esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Optoelectronic platform is an important payload in the field of aerospace and widely used in geographic mapping, measurement, and positioning. In order to obtain high-precision attitude measurement, gyro and accelerometer are applied in the feedback loop of light of sight (LOS) control system of optoelectronic platform. Aiming at compensating for gyro drift and maneuvering acceleration disturbance, an adaptive 3-axis attitude estimation method is proposed in this paper. An adaptive threshold criterion is designed by applying the accelerometer data in the sliding window. The threshold is determined in real time to judge whether the maneuvering acceleration exists. If it exists, the angular attitude error is compensated for by the gyro drift model. Otherwise, the angular attitude error is compensated by multi-sensor fusion. Furthermore, a phase-lag-free low pass filter (LPF) is applied to compensate for the phase lag error introduced in the above attitude estimation process. Compared with the angular attitude calculated by gyro, the root mean square error (RMSE) of the proposed method in roll, pitch, and yaw attitude decreased 44.23%, 49.91%, and 46.21%, respectively. In addition, the proposed method can estimate the attitude accurately without obvious phase lag when the maneuvering acceleration disturbance exists. The focus of this paper is to improve the performance of LOS motion control system of optoelectronic platform from the perspective of sensor signal processing. This method is suitable for aerospace applications with high-precision measurement and positioning requirements without maneuver interference, drift error and phase lag.  
optoelectronic platform; adaptive threshold criterion; multi-sensor fusion; attitude estimation +
Footnote †: journal: Applied Sciences  
1
Footnote 1: Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (Y.K.). [EMAIL_ADDRESS] (D.T.)  
2022
## 6 Information Fusion Strategy
### Comparison of Estimated Areas vs. FAO's Statistics  
In order to have both spatial and numerical certitude, the final areas were compared with the statistics of the harvested area of the countries of the region to verify that the error be under acceptable levels. As it can be seen in Figure 12, where each point represents the estimated area vs. the statistics reported from FAO (Food and Agriculture Organization) of each country considered, the estimated areas per country are very close to the data reported in the FAO's statistics. In the same way, in Table 7, the error per crop area in percentage using the estimated areas and the FAO's statistics in the entire region is shown, and, in all the selected crops, the difference is smaller than 10%. This step is crucial since the level of precision required in a study of agricultural drought risk assessment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline _Crop_ & \begin{tabular}{c} _Estimated_ \\ _(km\({}^{2}\))_ \\ \end{tabular} & \
\begin{tabular}{c} _FAOSTAT_ \\ _(km\({}^{2}\))_ \\ \end{tabular} &
\begin{tabular}{c} _Error_ \\ _(\%)_ \\ \end{tabular} \\ \hline _Banana_ & _3974.38_ & _3736.30_ & _6.37_ \\ _Coffee_ & _11,300.73_ & _11,295.11_ & _0.05_ \\ _Maize_ & _19,888.6_ & _21,347.7_ & _6.84_ \\ _Rice_ & _7354.98_ & _6744.94_ & _9.04_ \\ _Sugar Cane_ & _6656.3_ & _6914.6_ & _3.74_ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error between estimated area vs. FAO’s statistic in the region of study.  
Figure 11: Sample of estimated areas of coffee and verification through checkpoints from crowdsourcing’s images in Costa Rica.
# Personalized Advertising Design Based on Automatic Analysis of an Individual's Appearance
## 4 Methodology
### Supervised Model  
The path marked by the red circle with the letter B represents the first model, which was designed as a convolutional neural network architecture with classifier layers. This model served as the foundation for subsequent models and was trained using supervised learning, evaluated as a multiclass classification problem. For each style, there are two possible results: 0 indicates the absence of the assessed style, while 1 indicates its presence. The goal is to determine if the images in the Style7 dataset contain sufficient information to identify the apparent style.  
Figure 4: Class distribution histogram for Style7 dataset.  
Figure 3: Assignation to Style7 dataset classes.  
It is important to note that experimentation with this model involves ongoing optimization of several factors:  
* **Hyperparameters**: This includes fine-tuning parameters such as the activation function, optimizer settings, learning rate, batch size, and kernel size.
* **Architecture**: The overall structure and design of the model must be carefully considered and refined.
* **Regularization techniques**: Various techniques, such as normalization or dropout, may be applied to enhance the model's generalization and prevent overfitting.  
Style7 Architecture  
The Style7 architecture, as illustrated in Figure 5, serves as the initial approach for the model. It is based on a four-pair convolution and max-pooling layer architecture, which extracts features and reduces the volume's dimensions to 16 \(\times\) 16 \(\times\) 256. This embedding is then flattened and propagated through a multilayer perceptron (MLP) classifier network with three fully connected layers. The output layer of this network consists of a seven-position one-hot encoded vector representing the seven universal styles.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 5 Dual- and Triple-Frequency Multi-GNSS PPP Atmospheric Constraining with AR
### Dual-Frequency with GPS-AR Analysis  
The third scenario investigated involved resolving the ambiguities for GPS satellites while applying atmospheric constraints in a dual-frequency GREC PPP processing. Only GPS ambiguities were resolved for this analysis but future work would include GEC-AR. Similar to the other previous scenarios, the histogram, as shown in Figure 10, saw an average of 6% improvement over the unconstrained, in terms of percentages of stations converging under 20 cm of horizontal error. More insight was provided in the times series as shown in Figure 11.  
With convergence defined as 10 min for solutions to fall under 10 cm of horizontal positional error, and considering the 95 th percentile, the atmospheric constrained solutions with GPS-AR achieved convergence in less than 2 min. The steadiness of the converged solutions was consistent than triple-frequency atmospheric constrained solutions.  
In summary, it is imperative to bring into context all the significant levels of improvement throughout all the various scenarios addressed in this paper. Shown in Table 3 are the significant improvements in convergence time observed with contributions from the research work in this paper. The typical convergence in minutes is compared to the research contributions through the results presented in this paper. With RTK-like performance being the target, it is expedient to address how close PPP is to RTK performance. Considering a typical RTK convergence of a few centimetres in a few minutes, the atmospheric constrained multi-GNSS PPP solutions presented is closely comparable to RTK performance, though we are not there just yet. The significance of this enhanced performance informs the possibility of using PPP in much more time-sensitive applications.  
Figure 10: Histogram showing percentage of 40 stations converging based on 24 hourly solutions in dual-frequency GREC with GPS-AR processing mode. Results shown have a 20 cm horizontal error threshold.
## 2 Materials and Methods
### Measurements of Photosynthetic Parameters  
The photosynthetic efficiency was measured in three groups of plants: control, drought-stressed, and recovered ones in each _Brassica_ crop. Chlorophyll \(a\) (Chl) fluorescence measurements were measured _in vivo_ using a Plant Efficiency Analyzer (PEA, Hansatech, Norfolk, UK). Chl measurements were performed on seven (\(n=7\)) dark-adapted plants (30 min) per group of each cultivar. Chl transients (OJIP) were induced by applying a pulse of saturating red light with a maximum intensity at 650 nm and a photon flux of 3000 umol m\({}^{-2}\) s\({}^{-1}\). Changes in fluorescence were measured over one second, and the obtained data were used to calculate JIP-test parameters (Table 1) [47; 60]. OJIP transients represent the mean values of seven measurements for each treatment and variety, where every treatment was normalized to their corresponding control. The fully watered seedlings of each variety were served as controls, and they were used as referent values. Specific events of the OJIP transient in the OK, OJ, JI, and PI phases were shown as differences in the variable fluorescence and presented as \(\Delta\)V\({}_{\text{OP}}\), \(\Delta\)V\({}_{\text{OK}}\), \(\Delta\)V\({}_{\text{OI}}\), \(\Delta\)V\({}_{\text{JI}}\), and \(\Delta\)V\({}_{\text{IP}}\), normalized to corresponding controls [61]. Each curve represents the average of seven measurements (\(n=7\)) per treatment and variety. The total driving forces (DF\({}_{\text{total}}\)) of the total photosynthetic electron transport, shown as log PI\({}_{\text{total}}\), were summed up by the corresponding partial driving forces: log \(\gamma_{\text{RC}}/(1-\gamma_{\text{RC}})\), log \(\varphi_{\text{PO}}/(1-\varphi_{\text{PO}})\), log \(\psi_{\text{EO}}/(1-\psi_{\text{EO}})\), and log \(\delta_{\text{RO}}/(1-\delta_{\text{RO}})\) [62]. The difference for the control and the drought and/or recovery, respectively, \(\Delta\)DF\({}_{\text{total}}\), was calculated as \(\Delta\)DF = DF\({}_{\text{drought/recovery}}-\text{DF}_{\text{control}}\).
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 2 Hydrogel Materials
### Natural Hydrogels
#### 2.2.3. Alginate  
Alginate is a naturally occurring anionic polysaccharide commonly extracted from seaweed, composed of alternating blocks of mannuronic acid and guluronic acid groups, which are called the M unit and G unit, respectively [93]. The ratio of M and G units and the sequence significantly influence the overall characteristics of alginate hydrogels. Alginate hydrogels are commonly highlighted for their biocompatibility, biodegradability,and minimal toxicity and have been widely used in pharmaceutical and biomedical fields (Table 2) [93; 94; 95].  
Generally, alginate hydrogels are synthesized via ionic crosslinking. In the presence of divalent cations such as calcium (Ca2+) or magnesium (Mg2+), the G blocks of adjacent alginate chains bind together to form stable crosslinking points, yielding a three-dimensional network of alginate molecules [93; 96]. This gelation process can occur under physiological conditions, which makes it highly compatible with the applications that require the preservation of bioactivity or encapsulation of cells or proteins. Alginate can be covalently crosslinked using a similar group of crosslinking agents like glutaraldehyde, genipin, oruronic acid [97].  
The versatility of alginate hydrogels is emphasized most with its wide range of tunability, including mechanical strength, degradation mechanism, bioactivity, and biochemical modification with peptides or growth factors. Alginate hydrogels are widely utilized in drug delivery systems, owing to their capacity to encapsulate therapeutic agents within their matrix and release them in a controlled manner [23; 94; 95; 98; 99]. Additionally, the gentle gelation process offers great potential toward the injectable formulation of alginate, which allows the implantation of functional hydrogel at a desired site in the body with minimal invasive procedures [100; 101; 102].  
Interestingly, this gentle gelation process makes alginate the most suitable for 3D bioprinting technologies, since it can stay as a relatively low-viscosity solution and quickly turn into a soft gel when a divalent cation is introduced, enabling the precise fabrication of alginate-based scaffolds with complex architectures for tissue and organ regeneration [103; 104; 105]. However, one significant drawback of alginate hydrogel is the limited control over the rate of biodegradation since environmental heterogeneity and a diverse involvement of divalent cations, such as calcium and magnesium, facilitate various biological processes within the human body [103; 104; 88; 106]. This unpredictability becomes more critical for the application of alginate hydrogels in the circulatory systems.
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 1 Introduction
Therefore, many researchers have proposed methods for optimizing SVM parameters (Table 1). For instance, [PERSON] et al. [16] combined an SVM model with a PSO algorithm to predict man-hours in aircraft assembly. The forecasting results indicated that the PSO-SVM model was significantly better than the BPNN model. [PERSON] et al. [17] suggested applying the PSO-SVM hybrid model to predict the risk of the expressway project. The prediction results showed that the proposed model was more accurate and better than the traditional SVM model. [PERSON] et al. [18] used PSO-SVM, grid search (GS)-SVM models to predict steel corrosion. Compared with the GS-SVM model, the results showed that the PSO-SVM steel corrosion prediction model was more accurate. Additionally, [PERSON] et al. [19] proposed the use of a genetic algorithm (GA) to optimize an SVM model. The overall results indicated that the GA is an excellent optimization algorithm for increasing the prediction accuracy of an SVM. Figure 1: Differences in planned and actual lead times. In the landslide groundwater levels prediction field, the GA-SVM model was proposed by [PERSON] et al. [20]. The results showed that the GA-SVM model can understand the relationship between groundwater level fluctuations and influencing factors well. Moreover, other researchers combined other meta-heuristic algorithms such as the bat algorithm (BA) and the grasshopper optimization algorithm (GOA) with SVMs, and obtained good results [21; 22]. Unlike other studies, this paper proposes the application of a new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (NHPSO-JTVAC) algorithm, an advanced version of the PSO algorithm, to optimize the parameters in an SVM. Moreover, the NHPSO-JTVAC-SVM model is proposed to predict the lead time in the shipyard's block assembly and pre-outfitting processes. The remainder of this paper is organized as follows: Section 2 introduces the algorithm principle of the hybrid predictive model. Section 3 describes the construction of the model. Section 4 discusses the experimental results, and Section 5 summarizes and concludes the paper.
## 3 Properties of Different STBCs Signals
### Second STBC Option, \(\alpha=c_{1}\)  
The broadcast matrix demonstrated in (1b) shows that the sent sequences from antennas \(q=0\) and \(q=1\) of STBC \(\alpha=c_{1}\) can be respectively written as  
\(\mathbf{W}_{q=0}^{(\alpha=c_{1})}=\!\!\left[d_{0}^{(0)},-d_{1}^{(1)*},\cdots, d_{0}^{(b)},-d_{1}^{(b)*},\cdots\right]\) and \(\mathbf{W}_{q=1}^{(\alpha=c_{0})}=\!\!\left[d_{1}^{(0)},d_{0}^{(1)*},\cdots,d_ {1}^{(b)},d_{0}^{(b)*},\cdots\right]\). Accordingly, one writes  
\[\mathrm{E}\Big{[}\overline{w}_{q}^{(\alpha=c_{1})}(n)\overline{w }_{q^{\prime}}^{(\alpha,c_{1})}(n+\tau)\Big{]}= \tag{9}\] \[\left\{\begin{array}{rl}\sigma_{s}^{2}&\text{for $(q=0,q^{ \prime}=1,\tau=1)$},\\ -\sigma_{s}^{2}&\text{(for $q=1,q^{\prime}=0,\tau=-1)$},\\ 0&\text{otherwise},\end{array}\right.\]  
and using (3), (5), and (9) in (6), we write that  
\[U^{(\alpha=c_{1})}(\tau)= \tag{10}\] \[\left\{\begin{array}{rl}\frac{1}{2}\sigma_{s}^{2}\Big{(}h_{ \mathcal{S}\mathcal{D}}^{(0,1)}h_{\mathcal{S}\mathcal{D}}^{(1,2)}-h_{\mathcal{S }\mathcal{D}}^{(1,1)}h_{\mathcal{S}\mathcal{D}}^{(0,2)}\Big{)}&\text{for $\tau=\pm 1$},\\ 0&\text{otherwise},\end{array}\right.\]
## 4 Initial Trajectory Generation
### Unconstrained Polynomial Trajectory Generation with Collision Avoidance  
[PERSON] et al. [37] extended what [PERSON] [35] proposed for adding support for collision avoidance capabilities. They added an additional term for calculating the collision cost:  
\[\begin{gathered} J(\Gamma)=\xi_{obs}J_{obs}(\Gamma)+\xi_{smooth} J_{smooth}(\Gamma),\\ J_{smooth}=\mathbf{d}_{f}^{T}R_{ff}+\mathbf{d}_{f}^{T}R_{fp} \mathbf{d}_{p}+\mathbf{d}_{p}R_{pf}\mathbf{d}_{f}+\mathbf{d}_{p}^{T}R_{pp} \mathbf{d}_{p},\end{gathered} \tag{23}\]  
where \(J_{smooth}\) exactly equals (21). To estimate \(J_{obs}(\Gamma)\), it is required to initially calculate position \(\mathbf{p}_{i}(t)\) (16) and velocity \(\mathbf{v}_{i}(t)\) for each axis at time \(t\) after selecting the corresponding segment (\(i,i=1,\ldots,m_{d}\)):  
\[\begin{gathered}\mathbf{p}_{i}(t)=Tp_{i},\quad p_{i}=[\lambda_{ 0},\lambda_{1},\ldots,\lambda_{d}]_{i}^{T},\quad T=[t^{0},t^{1},t^{2},\ldots,t^ {d}],\\ \mathbf{v}_{i}(t)=\dot{\mathbf{p}}_{i}(t)=TVp_{i},\\ \mathbf{p}_{i}(t)=[\mathbf{p}_{x}(t)\ \mathbf{p}_{y}(t)\ \mathbf{p}_{z}(t)]_{i}, \quad\mathbf{v}_{i}(t)=[\mathbf{v}_{x}(t)\ \mathbf{v}_{y}(t)\ \mathbf{v}_{z}(t)]_{i}.\end{gathered} \tag{24}\]  
Knowing (the values of) \(\mathbf{p}_{i}(t)\) and \(\mathbf{v}_{i}(t),J_{obs}(\Gamma_{i})\) can be fully determined by:  
\[\begin{gathered} J_{obs}(\Gamma_{i})=\int_{S}c(\mathbf{p}_{i}(t ))ds=\int_{t=0}^{t^{d}}c(\mathbf{p}_{i}(t))\|\mathbf{v}_{i}(t)\|dt=\sum_{t=0}^ {t^{d}}c(\mathbf{p}_{i}(t))\|\mathbf{v}_{i}(t)\|\Delta t\\ \frac{\partial J_{obs}(\Gamma_{i})}{\partial d\mathbf{p}_{i}(t)}= \sum_{t=0}^{t}\|\mathbf{v}_{i}(t)\|\bigtriangledown_{i}c(T(C^{-1}S)_{pp}) \Delta t+c(\mathbf{p}_{i}(t))\frac{\mathbf{v}_{i}(t)}{\|\mathbf{v}_{i}(t)\|} TV(C^{-1}S)_{pp}\Delta t,\end{gathered} \tag{25}\]  
where \((C^{-1}S)_{pp}\) is the right-side matrix, which corresponds to \(\mathbf{d}_{p}\). For representing the collision cost \(c(\mathbf{p}_{i}(t))\), a line integral of a potential function, i.e., (44), was used. As total cost is given (21), \(J_{obs}(\Gamma)\) can be calculated for all the segments provided that \(\mathbf{d}_{p}^{*}\) can be estimated. In a cluttered environment, optimization problem is most likely to be nonlinear as well as nonconvex. Thus, Broyden-Fletcher-Goldfarb-Shanno (BFGS) [75] was used to solve the optimization problem. However, the solver failed to obtain the global minimum most of the time. Hence, several random restarts were needed to find the optimal solution. A thorough discussion of how random restarts were invoked into the optimization problem was detailed in [45].
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 4 Problem Formulation
### Tradeoff between Data Freshness and Fidelity
#### 4.3.3 Monotonicity of the Weighted-Sum Tradeoff  
Since the average AoI \(\overline{\Delta}(N)\) monotonically increases w.r.t. \(N\), and \(\overline{D}(N)\) monotonically decreases w.r.t. \(N\). The conclusion can be drawn that the weighted-sum is most time a unimodal function that decreases first and increases then, even it may fluctuate sometimes, we can still treat the first local optimum as global optimum since it saves system cost for deploying sensors. The monotonicity analysis paves the way for finding a sub-optimal number of sensor nodes which we will elaborate on in the next section.
## 3 Aqueous Film Forming Foam (AFFF)
### Early Research  
An early attempt to detect and quantify AFFF contamination on military bases was conducted by [PERSON] et al., (2004) [62]. The study sought to detect and quantify 4:2, 6:2, and 8:2 fluorotelomer sulfonates from groundwater at 3 military bases (Naval Air Station Fallon, NV, USA; Tyndall Air Force Base, FL, USA; Wurtsmith Air Force Base, MI, USA) [62]. LC/MS-MS was utilized as a means of separation and detection of AFFF in human serum, animal tissue, and surface water samples. This method has become a common technique used in many following studies [64; 67; 68; 70]. In addition to the detection and quantification of AFFF components, [PERSON] utilized fast atom bombardment mass spectrometry (FAB-MS) to also identify possible sources of the fluorotelomer sulfonate contamination observed. In terms of total fluorotelomer sulfonate concentrations, none were detected in at the Nevada site. At the Florida site, the concentrations ranged from 1100 to 14,600 \(\upmu\)g/L, while in Michigan, it ranged from 0.60 to 182 \(\upmu\)g/L. [PERSON] identified that fluoroalkylthioamido sulfonates were the main anionic fluorosurfactant in the AFFF concentrates and not fluorotelomer sulfonates as previously thought. Despite their abundance within the AFFF concentrate, fluoroalkylthioamido sulfonates were absent from groundwater samples indicating their degradation into fluorotelomer sulfonates. These results confirmed the complexity and relative lack of knowledge surrounding the biodegradation and transport of fluorotelomer sulfonates in groundwater.
# Exercise Suppresses Appetite in Obesity: A Biochemical, Metabolic, and Molecular Approach
## 3 Exercise and Appetite-Reducing Metabolites, Myokines, and Hepatokines
The injection of GDF15-expressing tumors in mice reduced body weight and energy intake and was attributed to decreased and increased levels of hypothalamic NPY and POMC mRNAs, respectively, as well as downstream signaling by leptin, which upregulates STAT3 in hypothalamic neurons [163; 296; 309; 310] (Figure 3). Thus, GDF15 impacts food intake and, consequently, body weight by modulating central neuropeptides and potentiating signaling by other appetite-regulating hormones. Reduced food intake and appetite are not the only mechanisms by which GDF15 mediates the effects of exercise on body weight. A correlation was observed between the reduction in abdominal fat mass and the upregulation of GDF15 in inactive individuals with obesity after 12 weeks of treadmill/cycling aerobic training [276]. Long-term GDF15 administration and overexpression induce weight loss characterized by selective fat mass reduction without lean mass depletion [311; 312; 313]. Long-term increases in GDF15 levels are associated with secondary adaptations related to energy expenditure and the expression of thermogenic and lipolytic genes, such as increased expression in uncoupling protein 1 (UCP1) mRNA in adipose tissues [314; 315], browning of white adipose tissue [316], and elevated expression of \(\beta\)-oxidative, thermogenesis, and lipolytic genes, such as adipose triglyceride lipase (Atgl), hormone-sensitive lipase (Hsl), and \(\beta\)-3 adrenergic receptor (Adrb3) [314; 317]. Such increases in lipolytic genes in adipose tissue occurred in transgenic mice overexpressing GDF15 that resulted in reduced body fat [318]. These effects suggest that the GFRAL receptor may be expressed in adipose tissues so that GDF15 could exert its effects. The upregulation of Atgl gene expression by GDF15 in adipose tissue is abolished in GFRAL-deficient mice [319]. GFRAL receptors are extensively expressed in both subcutaneous and abdominal adipose tissues [301; 317]. In summary, GDF15 reduces body weight by acting in the CNS to suppress appetite and energy intake and stimulates lipolysis in adipose tissues.