=\frac{M_{pi}}{M_{i}},\ \lambda_{i}=0,\ \mu_{pi}=0,\ \forall i,p=1, \ldots,N \tag{15}\]  
which correspond with the MLE estimator presented in (10). Since the solution of (10) is obtained for \(\lambda_{i}=0\), \(\mu_{pi}=0\), and satisfies the restrictions, this suggests that each \(\widehat{\Pi}_{i}\) can be obtained by solving the unconstrained version of (11).  
Appendix B Quadratic Canonical form and the Karush-Kuhn-Tucker Conditions for the Estimation Scheme of the Transition Matrix
###### Abstract  
Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the \(R^{2}\) obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N-PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.  
unfold methods; chemometrics; Sentinel-2; multispectral remote sensing +
Footnote †: journal: remote sensing  
0
Footnote †: journal: remote sensing
# Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson's Disease
## 5 Discussion
### Global Wavelet Curves Profile of PD Patients over 60 Years of Age (>60)  
In PD patients >60, the \(\delta\) brainwave was predominant as compared with the CL group >60 and both the <60 groups of CL and PD patients. Predominance of the \(\delta\) brainwave in PD patients >60 has been previously described and attributed to several phenomena, such as disturbance of the afferent impulses, mainly cholinergic from the basal forebrain to the cortex, disruptions in the pyramidal neurons, white matter lesion or thalamic lesion [50], the overall synchronization of glial cell activity [51] and the use of prescribed drugs like Rasagiline [48]. Furthermore, in this group, a high dispersion in both power and frequency was observed in the main \(\delta\) brainwave and in the secondary \(\alpha\) brainwave, possibly related to the neurodegeneration plus the effects caused by the mix of drugs routinely used in PD patients.