esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Offline Optimization and Adaptation Under Nonlinear Task Constraints
### Optimization and Adaptation Procedure  
#### 3.3.1 Optimization Procedure  
Based on the Lagrangian function in Equation (16) and constraints function defined in Section 3.2, we concretize the optimization procedure to obtain the optimal parameters. For notational brevity, we use the notation \(\mathbf{\theta}_{0}=\left(\mathbf{\mu}_{t}^{0},\mathbf{\sigma}_{t}^{0}\right)\) and \(\mathbf{\theta}=\left(\mathbf{\mu}_{t},\mathbf{\sigma}_{t}\right)\) to denote the intial parameters and optimized parameters. We can rewrite the Lagrangian function in Equation (16) as follows:  
\[L\big{(}\mathbf{\theta},\big{\{}\lambda_{k,t}\big{\}}\big{)}=\text{D}_{\text{KL}}[ \mathbf{\theta}||\mathbf{\theta}_{0}]+\sum_{k,t}\lambda_{k,t}\Big{(}\alpha_{k,t}-F_{ \alpha_{k}}(\mathbf{\theta})\Big{)} \tag{30}\]  
The term \(\text{D}_{\text{KL}}[\mathbf{\theta}||\mathbf{\theta}_{0}]\) denoting the KL divergence between two univariate Gaussian distributions is further derived by:  
\[\text{D}_{\text{KL}}[\mathbf{\theta}||\mathbf{\theta}_{0}]=-\frac{1}{2}+\log\Big{(} \sigma^{0}\Big{)}-\log(\sigma)+\frac{\sigma^{2}+\big{(}\mathbf{\mu}-\mathbf{\mu}^{0} \big{)}^{2}}{2\left(\sigma^{0}\right)^{2}} \tag{31}\]  
We express the optimized parameters derived in Equation (17) as follows:  
\[\left(\mathbf{\theta}^{*},\Big{\{}\lambda_{k,t}^{*}\big{\}}\right)=\underset{ \lambda_{k,t}\geq 0}{\text{max}}\ \underset{\mathbf{\theta}}{\text{min}}\ L\big{(}\mathbf{\theta},\big{\{} \lambda_{k,t}\big{\}}\big{)} \tag{32}\]  
To solve the problem, we utilize a double-loop algorithm based on the gradient ascent-descent method. For the outer-loop, we optimize \(\lambda_{k,t}\) with the gradient quasi-ascent steps. The Exponential Method of Multipliers (EMM) [41] method is suitable to solve the inequality constraints of the Lagrangian function via exponential factors. We can express the optimized \(\lambda_{k,t}\) after each iteration as \(\lambda_{k,t}^{(s+1)}=\lambda_{k,t}^{(s)}\cdot\exp\Big{\{}\eta_{k}\partial_{ \lambda_{k,t}}L\Big{\}}\), where \(\eta_{k}\) decides the convergence speed; the gradient of \(\big{\{}\lambda_{k,t}\big{\}}\) is obtained by \(\partial_{\lambda_{k,t}}L=\alpha_{k,t}-F_{\alpha_{k}}(\mathbf{\theta})\). For the inner-loop, we optimize \(\mathbf{\theta}\) with the gradient descent method. The LBFGS method [42] is tailored for solving nonlinear optimization problems with low computing and memory costs. We perform several LBFGS steps in \(\mathbf{\theta}\), with the gradient \(\partial_{\mathbf{\theta}}L\) computed using the automatic differentiation frameworks in Tensorflow [43]. The convergence conditions are checked after each iteration of LBFGS to speed up convergence. After we obtain the optimized \(\big{(}\mathbf{\mu}_{t}^{*},\mathbf{\sigma}_{t}^{*}\big{)}\), we directly perform several LBFGS steps to obtain the optimized \(\big{(}\mathbf{\mu}_{\mathbf{w}}^{*},\mathbf{\Sigma}_{\mathbf{w}}^{*}\big{)}\) based on Equation (18).
## 4 Numerical Example
### Calibration of the Algorithm  
The main challenge of adapting the WOA methodology is to find a balance between the algorithm performance and the processing time. In that sense, multiple numerical simulations were carried out for the case study, using the objective function J1 and the Kobe excitation. Alternatives of 10, 30, 50, 100, 200, and 500 whales combined with 5, 10, 30, and 50 iterations were considered. Figure 3 depicts the performance index (PI), defined as the ratio between the maximum controlled and uncontrolled response of the system, and the processing time for every simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Record** & **Event Name** & **Station** & **Component** & **PGA [g]** & **Duration [s]** \\ \hline
1 & El Centro & El Centro & S00E & 0.35 & 53.73 \\
2 & Kobe & Takatori & 000 (CUE) & 0.61 & 30.03 \\
3 & Loma Prieta & Capitolia & 90 DEG & 0.40 & 39.98 \\
4 & Northridge & Rinaldi & S49W & 0.84 & 14.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of the benchmark records.  
In the same way, the differences in the PI values of alternatives A and B compared with alternative C are 0.0119% and 0.0124%, respectively. Considering these data and taking into account the stability of the methodology by employing a larger number of generations, alternative B is adopted in this work for the determination of optimal design parameters of TMDs.
# Duty Factor Reflects Lower Limb Kinematics of Running
## 1 Introduction
This passive impact peak usually occurs at about 25 ms after initial ground contact and this event marks the middle of the passive peak impact attenuation (IA) phase [24; 25; 26; 27; 28; 29; 30; 31; 32]. Both passive and active impact peaks need to be attenuated and different strategies for impact attenuation have been observed [33; 34]. Forefoot strikers were shown to have a greater reliance on soft tissues (active mechanisms) [34; 35] whereas rearfoot runners were shown to more prominently attenuate the impact using passive mechanisms [24; 34], but also using active mechanisms [36; 37]. Indeed, the heel pad of the foot and skeletal tissue are absorbing most of the shock when rearfoot striking [24; 34]. In addition, the eccentric action of the knee extensor [37] and dorsiflexor [36] muscles during \(t_{c}\) were also shown to play a role. As for running with a forefoot strike pattern, the eccentric action of the plantar flexor muscles during \(t_{c}\), acting as a shock absorber, is required [25; 38; 39]. These impact attenuation strategies between forefoot and rearfoot strike patterns induce different loadings on the lower limb and different three-dimensional (3D) stress patterns in the ankle, knee, and hip joint [40; 41; 42], and different sagittal plane (hip, knee, and ankle) joint angles and pelvis and foot segment angles during stance [42; 43], but with no global advantage of one strike pattern over the other [40]. Following verbal instructions directing the runners towards a more compliant running stride, rearfoot strikers were able to adopt a higher degree of knee and hip flexions during \(t_{c}\)[44]. Similarly, \"Groucho\" running [45] shows global body flexion while \"Pose\" running [46] uses an extended posture albeit the greater knee flexion at initial contact. Therefore, these observations suggest the existence of global running patterns associated with specific lower limb kinematics. Hence, the classification of runners based on their \(\overline{\rm DF}\) showed the existence of two different self-optimized global running patterns [17]. However, nothing is known about their lower limb kinematics, particularly their sagittal plane (hip, knee, and ankle) joint angles and pelvis and foot segment angles during stance. Therefore, the purpose of this study was to identify the lower limb kinematics for both DF\({}_{\rm high}\) and DF\({}_{\rm low}\). As DF\({}_{\rm high}\) and DF\({}_{\rm low}\) favor long \(t_{c}\) and long \(t_{f}\) (therefore shorter relative \(t_{c}\)), respectively, we hypothesized that these two DF groups would rely on two different lower limb kinematic mechanisms. More specifically, we hypothesized that DF\({}_{\rm high}\) would use more lower limb flexion during \(t_{c}\) than DF\({}_{\rm low}\).
## 3 Instrumental Analytical Techniques
### Mass-Spectrometry not Coupled with GC  
MS is not exclusive to GC because it was an independently created technique; thus, in its evolution, various inventions disconnected from GC have been made, and it is remarkable that despite this fact, MS has contributed immensely to the investigation of key VOCs in this field of knowledge [118]. MS consists of an instrumental method that measures the mass of molecules or fragments from these by means of the mass-to-charge ratio (m/z) when ionized using an apparatus denoted as a mass spectrometer. This mechanism consists of three main sections: the ionizer (where the molecules are converted to particles with electrostatic charge), the analyzer (device that filters, separates and groups the charged particles according to their m/z), and the detector (in which the m/z from the particles coming from the analyzer is quantified) [119].  
Some of the most interesting applications of MS in research related to VOCs originating from insects are the superficial scanning of exoskeletons by DAPPI-MS (desorption atmospheric pressure photoionization mass spectrometry) aiming to characterize VOCs for defense produced in the glands, namely, from _Prohinoternes simplex_ soldier termites and from the beetle _Graphosoma lineatum_, achieving a topological picture of emissions [120]. This scope was also employed on _Drosophila melanogaster_ with AP-SMALDI (atmospheric pressure scanning microprobe matrix assisted laser desorption/ionization) for metabolite characterization [121] in which the study was addressed to hydrocarbon-chain compounds and there was interest in pheromones with such structure. Other useful applications of MS have been reported for aphrodisiac pheromones of the small cabbage white butterfly (_Pieris rapape_) for release monitoring by means of cDART-MS (confined direct analysis in real-time mass spectrometry) [44], which is a portable MS method and is ideal for assessment in field work [122].  
In the investigation of VOCs originating in plants, a remarkable example is a study that employed direct ambient corona discharge ionization mass spectrometry for the characterization of volatiles coming from flowers of selected species (_Photinia serntata_, _Castanopsis sclerophylla_ and _Stemona japonica_) with insect semen-like odor without any further sample preparation. Such research found some compounds that were not previously identified by GC-MS for the species under investigation [123].
# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
### Innovation Points  
Neural networks, such as BP and LSTM, address some of these limitations by capturing nonlinear relationships, yet their predictive accuracy can be further enhanced through data preprocessing and parameter optimization. Recent research highlights the strong potential of hybrid models (e.g., VMD-SSA-LSTM) for forecasting complex, non-stationary data like construction material prices, offering the industry more accurate and reliable predictive tools. Variational Modal Decomposition [29] (VMD) is a powerful tool used for signal decomposition, effectively separating noisy components from meaningful data. The Sparrow Search Algorithm [30] (SSA) is a bio-inspired optimization technique that fine-tunes model parameters, reducing overfitting and improving convergence. When combined with LSTM, these methods create a robust framework capable of handling complex, non-stationary time series data. In this paper, examples are presented to validate the prediction accuracy and effectiveness of the VMD-SSA-LSTM model. The experimental results show that the VMD-SSA-LSTM model proposed in this paper significantly outperforms the traditional BP, LSTM, and VMD-LSTM models in terms of prediction accuracy. Especially in the prediction of highly volatile data, the model demonstrates stronger stability and reliability, providing an effective tool for engineering cost management.
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 4 Results
### Conclusions  
The central predictive vehicle dynamics control fulfills the defined control objectives as desired. A summary of the control quality is given in Table 2. In addition to this, the results are compared with those of a vehicle with passive chassis elements and those obtained by a conventional control approach.  
The main control objective of the central predictive vehicle dynamics control, the active roll stabilization, is met with an excellent control quality. In comparison to the conventional control approach, the control quality is improved by approximately 67.08%. Likewise, good results are achieved with regard to the further subordinate control objectives. As a result, pitching movements are reduced by approximately 19.75% compared to the conventional control approach. Despite the intervention of the control system, the self-steering behavior of the controlled vehicle corresponds to that of the passive vehicle.  
The validation demonstrates the advantages of a centralized predictive control structure over a passive vehicle and a vehicle using a conventional control approach comprising a proportional integral derivative controller and a skyhook damping. The improvements in the control quality by of a non-linear model-based predictive control compared to a linear control approach utilizing a skyhook damping for a pure active roll stabilization are also presented in [33].
# Empowering Advanced Parametric Modes Clustering from Topological Data Analysis
## 2 Methods
### On the Surface Topology
#### 2.2.2 Features' Filtration  
To describe the appearance and disappearance of the features of \(\mathbb{M}\) across different scales, we consider the so-called _Alpha Filtration_. For that purpose, an interval \([\alpha_{\min},\alpha_{\max}]\) is considered. It reflects the smallest features' scale, in our case \(\alpha_{\min}=0\) (the vertex) and the largest one \(\alpha_{\max}\) representing the largest distance between points of \(\mathbb{M}\).  
The features of \(\mathbb{M}\) considered here are elements of the _Simplicial complex_ associated with \(\mathbb{M}\), noted \(S(\mathbb{M})\), and constructed from the finite cells of the the Delaunay Triangulation of the set of points in \(\mathbb{M}\). The elements of \(S(\mathbb{M})\) are the geometric features of \(\mathbb{M}\) i.e., tetrahedrons, triangles, edges, and vertices.  
In order to describe efficiently the elements of the _Simplicial complex_\(S(\mathbb{M})\), and map a set of \(d\)-dimensional simplices to a set of \((d-1)\)-dimensional simplices, we construct a finite sequence of sets of \(d\)-dimensional simplices, denoted by \(S_{d}(\mathbb{M})\), \(d=0,1,2,3\).  
Set \(S_{0}(\mathbb{M}):=\{[x_{m}]:x_{m}\in\mathbb{M}\}\); then, the simplices in \(S_{d}(\mathbb{M})\) for each \(d=1,2,3\) are obtained from the simplices in \(S_{d-1}(\mathbb{M})\) taking into account the following two properties:  
1. For every simplex in \(S_{d}(\mathbb{M})\), the \((d-1)\)-dimensional simplices forming it are in \(S_{d-1}(\mathbb{M})\) (e.g., a triangle is in \(S_{2}(\mathbb{M})\) and its three edges are in \(S_{1}(\mathbb{M})\));
2. If two simplices in \(S_{d}(\mathbb{M})\) have a common element \(\sigma\), then there exists \(0\leq l\leq(d-1)\) such that \(\sigma\in S_{l}(\mathbb{M})\). Given the scale values \((\alpha_{i})_{j=0}^{m}\), the _Alpha Filtration_ is then a non-decreasing sequence describing the evolution of the features of the simplicial complex \(S(\mathbb{M})\) at each scale \(\alpha_{j}\), and computed as follows: * First, the filtration value of each tetrahedron is computed as the circumradius of the tetrahedron if its circumsphere is empty, and as the minimum of the filtration values of the triangles that are within the circumsphere otherwise. * Similarly, the filtration value of each triangle is computed as the circumradius of the triangle if the circumcircle is empty, and as the minimum of the filtration values of the segments that are within the circumcircle otherwise. * Then, the filtration value of each segment is computed as its circumradius. * Finally, the filtration value of the vertices is set to 0. The discrete values used for the radii are the \(\alpha_{j}\), and all simplices that have a filtration value larger than \(\alpha_{\max}\) are discarded. The time complexity of the algorithm is \(\mathcal{O}(n^{2})\). The choice of the _Alpha Filtration_ was motivated by its relatively much smaller size compared to other filtrations. A detailed definition and implementation are provided in [17; 18].
## 4 Discussion
The findings of this study are of relevance for the growing interest of governments, companies and the scientific community in CE. The study can support policy makers with scientific insights about the environmental opportunities and risks related to CE textile products. Furthermore, the results are directly useful for textile companies in understanding that CE practices are not per se more environmentally sustainable than today's existing practices. Nevertheless, they have the potential to lower impacts significantly if the entire product system--starting from the design of the textile--is aligned to CE principles. For the scientific community, this study gives a first glimpse of the possible risks involved in CE practices for the textile industry, such as the accumulation of hazardous substances. Further research is needed to identify all major risks inherent in CE production processes for textiles and how technology can advance to overcome these challenges.
## 3 Results and Discussion  
Extraction based on CPE for different analytes has many features such as a high enrichment factor, easy conjunction to automated instruments, simplicity of use, and rapidity. In the presented work, methyl 4,20-diisobutyl-2,5,8,16,19,22-hexaoxo-7,17-dipropyl-3,6,9,15,18,21-hexaaza-1(2,6)-pyridinacyclodoc-osaphane-10-carboxylate (DLNL) is presented as a chelating agent for Pb\({}^{2+}\) ions forming a stable 1:1 complex. The complex was extracted into surfactant-rich phase containing Triton X-114. To verify the optimum conditions of the complex formation and its extraction of the complex, the performance features such as pH, concentrations of ligand and surfactant, temperature, and time of incubation were evaluated.  
Figure 1: Chemical structure of methyl 4,20-diisobutyl-2,5,8,16,19,22-hexaoxo-7,17-dipropyl-3,6,9, 15,18,21-hexaaza-1(2,6)-pyridinacyclodocosaphane-10-carboxylate (DLNL).
###### Abstract  
Mobile crowdsensing (MCS) has recently emerged as an urban-sensing paradigm that takes advantage of smartphone sensing capabilities and user mobility. A major challenge in mobile crowdsensing-based urban sensor networks is how to efficiently transfer data from sensors to the sink (e.g., the server center). Therefore, this study proposes a human location prediction-based routing protocol (HLPRP) in such networks. Specifically, a human location prediction (HLP) model is designed to estimate the location of mobile nodes. The proposed HLP model is based on a recurrent neural network with long short-term memory cells. The movement history of each person is used in the HLP model to predict their future locations. Experimental results on real traces are used to validate the proposed HLP model. Then, using predicted location information from the HLP model, packet delivery predictability is obtained. Packet delivery predictability represents the possibility that a node will deliver a packet to its destination and is used to select optimal relay nodes to maximize the packet delivery ratio, minimize the packet delivery cost, and reduce delivery latency. In addition, the proposed routing protocol considers social strength for relay selection. To evaluate the HLPRP, we conduct simulations and compare results with other routing protocols, showing that the HLPRP can outperform existing protocols.  
1  
Human location prediction; social relationship; packet delivery predictability; social strength; routing protocol
# Impact of Frost on the Morphology and Chemical Composition of _cv_. [PERSON]
###### Abstract  
Frost events and extreme weather phenomena greatly affect several characteristics of the olive fruit. This study evaluated the impact of frost on the morphology, composition (moisture, fat, fatty acids, tocopherols, and total phenolic contents), and antioxidant activity of olives of _cv_. Santulhana. A total of 14 trees from the same geographical region (Santulhao, northeast of Portugal) were chosen, including trees subjected or not subjected to frost conditions (n = 7 each). The results showed that frost led to morphological changes in olive fruits, particularly in terms of weight and diameter, which were imposed by a huge decrease in the moisture content (\(-20\%\)). Fat relative content increased as a consequence of the water loss (+29% in fresh pulp weight), with a slight reduction of the relative abundance of saturated fatty acids (\(-4\%\)) and tocopherol contents in the fat (\(-17\%\)). However, the total phenolic contents and antioxidant activity were severely affected (\(-70\%\) and \(-42\%\), respectively), with potential consequences for the olive oil stability and sensorial attributes. Principal component analysis showed that both morphological and chemical parameters could be used as biomarkers to identify olives subjected or not subjected to frost. The overall negative impact of frost on the minor antioxidant contents of _cv_. Santulhana olives may anticipate a quality loss of olive oils extracted from olives affected by frost.  
frost; _cv_. Santulhana; fruit morphology; fatty acids profile; bioactive compounds
## 4 Dependency Relation and Dependent Shrink  
The firing frequency of a certain transition can be calculated if the firing frequencies of the transitions attached to that transition have been determined thus far, i.e., when these transitions are dependent on each other with respect to the firing frequencies. To provide a clear and precise definition of this dependency of the transitions, we introduce a new concept called a \"dependency relation\" for transitions, which is formalized based on the retention-free concept presented in the previous section. After that, we propose a method for shrinking a Petri net subnet, induced by the transitions satisfying a dependency relation, into a single transition. In other words, if a subnet can be shrunk, then the firing frequency of any transition in the subnet can be deduced based on the firing frequency of a certain transition inside the subnet.
## 3 Results and Discussions
### Output Characteristics under High Pressure  
In subsequent experiments of pressure measurement range, two devices with different diaphragm thicknesses, 40 and 50 \(\upmu\)m, were tested. The V-P curves of the two devices are presented in Figure 6. As depicted, the device with the thinner diaphragm showed a higher sensitivity but with higher nonlinearity over a wide pressure range. Gratifyingly, the two pressure sensors were operated up to 5 MPa with an acceptable linearity. In order to evaluate the performance of the silicon pressure sensors, the main characteristics of tested devices are presented in Table 1, and the comparison among tested devices and the other pressure sensor is presented in Table 2. As can be seen, the increase of ambient temperature leads to the decrease of the full-scale output and sensitivity of the pressure sensor, and the increase of its nonlinearity. The micro sensor is designed for harsh applications; therefore, the thickness of the sensing diaphragm is increased to enhance the performance under high pressure. Note that the increase of diaphragm thickness leads to decreased full-scale output, nonlinearity, and sensitivity of the pressure sensor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Units** & & & **Tested Devices** & \\ \hline Operation & \({}^{\circ}\)C & 25 & 125 & 25 & 25 \\ Temperature & \({}^{\circ}\)C & 25 & 125 & 25 & 25 \\ Thickness of & & & & & \\ Diaphrag & \(\upmu\)m & 50 & 50 & 40 & 50 \\ Excitation Voltage & V & 3.3 & 3.3 & 3.3 & 3.3 \\ Pressure Range & MPa & 0–3 & 0–3 & 0–5 & 0–5 \\ Full-Scale Output & mv & 69.28 & 55.23 & 167.87 & 117.1 \\ Sensitivity & mv/MPa & 22.99 & 18.52 & 33.04 & 23.55 \\ Nonlinearity & F.S.\% & 0.25 & 0.31 & 0.48 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the tested devices under different test conditions.  
Figure 6: Output voltage of the sensor with different diaphragm thickness.