esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Aspect**} & **Hybrid** & **2\% D Visual Servoing** & \multirow{2}{*}{**MPC-Based IBVS** **(**[PERSON] et al., 2020) [23]**} & **Proposed Feedforward-** \\  & **Switched-System** & **([PERSON] and [PERSON], 2007) [21]** & & **2000)**[22] & **Feedback IBVS** \\ \hline \multirow{2}{*}{Main Idea} & Switch between IBVS and PBVS & Combine 2D image + 3D pose feature & Predict joint input from image error via MPC & Combine feedforward joint control with adaptive feedback \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Local \\ Minima \\ Handling \\ \end{tabular} } & Switching avoids & Blended Jacobian avoids singularities & Predictive avoidance via optimization & Feedforward brings pose close to global minimum \\ \hline \multirow{2}{*}{Stability Guarantee} & Local Lyapunov & Local/global under ideal pose estimation & Guaranteed via MPC feasibility &
\begin{tabular}{c} Inner/outer loop \\ stability via \\ Youla design \\ \end{tabular} \\ \hline \multirow{2}{*}{Computational Load} & \multirow{2}{*}{Moderate (switch logic)} & Moderate (hybrid  Jacobian computation) & High (real-time optimization) & Moderate (adaptive SVD and closed-form update) \\ \hline \multirow{2}{*}{Overshoot Risk} & Low, but may switch abruptly & Minimal, smooth trajectories & Well-regulated by MPC & May occur in feedforward if not well-tuned \\ \hline \multirow{2}{*}{Real-Time Suitability} & \multirow{2}{*}{Yes} & Yes & Yes, with efficient solver & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of four visual servoing strategies addressing local minima and control robustness. In summary, this paper investigates the overdetermination problem in stereo-based IBVS tasks. While future improvements to the algorithm are possible, the proposed control policy has demonstrated significant potential as an accurate and fast solution for real-world eye-in-hand (EIH) visual servoing tasks. Conceptualization, R.L. and F.A. ; methodology, R.L. ; software, R.L. ; validation, R.L. and F.A. ; data curation, R.L. ; writing--original draft preparation, R.L. ; writing--review and editing, F.A. ; supervision, F.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data is contained within the article. The authors declare no conflict of interest.
## 4 Experiments
### Results and Analysis  
Figure 3(a),b illustrate the classification performance of the model on datasets LRE-1 and LRE-2, respectively. The classification performance of the network is evaluated using the training epochs and corresponding accuracy. In the LRE-1 experiment, the baseline DSSM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Sensors Number** &
\begin{tabular}{c} **Samples Number/** \\ **Samples Length** \\ \end{tabular} & **Subsequence Length** \\ \hline LRE-1 & 28 & 50/5500 & 25 \\ LRE-2 & 48 & 2/20,000 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of two LRE datasets.  
Figure 3: Automatic relevance determination network.  
model exhibits poor classification performance, while the DSSM-A model, augmented with the ARD network, shows improvement in classification results after additional epochs. Furthermore, the classification performance of the DSSM-nl model, which includes nonlinear state-space transitions, surpasses that of the baseline DSSM model, indicating that nonlinear layers can better fit the data. The IRVSSM, which combines nonlinear state-space transitions and ARD network, demonstrates the best performance after a certain number of epochs, indicating that the combination of ARD network and nonlinear layers effectively enhances the model's classification capability. Similar classification results are observed in the LRE-2 experiment, demonstrating the effectiveness of the model.  
Figure 5 depicts the sensor weight diagram obtained after training the ARD network on the LRE-1 dataset. Sensors such as low-pressure oxygen turbine pump flow, high-pressure oxygen turbine pump flow, high-pressure oxygen turbine pump pressure, fuel preburner pump pressure, high-pressure hydrogen turbine pump flow, low-pressure hydrogen turbine pump flow, and fuel preburner pump pressure (lpotpQ, hpotpQ, hpotpPo, fpbQoc, hpoftpQ, lpftpQ) exhibit relatively high weights. In actual faults, such as oxygen turbine pump leaks, hydrogen turbine pump leaks, and cooling jacket leaks, changes in turbine pump flow and pressure occur. Therefore, the high weights of turbine pump flow and pressure correspond to actual fault patterns, demonstrating that the ARD network effectively captures crucial information from the data and verifies the interpretability of the model.  
Figure 4: (a) Classification accuracy of LRE-1; (b) Classification accuracy of LRE-2.  
Figure 5: Sensor weight graph.
## 3 Methods
### Spatial Extraction Module  
Figure 2 shows the Spatial Extraction Module (SEM) which consists of two parts: the adaptive dilated convolution attention (ADCA) module and the simple-res block (S-res).  
Instead of completely stacking traditional \(3\times 3\) or \(1\times 1\) convolution blocks, we design this new Spatial Extraction Module mainly with dilated convolution. Compared to traditional convolution, dilated convolution can collect spatial information over a larger area. Therefore, the ADCA module can significantly improve the network's capacity to capture spatial information across multiple scales. The S-res block, composed of \(3\times 3\) convolutions, improves the fusion of multi-scale information, including semantic and spatial details. The Spatial Extraction Module can be formulated as:  
\[\begin{split}\tilde{\text{F}}&=\text{S-res}( \text{F}),\\ \sum_{i=0}^{2}\tilde{\text{F}}_{i}&=\text{chunk}_{ 3}\big{(}\tilde{\text{F}}),\\ \tilde{\text{F}}_{o}&=\text{Cat}\big{(}\text{Conv}_{ r1}\big{(}\tilde{\text{F}}_{0}\big{)},\text{Conv}_{r2}\big{(}\tilde{\text{F}}_{1} \big{)},\text{Conv}_{r3}\big{(}\tilde{\text{F}}_{2}\big{)}\big{)},\\ \tilde{\text{F}}_{A}&=\text{SE}\big{(}\tilde{\text{ F}}_{o}\big{)}+\tilde{\text{F}},\\ \text{Out}&=\text{S-res}\big{(}\tilde{\text{F}}_{A} \big{)},\end{split} \tag{2}\]  
where F, \(\tilde{\text{F}}\), \(\tilde{\text{F}}_{A}\), and Out represent the input feature of SEM, input feature of ADCA, output feature of ADCA, and output feature of the entire SEM, respectively. In the ADCA module, the input feature is evenly split along the channel dimension to undergo different dilated convolution operations. After the operations are completed, the output features are then recombined to match the original feature map size. Following the approach in [29], the dilation rates for the three branches are assigned values of 1, 2, and 3, respectively. Subsequently, \(\tilde{\text{F}}_{o}\) is passed through the SE block [30] for further computation, and the result is added to \(\tilde{\text{F}}\) to produce the ADCA output \(\tilde{\text{F}}_{A}\). The SE block refers to the Squeeze-and-Excitation block [30], a lightweight yet effective architectural component that aims to improve the network's representational capacity. The S-res block consists of a convolutional layer with the residual connection [31]. It is used both before and after the ADCA module to process the features, increasing model depth and enhancing its ability to understand features. The detailed explanation of these formulas will be presented in the Appendix A on page 4.  
The ADCA module enables the network to adaptively prioritize more relevant spatial information and combine it with the semantic information contained in the features by increasing attention, avoiding performing multiple operations on the same feature and wasting a lot of computation. At the same time, residual connections can integrate the  
Figure 4: A ViT-based module with MSCA and FFN as the core components.  
advantages of features processed before and after ADCA blocks, further enhancing the expressive power of the features. Therefore, SEM has achieved good results.
## 2 Materials and Methods
### Outcomes  
Imaging was conducted using a 3T Siemens Trio MRI scanner (Siemens Healthineers, Erlangen, Germany) with 12-channel transmit/receive head coils. T1-weighted MPRAGE sequences were used for anatomical localization, voxel placement, and partial volume correction of cerebrospinal fluid. Single-voxel proton magnetic resonance spectroscopy (1H-MRS) data were collected pre- and post-treatment, approximately 15 min after t-PBM sessions. A voxel (30 \(\times\) 30 \(\times\) 15 mm\({}^{3}\)) was placed in the left frontal lobe for magnetic resonance spectroscopy (MRS) acquisition using a Point RESolved Spectroscopy (PRESS) sequence [28] (TR = 2000 ms, TE = 30 ms, bandwidth = 2000 Hz). The acquisition included 128 signal averages, with a total acquisition time of 4 min and 48 s for the main spectroscopy sequence and 16 s for the water-suppressed spectroscopy sequence (four signal averages, acquisition duration = 514 ms).  
Neurometabolites measured included total NAA (the sum of N-acetylaspartate [NAA] and N-acetylaspartate [NAAG]), total Cho (phosphorylationcholine [PCho] and free choline), and total Cr (creatine and phosphocreatine). Quantification was performed using the jMRUI toolkit v6.01 and the QUEST algorithm, with phase correction and water-referenced spectra. Only metabolites with a Cramer-Rao lower bound (CRLB) \(<\) 25% were included. Tissue segmentation using Gannet and SPM12 corrected for partial volume effects in gray matter, white matter, and cerebrospinal fluid (CSF). Only metabolites with a Cramer-Rao lower bound (CRLB) \(<\) 25% were considered for further analysis. The levels were corrected for partial volume effects by registering the MRS voxel to the T1 anatomical space and segmenting it into gray matter, white matter, and CSF using the Gannet toolkit v3.13 [29] and SPM12 [30]. The segmented tissue fractions were applied to partial volume corrected for the metabolite levels using jMRUI-QUEST to account for CSF content, as outlined in the literature [31]. The T1 and T2 values for gray matter, white matter, and CSF used in this study were 1331, 832, and 3817 ms and 110, 79, and 503 ms, respectively. The metabolite relaxation times used for calculating final corrected levels were based on previous studies [32; 33]. Figure 1 illustrates the MRS voxel placement, segmentation, and a representative spectrum.  
To assess clinical outcomes, we applied the Montgomery-Asberg Depression Rating Scale (MADRS) [34] and the Symptoms of Depression Questionnaire (SDQ) [35]. MADRS scores were used to evaluate changes in depressive symptoms, while SDQ scores captured a broader range of depression-related symptoms, including anxiety.
## 2 Bearing Failure Modes
### Infrared Thermography Detection  
Infrared thermography, as a non-contact detection technology that does not damage the properties of bearings, is a fault diagnosis technology that determines the health status of bearings by monitoring the temperature field distribution and thermal changes of bearings [54]. The principle is shown in Figure 7.  
When bearings experience faults such as increased friction, insufficient lubrication, or structural damage during operation, the temperature often rises abnormally [55]. In addition, heating excitation can also be applied to bearings to affect the temperature distribution around defects such as cracks and thermal damage. In thermal imaging detection, a thermal imager is used to capture the temperature distribution map of the bearing surface [56]. By processing these temperature distribution maps, it is possible to determine whether there is abnormal temperature in the bearing and thus determine the potential fault location and type.  
Sensors and data processing units are integrated together for detection. For example, [PERSON] et al. proposed using infrared thermal imaging technology as an intelligent sensor, combined with algorithm units, to detect bearing faults in induction motors [57]. To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters, and the best diagnostic rate. A fault diagnosis rate of 98.8889% was achieved [58]. [PERSON] et al. designed an unsupervised learning framework named Feature-Preserving Cycle Consistent Generative Adversarial Network (FP-CycleGAN) for defect detection in imbalanced rolling bearing infrared thermal imaging samples [59].  
The main advantage of this method is that the detection process is non-contact and can be carried out during equipment operation without affecting the normal operation of the bearings. Meanwhile, thermal imaging detection can quickly and extensively scan  
Figure 7: Principle of infrared thermography.  
the entire equipment, making it suitable for regular inspections and online monitoring of large mechanical equipment. It has high sensitivity and can respond to slight temperature changes, which is especially suitable for detecting initial faults.  
However, thermal imaging detection also has certain limitations: it can only detect temperature changes at the faulty location and has low sensitivity to minor early damage or faults that have not caused temperature anomalies. Meanwhile, thermal imagers are relatively expensive and may experience temperature interference in high ambient temperatures or densely populated environments, which can affect detection accuracy. Therefore, thermal imaging detection is often combined with vibration analysis, acoustic analysis, and other methods to provide more comprehensive fault diagnosis results.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods
### Optimization Algorithm Based on PSO-GA Improvement
#### 3.6.1 Particle Swarm and Genetic Algorithm Overview  
The particle swarm optimization algorithm, an optimization algorithm based on group intelligence, was proposed by [PERSON] and [PERSON] in 1995 [51]. The idea of the particle swarm optimization algorithm originates from the social behaviors of bird flocks and fish schools in nature, and it can be used to solve problems such as optimizing combination weights. During the foraging process, each bird in the flock adjusts its flight direction by observing the flight paths and positions of its surrounding companions in order to find the food source. Similarly, in the PSO algorithm, each \"particle\" represents a candidate solution in the problem space, and the particle adjusts its position by tracking its own historical best position and the global best position in its neighborhood. Compared with other optimization algorithms, the PSO algorithm has fewer control parameters, particle memory, and faster convergence. However, the PSO algorithm is prone to falling into local optimal solutions during the search process due to the lack of population diversity, and the search range is small. Especially when dealing with the multi-peak function optimization problem, the particles may prematurely gather near a local optimal solution during the iteration process, leading to the premature convergence of the algorithm [52].  
In contrast, genetic algorithms, inspired by evolutionary theory, search for optimal solutions by mimicking natural selection and hereditary processes. The GA has selection, crossover, and mutation operations that increase the diversity of the population, thus helping to reduce the problem of falling into local optima. It iteratively searches the entire solution space rather than only local space, which enables the GA to search for globally optimal or near-optimal solutions, and adopting a probabilistic rather than a deterministic search strategy allows the GA to reduce the number of local optima to be trapped in. Since crossover and mutation operations in genetic algorithms are randomized and do not rely on past search experience, this may cause the algorithm to take more time to converge to the optimal solution. As verified by previous studies, since the above two optimization algorithms have complementary characteristics in terms of strengths and weaknesses, the optimization algorithm formed by mixing two single algorithms is able to overcome each single algorithm's limitations and achieve complementary advantages, which helps to avoid precocious convergence and falling into local optima [53].
## 2 Results  
### Balanced Up-Downwind Semi-Discretized Scheme  
Let \(-X=x_{0}<x_{1}<\cdots<x_{M}<x_{M+1}=X\), \(0=y_{0}<y_{1}<\cdots<y_{N}<y_{N+1}=Y\), for which \(x_{m}-x_{m-1}=h_{m},\ y_{n}-y_{n-1}=k_{n},\ 0<h_{m},k_{n}\ll 1,\)\(m=1,2,\ldots,M+1,n=1,2,\ldots,N  
Case 1: \(\eta>2r\).  
\[u_{x}(x_{m},y_{n},\tau) \approx\Delta_{x,+}u_{m,n},\ u_{y}(x_{m},y_{n},\tau) \approx\Delta_{y,+}u_{m,n}, 2r\geq y>0, \tag{17}\] \[u_{x}(x_{m},y_{n},\tau) \approx\Delta_{x,-}u_{m,n},\ u_{y}(x_{m},y_{n},\tau) \approx\Delta_{y,+}u_{m,n}, \eta \geq y>\tau,\] (18) \[u_{x}(x_{m},y_{n},\tau) \approx\Delta_{x,-}u_{m,n},\ u_{y}(x_{m},y_{n},\tau) \approx\Delta_{y,-}u_{m,n}, Y>y>\eta. \tag{19}\]  
Case 2: \(\eta\leq 2r\).  
\[u_{x}(x_{m},y_{n},\tau) \approx\Delta_{x,+}u_{m,n},\ u_{y}(x_{m},y_{n},\tau) \approx\Delta_{y,+}u_{m,n}, \eta \geq y>0, \tag{20}\] \[u_{x}(x_{m},y_{n},\tau) \approx\Delta_{x,+}u_{m,n},\ u_{y}(x_{m},y_{n},\tau) \approx\Delta_{y,-}u_{m,n}, 2r\geq y>\eta,\] (21) \[u_{x}(x_{m},y_{n},\tau) \approx\Delta_{x,-}u_{m,n},\ u_{y}(x_{m},y_{n},\tau) \approx\Delta_{y,-}u_{m,n}, Y>y>2r. \tag{22}\]  
The computational stencils for the Case 1 and Case 2 are given in Figure 1.  
Define  
\[h_{\min}=\min_{m=1,2\cdots M}h_{m},\ h_{\max}=\max_{m=1,2\cdots M}h_{m};\ k_{ \min}=\min_{n=1,2\cdots N}k_{n},\ k_{\max}=\max_{n=1,2\cdots N}k_{n}.\]  
We now approximate the cross-derivative in (9) dynamically. To this end, we have the following two cases presented in Sections 2.1.1 and 2.1.2.
## 4 Material and Methods
### RNA Extraction, Library Construction, and Sequencing  
Fresh bacterial cultures of each strain were inoculated into 50 mL of LBNS medium at 1% and cultured to the mid-logarithmic phase (OD\({}_{600}\) = 0.4). Each strain was carried out in three biological replicates. Bacterial cells were harvested and used to extract the total RNA using TRIzol(r) Reagent according to the manufacturer's instructions (Invitrogen). A high-quality RNA sample (OD\({}_{260/280}\) = 1.8-2.0, OD\({}_{260/230}\)\(\geq\) 2.0, RIN \(\geq\) 6.5, 285:18S \(\geq\) 1.0) was used to construct the sequencing library.  
A RNA-Seq transcriptome library was performed using a TruSeq(tm) RNA sample preparation kit from Illumina (San Diego, CA, USA). After a cluster generation, a paired-end RNA-Seq sequencing library was sequenced with the Illumina HiSeq \(\times\) TEN (2 \(\times\) 150 bp read length). The processing of original images to sequences, base-calling, and quality value calculations were performed using the Illumina GA Pipeline (version 1.6), in which 150 bp paired-end reads were obtained.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 5 Application of Silica Mesopores in Drug Delivery
### \(H_{2}o_{2}\)-Responsive Drug Delivery  
Targeting H\({}_{2}\)O\({}_{2}\) as a diagnostic marker and therapeutic agent has shown great potential. In 2012, [PERSON] et al. reported on a biocompatible delivery platform based on an H\({}_{2}\)O\({}_{2}\) responsive, controlled-release system using mesoporous silica nanoparticles to realize the targeted delivery of the therapeutic metal chelator for treating Alzheimer's disease [202]. Increasing the levels of H\({}_{2}\)O\({}_{2}\) led to the release of the metal chelator. The advantages of this delivery system are its excellent biocompatibility, good cellular uptake, and appropriate intracellular release of metal chelators. Hence, this delivery system shows the potential for controlled drug release applications. [PERSON] et al. reported a temperature and H\({}_{2}\)O\({}_{2}\) dual-responsive nanovalve, which were fabricated from ferrocene modified MSNs and cyclodextrin-poly(N-isopropylacrylamide) star shaped polymer due to the host-guest interactions for controlled drug release and exhibited excellent anti-cancer activity [203].
## 5 Results and Analysis
### Visual Effects Evaluation  
In this paper, two groups of representative complex remote sensing images were selected to showcase the testing results of the reconstruction experiment, as shown in Figure 6.  
Figure 5: Variation of PSNR and SSIM during the training.  
Figure 6: _Cont._Figure 6 depicts the outcomes of various reconstruction methods applied to remote sensing images. The bicubic method yields images with roughly restored content but lacks intricate texture details, resulting in a blurry appearance. Leveraging the robust feature extraction abilities of CNN and transformer networks, full-size models like EDSR, SwinIR, RCAN, and HAT offer superior reconstruction by capturing finer details and texture changes in the original remote sensing images. In contrast, while the ECBSR model significantly enhances texture features and restores ground object details compared to bicubic, its reconstruction effect doesn't match the full-size models due to its lightweight network structure. Notably, our proposed SRRepViT model, optimized through structural re-parameterization as a lightweight model, showcases enhanced reconstruction performance compared to ECBSR. It presents finer edge and texture information in the images. Upon visual comparison with other super-resolution reconstruction models, SRRepViT stands on par with EDSR, SwinIR, RCAN, and HAT, slightly outperforming ECBSR in capturing image details.
## 6 Results and Analysis  
We have simulated the SAPS protocol by deploying nodes for the patient, doctor/nurse, and the trusted server using TCL script in NS 2.35.The simulation parameter and system setting defined in Table 5. We have separately configured the patient and doctor/nurse for low power devices. The server is configured for high residual and transmission power. In the TCL file, communication messages are initiated and traffic sources are also configured along with packet sizes. Moreover, C/C++ files are developed for providing device-level functionalities, including send, receive, hash, encrypt, and decrypt functions. Results are extracted from the trace file by executing the AWK scripts. We have used a 160-bit key along with \(f(x)=x^{167}+x^{9}+x^{7}+x^{5}+x^{3}+1\) where the tuple \(T=(\ m,f,(x)a,b,G,n,h)\). The performance of SAPS is evaluated regarding storage, computation, and communication costs for the base scheme TPKE [15], A-TIMS [16], EAKA [40], and AAS [41]. A list of simulation parameters is shown in Table 4.
# A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
## 4 Experimental Results and Analysis
### Datasets Introduction  
In order to validate the effectiveness and applicability of the proposed method, experiments were conducted on both real and simulation datasets. The real dataset consisted of various business process models collected from the Modeler application of SAP, which is a provider of enterprise management solutions, where these process models contained complex selection, loop, sequential, and parallel structures. The process models in the real dataset were short in length and, therefore, suitable for evaluating the performance of the proposed approach on short-process models. The simulation dataset was obtained by randomly expanding nodes in the process model of the real dataset and was used to evaluate the performance of the proposed method on long process models. The statistical information for both datasets is provided in Table 3.