esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Technical Implementation Principles  
The system's architecture was designed according to the principle of separating concerns [20], covering three developmental aspects: data collection, front-end display, and back-end processing (see Figure 2). Developers working on the data collection module managed external interfaces, while front-end developers focused on user experience and interface design, and back-end developers handled data processing, algorithm optimization, and secure storage.  
The first step of the technical implementation pathway comprises recording classroom teaching using high-resolution cameras, with video streams subsequently transmitted in a stable manner to the processing module on the cloud server via Real-Time Streaming Protocol (RTSP). The CogVLM2-Video neural network model automatically recognizes video content and classified actions, and the evaluation model processes the results, which are then displayed on the front-end interface. Multiple cameras are strategically placed in key classroom locations to achieve comprehensive coverage of teacher and student behaviors and their interaction behaviors.
## 2 Materials and Methods
### HSP Distance to Characterize Monomer Solubility  
During capsule synthesis, it is essential to characterize the solubility of the monomers in the fragrance mixture since the solubility impacts the transfer of monomers, which, in turn, impacts the quality of the capsule's properties. The solubility of monomers is determined using the Hansen solubility parameters (_HSP_), which consider the combined effects of dispersion forces (\(\delta_{d}\)), polar forces (\(\delta_{p}\)), and hydrogen bonding forces \((\delta_{h})\)[22]. For each of the monomer mixtures employed, the distance in Hansen solubility parameters between the encapsulated fragrance mixture and the monomer mixture itself (_HSP Distance_) is defined via Equation (1):  
\[HSP\ Distance=\sqrt{4\big{(}\delta_{d_{M}}-\delta_{d0}\big{)}^{2}+\big{(} \delta_{p_{M}}-\delta_{p0}\big{)}^{2}+\big{(}\delta_{h_{M}}-\delta_{h0}\big{)} ^{2}} \tag{1}\]  
where \(\delta_{dM}\), \(\delta_{pM}\), and \(\delta_{hM}\) are the mass-weighted _HSP_ parameters for the monomer mixture, while \(\delta_{d0}\), \(\delta_{p0}\), and \(\delta_{h0}\) are the mass-weighted _HSP_ parameters for the fragrance mixture. The lower the distance in the Hansen space between the two mixtures, the higher the degree of miscibility.  
The HSP parameters were calculated via HSPiP 5.4.08 software.  
To preserve the fragrance composition's confidentiality, each sample's _HSP Distance_ was normalized to the _HSP Distance_ of a reference sample (CAP1110101), which was defined in Table 2.
## 1 Introduction
Algorithmic and Theoretical Advancements: A novel algorithm is developed to solve the optimization problem posed by FPLS. Theoretical analysis confirms the convergence properties of the model and establishes the equivalence of prediction variances between FPLS and traditional PLS, ensuring stable predictive performance. Through theoretical analysis, we elucidate the relationship between the filter length and the original objective function, providing guidelines for selecting appropriate filter length. 3. Bounds on Prediction Metrics: We explore the interplay between RMSEP and CS, deriving theoretical upper and lower bounds for both metrics. This analysis offers a deeper understanding of how changes in one metric affect the other, facilitating more informed model evaluation. 4. Experimental Results: Extensive experiments conducted on four diverse spectral datasets--Corn, Octane, Mango, and Soil Nitrogen--demonstrate that FPLS achieves superior prediction accuracy compared to existing methods. The consistency between our theoretical findings and empirical results underscores the practical value of FPLS. The remainder of this paper is organized as follows. In Section 2, we review related work. In Section 3, we formulate the filter learning-based PLS model and provide an efficient algorithm for solving the proposed model. Additionally, we present an analysis of convergence, bound, and variance. In Section 4, the experiments and analysis conducted are provided. Finally, conclusions are drawn in Section 5.
## 3 Results
### Test Quality Parameters of the Prediction Methods  
#### 3.2.1 Tennis Recommendation Score  
Using the TRS (see Table 4), a total of 39 of the 174 test participants (22.4%) were predicted to become talented tennis athletes (selection rate). Overall, 135 participants (77.6%) did not receive a top-level assignment because it was assumed that they could not reach the top level due to their low individual recommendation values. With a selected threshold value of \(z_{TRS}\) = 1.3, 12 of the 16 later top performers (75%) were true-positives (sensitivity). One hundred and thirty-one participants (82.9%) who did not reach the top level were also correctly classified as low performers, that is, true negatives (specificity). In total, 143 of 174 (82.2%) children could be correctly identified as later top or low performers via this linear method. If the talent forecasts with the same prediction rate (22.4%) were expressed at random, only 4, as compared with 12, of the top performers and 122, as compared with 131, of the low performers would be correctly predicted, and the overall prediction quality would only be 72.4%, as compared with the value of 82.2% described above. If the percentage values for sensitivity and specificity were considered in combination, the benefit is more obvious. With a cutoff limit value of \(z_{TRS}\) = 1.3, the Youden Index amounts to a total of 157.9% (75% + 82.9%), thus representing a 57.9% improvement as compared with a random drawing. The Area Under ROC Curve was 0.852 (standard error = 0.048; 95% confidence interval: min = 0.759, max = 0.946).
# Use of Total Least Squares Adjustment in Geodetic Applications
## 1 Introduction
The problem of finding the correct geodetic datum is of utmost importance in the field of deformation analysis when comparing the coordinates of points in the geodetic network between different time epochs. The total least squares approach in the S-transformation has not yet been identified in the literature, and we believe not only is the topic appropriate, but our findings also indicate the possibility to identify the set of unstable points in the geodetic network, which are described in more details at the end of this article.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Invertebrates
#### 2.3.2 Mollusk  
Phylum Mollusca represents one of the largest and most diverse groups of marine animals, and within the phylum, clams, oysters, scallops, and mussels are some of the well-known invertebrates. Several bioactive compounds, including primary metabolites such as amino acids, nucleic acids, simple sugars, and lipids, as well as secondary metabolites, like alkaloids and terpenoids, have been isolated from molluscans [4]. These components have been proven to possess unique biological functions. As a result, they are used for the development of functional foods as well as an alternative medicine for various diseases. Among these organisms, mussel, namely _Mytilus_ and _Perna_, components have been extensively investigated by many researchers in recent decades. For example, peptides obtained from mussels (_Perna canalicus_) exhibited antioxidant and angiotensin-I-converting enzyme (ACE) inhibitory activity [101]. Moreover, [PERSON] and [PERSON] [102] reported that mussel (_Mytilus edulis_) by-products, including byssus threads, shell, and discarded meat, contained  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Bioactive Compound** & **Sources** & **Biological Activity** & **References** \\ \hline \multirow{3}{*}{Peptides} & _Disoderma_ sp., _Geodia_ sp., _Halichondria_, _Cylindrata_, _Theonella_ sp., _Microscleroderma_ sp., _Halichon nigra_, _Sidonops microspinosa_, _Cliona_ & Anticancer, antitumor, antiproliferative, antibacterial, antifungal, and immunosuppressive properties \\  & _ustifica_, _Chatria_ sp., and _Chatria_ sp. & _Antimicrobial properties_ & [94; 96] \\ \hline \multirow{3}{*}{Sterols} & _Halichondria mooriei_, _Taxodacia zumi_, _Dysilea_ & _Antimicrobial properties_ & [94; 96] \\  & _arenaria_, _Hyrtios eubamma_, and _Disidea pallecens_ & _Antimicrobial and antioxidant properties_ & [97; 98] \\ \hline \multirow{3}{*}{Phenols} & _Halichondria panacea_, _Zygonycale parishili_, and _Callyspongia diffusa_ & _Antimicrobial and antioxidant properties_ & [97; 98] \\ \hline \multirow{3}{*}{T terpenoids} & _Spongia officinalis_, _Luffarella variabilis_, _Cacospongia salaris_, _Phyllospongia foliascens_, _Sarcotragus_ sp., and _Prianos_ sp. & Anticancer, anti-inflammatory, antifungal, antibiotic, and Na, K-ATPase inhibitory activities & [94; 98; 99] \\ \hline \multirow{3}{*}{Alkaloids} & _Stylissa carteri_, _Hyrtios ercta_, _Monanchora_ sp., _Corticium_ sp., _Stylissa massa_, and & Anti-inflammatory and antiviral properties & [93; 100] \\  & _Halichondria okadai_ & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bioactive compounds in sponges.  
a wide range of valuable components like polyunsaturated fatty acids, proteins, minerals, pigments, and enzymes, among others.
## 4 Experiments
### Performance Comparisons  
To demonstrate the effectiveness of LWMD, we conduct experiments under CTC and AED frameworks, and Tables 1 and 2 provide experiment results. For the AED framework, we set the beam size as 5 for the beam search and find FLOPs of AED frameworks are obviously larger than CTC frameworks.  
Among the human-designed baselines, Conformer obtains the best performance, which demonstrates the effectiveness of combing the CNN module with the MHA module. Even though LiteTransformer and LightConv can obtain fewer parameters and FLOPs, there existed obvious performance drops compared with Conformer, which indicates they are not the best light-weight architecture choices for E2E ASR.  
Different from human-designed baselines, LWMD result in a better performance-speed trade-off. Compared with the best human-designed baseline, the searched architectures by LWAS can obtain much smaller parameter numbers and FLOPs with comparable performance both on CTC and AED frameworks. Compared with the random search results that adopt the same search space as LWAS, the performance gain of LWAS owns to our search algorithm. To be specific, the LWAS framework can obtain at least 1.77\(\times\), 1.79\(\times\) parameter reduction and 2.18\(\times\), 2.49\(\times\) FLOPs reduction under CTC and AED frameworks, respectively, which demonstrates the effectiveness of LWAS in searching light-weight architectures. Moreover, by incorporating LWAS with DSP, the complete LWMD platform can further reduce parameters and FLOPs of searched architecture by around 20% with a negligible performance drop. To sum up, these comparisons illustrate the promising ability of LWMD as a comprehensive E2E ASR model compression platform.
# Study on Load Distribution and Fatigue Elastic Life of Ball Screw under Ultimate Conditions
[PERSON] et al. generated and analyzed ball screw finite element models. They varied design parameters and obtained a comprehensive characterization of load distribution morphologies in ball screws [21]. [PERSON] et al. proposed a model for double nut ball screws, accounting for different deformations of the screw and geometric errors in balls and analyzed the effects of structural parameters on stiffness reliability [22]. While previous literature has provided detailed studies on the distribution of loads, there is a scarcity of research examining the fatigue elastic life of ball screws, and the effect of uneven ball forces has not been taken into account. Therefore, this article proposes an analytical method considering the nut's position and screw length and obtains the load of working balls. [PERSON]'s elasticplastic theory is used to study the plastic deformation of the raceway surface. Then, the fatigue elastic life under ultimate conditions is studied and verified by experimental results from other literature. Finally, the influence of screw length, nut position, and screw parameters on fatigue elastic life is studied. The research is significant to the optimal design and reliability improvement of ball screw pairs.
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
## 4 FEM Numerical Analysis  
### FEM Calibration and Verification  
The numerical model is created by ABAQUS, finite element method (FEM), based on the model test, as shown in Figure 10. The linear elastic model is used for piles, subsoil, and reinforcement. The Mohr-Coulomb model is used for the embankment and cushion. The regular contact surfaces between the piles and subsoil adopt hard contact, and the tangential connection adopts penalty. The interaction between reinforcement and filling is embedded. Horizontal constraints are applied around the model, and vertical constraints are applied at the bottom of the model [26]. The material parameters are shown in Table 2, and the test program is shown in Table 3.  
The curves of the vertical stress obtained, which are distributed along the central axis of the embankment, by the FEM test M3 and the model test T3 are shown in Figure 11. There is some discrepancy between the two tests. The reasons are:  
1. The stiffness of soil pressure cells used in the model tests is more prominent than it of the soil, and its diameter is 78 mm. This results in a concentration of stress above the soil pressure cells. Due to the aforementioned factors, the measured results of soil pressure cells are too large.
2. It can be seen from Figure 11 that the measurement discrepancy is slight when the distance from the embankment bottom exceeds 0.4 m, but not when the distance from the embankment bottom is less than 0.4 m. The soil pressure cells are shifted during the process of embankment filling, because of the relative movements between the embankment filling. Soil pressure cells' measurement accuracy is hampered.
3. The compressive properties of the medium-fine sand lead to a continuous decrease in the compressibility of the filling during compression. At the same time, the material's elastic modulus remains unchanged during the process of numerical simulation, which leads to the difference between the two tests.  
Figure 11: Verification of the finite element method.  
Although there are differences between the finite element method and model tests, both can reflect the internal stress redistribution of embankment caused by pile-subsoil differential settlement. And the height of vertical stress deflection in both tests is about 0.4 m. Therefore, the finite element method still has specific guiding significance for analyzing soil arching.
1 Institute of Lightweight Engineering and Polymer Technology, Technische Universitat Dresden, Holbeinstrasse 3, 01307 Dresden, Germany; [EMAIL_ADDRESS]  
2 Laboratory for Material and Joining Technology (LWF), Paderborn University, Pohlweg 47-49, 33098 Paderborn, Germany; [EMAIL_ADDRESS]  
2 Laboratory for Material and Joining Technology (LWF), Paderborn University, Pohlweg 47-49, 33098 Paderborn, Germany; julian.vorderbrueggen@lwf.  
materials [4]. These elements can be integrated into composites during or after the part manufacturing process. [PERSON] et al. [5] presented a process for thermoset matrix composites based on the integration of a metallic intermediate plate in the joining zone prior to the infiltration process. Subsequently, the weld was created, and finally, the whole assembly was infused with resin. [PERSON] et al. applied a flat weld insert into the composite during preforming, whereby the fibres were displaced by a pin in the welding area [6]. Subsequently, the component was infiltrated in a resin transfer moulding (RTM) process, excluding the welding zone.  
An example for the application of additional metallic elements after the composite part manufacturing process is described by [PERSON] et al. [7]. The composite joining part was pre-punched in the flange area and then additional steel doubler strips were positioned on the composite and bonded with adhesive. In the hole area, the additional steel doubler strips were then welded to the metal component creating a form fit between composite and steel. Other studies have investigated the principle of resistance element welding, which was developed for joining high-strength (e.g., ultrahigh-strength steel) and low-ductile materials (e.g., aluminium) in multi-material joints [8]. The additional steel element can be positioned in a pre-hole [9] of the composite component or directly inserted by a punching process and joined to the steel component by resistance welding [10]. [PERSON] et al. presented an approach to insert multiple metal layers (multilayer insert) locally into a composite using automated fibre placement [11]. These multilayer inserts lead to a reinforcement of the hole, into which a Flexweld(r) insert was inserted to join the composite to a steel component by resistance welding [12].  
These joining techniques allow the use of conventional spot-welding guns and the integration of composite components in production lines established for metals. Besides, they cause local damage to the reinforcing fibres, which results in reduced load bearing capacity as shown for moulded holes compared to drilled ones [13]. For this reason, [PERSON] et al. [14] developed a new type of weld insert with a head plate and pins, which is suitable for low-damage integration into thermoplastic composites. Despite the reduction of fibre damage, as no pre-hole is necessary, it has the disadvantage that the embedding of the weld insert is an additional process step. Therefore, the authors pursue a new approach, in which the weld insert is integrated during composite component manufacture in the compression mould without fibre damage.
# Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers
## 2 System Model and Problem Description
### Problem Formulation  
WDP encompasses both PA nonlinear modeling and DPD modeling, so we decompose the problem into two parts for a clearer explanation.  
In the following formulation, \(\mathbf{D}_{\text{tr}}\) denotes the training set, and N represents the sample length. The PA input and output signals are denoted by \(\mathbf{x}\) and \(\mathbf{y}\), respectively, both of which include I/Q components represented as \(\mathbf{I}_{x}\), \(\mathbf{Q}_{x}\), \(\mathbf{I}_{y}\), and \(\mathbf{Q}_{y}\). The sample spaces of the PA input and output signals are \(\mathbf{X}\) and \(\mathbf{Y}\). The function \(f_{\text{Mod}}(\cdot)\) represents the output of the PA nonlinear model, while \(f_{\text{DPD}}(\cdot)\) corresponds to the output function of the DPD model. The PA's output function is denoted as \(f_{\text{PA}}(\cdot)\). The expectation operator is represented by \(\mathbb{E}\)and \(\mathcal{L}_{\text{MSE}}\) refers to the mean squared error (MSE) loss function. The nonlinear output of the PA model is denoted by \(\widehat{\mathbf{y}}\), which is computed as  
\[\widehat{\mathbf{y}}=f_{\text{Mod}}(\mathbf{x}). \tag{1}\]  
After applying DPD, the cascaded model produces a linear output, represented as \(\overline{\mathbf{y}}\), which is given by  
\[\overline{\mathbf{y}}=f_{\text{PA}}(f_{\text{DPD}}(\mathbf{x})). \tag{2}\]  
Finally, \(A\) denotes the gain of the PA, which is computed as  
\[A=\frac{1}{N}\sum_{m=0}^{N-1}\frac{\sqrt{\mathbf{I}_{y}^{2}[n]+\mathbf{Q}_{y}^{2}[n]}} {\sqrt{\mathbf{I}_{x}^{2}[n]+\mathbf{Q}_{x}^{2}[n]}}. \tag{3}\]  
The dataset for WDP includes the input and output signals of the PA in each time step as  
\[\mathbf{D}_{tr}=\{(\mathbf{x}[n],\mathbf{y}[n])\}_{n=0}^{N-1}. \tag{4}\]  
The problem with PA nonlinear modeling is to generate a mapping function, \(f_{\text{Mod}}\in\mathcal{F}:\mathbf{X}\to\mathbf{Y}\), and ensure the ability to simulate the nonlinearity of the PA. The expected nonlinear model output error is minimized as  
\[\min_{f_{\text{Mod}}\in\mathcal{F}}\mathbb{E}_{(\mathbf{y},\widehat{\mathbf{y}})} \mathcal{L}_{\text{MSE}}(\mathbf{y},\widehat{\mathbf{y}}). \tag{5}\]  
The goal of the DPD problem is to generate a mapping function, \(f_{\text{DPD}}\in\mathcal{G}:\mathbf{A}\cdot\mathbf{X}\to\mathbf{Y}\), and ensure that the output of the PA is linear. Its expected model output error is minimized as  
\[\min_{f_{\text{DPD}}\in\mathcal{G}}\mathbb{E}_{(\mathbf{A}\cdot\mathbf{x},\overline{ \mathbf{y}})}\mathcal{L}_{\text{MSE}}(\mathbf{A}\cdot\mathbf{x},\overline{\mathbf{y}}). \tag{6}\]
## 2 Materials and Methods  
### Refrigerants under Consideration  
Hydrocarbons as natural refrigerants attract attention due to their comparable critical temperature with common HFCs such as R134a. In this study, R290, R600a, and R1270 were chosen as hydrocarbon refrigerants, while R134a was selected as the reference HFC refrigerant. Table 1 provides a general overview of the relevant characteristics of these refrigerants. The selected hydrocarbons have an ODP of zero, a very low GWP, and good thermodynamic properties, making them ideal candidates for various refrigeration applications. The latent heat of the selected hydrocarbons is higher than that of the reference refrigerant, R134a, which indicates that the refrigerant charge amount can be reduced in the systems operating with hydrocarbons compared with R134a. The properties of refrigerants in Table 1 and the rest of the work have been extracted from REFPROP v10 [23].  
The compatibility of hydrocarbons with most of the lubricants used in refrigeration, heat pump systems, and most elastomer and plastic materials used as valve seats, gaskets, and \"O\" rings is another attractive characteristic of hydrocarbons.