esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Literature Review on DAS+PRS
### Feature Extraction
#### 4.1.3 Time-Frequency Domain-Based Feature Extraction Methods  
Wavelet transforms and STFFT were mostly employed in these methods. For example, in [20] the energy in frequency bands computed from multi-scale wavelet decomposition is employed, using 8 different frequency bands. The authors of [26; 27] computed the discrete-time wavelet transform for each signal, and assigned high and low decomposition levels based on the low and high frequency components of the signal, respectively. This multi-scale decomposition was then used to compute the energy distribution for each scale. These energy distributions form the feature vector in those works. On the other hand, in [17], the authors employed the STFFT to compute spectrogram-based features.
## 3 Methodology
### PSO Deep-ConvLSTM Network
#### 3.2.2 Structure of PSO Deep-ConvLSTM  
The network architecture of PSO Deep-ConvLSTM consists of an input layer, multiple ConvLSTM layers, and an output layer. The specific number of ConvLSTM layers, as well as hyperparameters such as learning rate, epoch number, and convolution kernel size, serve as the objectives to be optimized through PSO. After defining the range of values for hyperparameters optimization and setting the parameters for the particle swarm, the positions and velocities of each particle are randomly initialized.  
Within the learning model, both the loss function for ConvLSTM and the fitness function for PSO are selected as the \"mean squared error (MSE)\". The fitness values are computed and ranked, aiming to identify a set of hyperparameters that minimize the ConvLSTM error. The formula for the fitness function can be expressed as follows:  
\[MSE=\frac{1}{n}\underset{1}{\overset{n}{\sum}}(\hat{y}_{i}-y_{i})^{2} \tag{6}\]  
In this equation, the variable n represents the sample size of the validation dataset, \(y_{i}\) denotes the actual value, and \(\hat{y}_{i}\) represents the predicted value.  
The optimal value of the target parameter is output when the optimization process satisfies the convergence condition. Otherwise, it will repeat the process of computing the particle's fitness, updating the position and velocity of each particle, and updating the \(p_{best,i}^{k}\) and \(g_{best,i}\) until satisfying the convergence conditions.
# Adipose Stem Cell Response to Borophosphate Bioactive Glass
## 3 Results
### BPBG Alters ASC Secretome  
A quantitative sandwich-based ELISA array was performed to determine if BPBGs influenced the ASC secretome. After conditioning ASCs with a low concentration of BPBG for 24 h, the conditioned media was examined for 200 secreted proteins, including cytokines, growth factors, proteases, soluble receptors, and others (Figure 4). Of those, 187 proteins were detectable in sufficient expression levels, with glass treatment causing the upregulation of 87 proteins (Figure 5A) and the downregulation of 100 proteins (Figure 5B). Of those that were upregulated, 31 were upregulated with all three glass compositions, with 5 having no detectable levels in untreated ASCs. On the other hand, 24 proteins were downregulated in all three glass compositions, with 4 (BTC, CCL28, MSP, and HB-EGF) completely inhibited in glass treatment. There were 21 proteins that were differentially secreted with boron concentration: 1 decreased, 4 increased from levels detected in CCM, 11 were highest in CCM but then had increasing levels in glass-treated media, and 5 had increasing concentrations with increasing boron but with only X60 being above CCM base levels (Table 4).  
Figure 3: BPBGs affect cell migration. (**A**) A transwell assay examined migration. ASCs were loaded onto the top inserts, while 2.5 mg/mL of glass was added to the bottom. After 5 h of incubation, migrated cells were measured by CyQuant. (**B**) ASCs were treated with glass for 24 h under standard conditions. The resulting conditioned media was put in the bottom of a transwell with ECs added to the top. After 5 h of incubation, migrated cells were measured by CyQuant. In both assays, three separate ASC donors were examined in triplicate. Error bars indicated SD (n = 9); * \(p\)-value \(\leq\) 0.05.  
Figure 4: Representative hierarchal heatmap clustering the proteins based on function. All proteins presented are significant from the control (\(p<0.5\)). Log2(FC) is used in which (\(-5\) to \(5\)) is the range, and any value above \(5\) is represented as dark blue whereas any value below \(-5\) is represented as dark red color. In contrast, the white color represents all the proteins that are not significant. FC represents the fold change between the samples treated with glass in relation to the control.  
Figure 5: Effect of BPBGs on the ASCs secretory profile. Subconfluent ASCs were treated with \(2.5\) mg/mL of BPBG for 24 h. Media was then collected and analyzed for secreted proteins. ASC protein secretion was increased (**A**) or decreased (**B**) with glass treatment.
## 3 Results and Discussion
### Practical Prospects
#### 3.4.2 Interference  
The NH\({}_{3}\) measurement by the Gasmet analyser was known to interfere with carbon-dioxide (CO\({}_{2}\)) by the manufacturer. Although CO\({}_{2}\) compensation was incorporated in the Gasmet software, still a reading contribution of 0.086 ppm\({}_{\rm v}\) NH\({}_{3}\) per 100 ppm\({}_{\rm v}\) CO\({}_{2}\) was found on laboratory results with the tested device (unpublished data). The apparent CO\({}_{2}\) concentrations were, respectively, 782 \(\pm\) 217 ppm\({}_{\rm v}\) in the poultry barn and 2073 \(\pm\) 239 ppm\({}_{\rm v}\) in the pig barn measured by the Gasmet. After compensating for the interference, the RMSE of Gasmet comparing to the wet-chemistry method was reduced to 0.41 ppm\({}_{\rm v}\), and the instrument also reached sufficient equivalence to the wet-chemistry method, with \(r=0.999\), \(\beta_{0}=-0.306\) and \(\beta_{1}=1.018\). For the Gasmet analyser, gas composition plays an equally important role as the reliability of the reference NH\({}_{3}\) values, and single-gas calibration appeared to be insufficient for achieving an optimal accuracy under field conditions. Similar interference issues could also occur on the other analysers and should be investigated.
# Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
## 4 Discussion
### Impact of COVID-19 and Other Factors on Crop Choice
In addition to drought, irregular and intensified extreme climate events, such as hurricanes, have adversely affected the region over the past decade, as reported by local farmers in our informal communications with them. While the pandemic exacerbated these challenges, it also served as a catalyst for Thai farmers to embrace innovative technologies. For instance, some farmers have adopted drone-assisted crop management for plant health monitoring and pesticide spraying, proving its effectiveness for smallholder farming (see Figure 10).
# MuIm: Analyzing Music-Image Correlations from an
## 3 Multi-Layered Music-Image Correlation
### Limitations of Current State-of-the-Art Methods  
Current state-of-the-art (SOTA) methods of audio-visual correlation primarily employ two approaches: one-to-one mappings based on time, and few-to-few mappings based on emotion, as highlighted in Table 2. One-to-one methods are prone to bias, due to factors such as background noise and frame misalignment, which can reduce their accuracy. In contrast, few-to-few mappings offer better control over matching pairs and thus provide more reliable alignments. Our focus on few-to-few mapping aimed to achieve precise alignment by considering both emotional and semantic attributes.  
These existing methods typically emphasize either semantics or emotions in audio-visual associations. However, as illustrated in Figure 2a, these modalities often exhibit a complex, multi-layered relationship. For example, audio and visual data may share the same semantics but evoke contrasting emotions, or they may differ semantically yet  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aspect** & **Categorical Model** & **Dimensional Model** & **Hybrid Model** \\ \hline Emotion Representation & Discrete, with a fixed set of basic emotions. & Continuous, using a 2D vector (e.g., valence-arousal). & Continuous, with a 28D vector for multiple emotions and intensities. \\ \hline Complexity of Emotions & Simplistic; one emotion at a time. & Flexible; captures a range but may miss overlaps. & Highly flexible; captures overlapping, complex emotions. \\ \hline Intensity of Emotions & Lacks intensity variation. & Includes intensity but limited in handling overlaps. & Uses intensity scales for continuous, overlapping emotions. \\ \hline Applicability & Suitable for single-dominant emotion labeling. & Captures broader spectra but limited for complex multi-emotions. & Designed for complex artistic/multimedia content. \\ \hline Interpretability & Easy to interpret; defined categories. & Moderate; requires understanding dimensions. & Allows detailed, nuanced emotional analysis. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of categorical, dimensional, and hybrid models for emotion representation.  
produce similar emotional responses. Visual data often contain clearly identifiable semantic elements, whereas music's semantics are more subtly embedded in aspects such as style, genre, and instrumentation. This highlights the need for a multi-layered analytical approach to comprehensively understand the interplay between these two modalities.
## 5 Discussions  
It is well known that Gaussian noise is widely used in image denoising, thus our generative network can fully manage such noise. However, real-world noise, such as Poisson noise, Gaussian-Poisson noise, and salt and pepper noise, is usually non-Gaussian. Poisson noise and Gaussian-Poisson noise are so-called signal-related noise. In an image, their noise levels are variable while the noise level of Gaussian noise is fixed. To handle  
Figure 11: Denoising results of one image in BSD with noise level \(\sigma=40\): (**a**) original; (**b**) noisy image; (**c**) BM3D/26.11 dB; (**d**) NCSR/26.28 dB; (**e**) TWSC/26.46 dB; (**f**) WNNM/26.53 dB; (**g**) RED-Net/23.89 dB; (**h**) DnCNN/26.64 dB; (**i**) FFDNet/26.69 dB; (**j**) CsNet/27.64 dB; (**k**) DIP/24.89 dB; (**l**) Proposed/28.08 dB.  
these cases, we can exploit the average noise level [32] rather than the fixed noise level in our method. To handle salt and pepper noise, we must utilize the corresponding denoising algorithms to obtain preliminary images and use the noise ratio as a condition. That is, the criterion for over-fitting is no longer the noise level, but the noise ratio. Therefore, under the framework of our method, as long as the preliminary denoising images and the iteration termination conditions are modified accordingly, the salt and pepper noise or other types of noise can also be handled well.  
In this work, we adopt the mixed loss function, in which the three terms have the same weight, and achieved satisfactory results. Here, we adopt the noisy image to utilize its internal information, but its guiding ability is interfered with by different noise levels to varying degrees. Theoretically, when the noise level is relatively low, the noise image contains more useful information, so it can occupy more weight; when the noise level is relatively high, the noise image is seriously disturbed, and it contains less useful information, so it will occupy less weight. In future work, we consider assigning different weights to the terms to further improve the denoising performance of our generated network.  
Further, we exploit the MSE loss function that uses the L2 norm to characterize the distance between the generative image and the noisy image, and preliminary denoising images, respectively. Although the MSE loss function can easily reach local minimums and is sensitive to errors, it still has some defects. For example, it could over-penalize larger errors and may not capture complex characteristics in some cases; meanwhile, the mean absolute error (MAE) loss function that uses the L1 norm to describe the distance may allow our network to obtain better results. Thus, we are considering exploring a mixed loss function with more norms in future work.  
It should be noted that although the proposed method obtains the optimal result through online training, it requires a large number of gradient updates, resulting in long inference times. Thus, its execution efficiency is relatively low. In the future, we will consider adopting transfer learning [33] to first find a suitable general initial parameter to improve performance for a faster denoising process.
## 4 Numerical Implementation
### Matrix Form Stress-Strain Relation  
To better realize secondary development in the ABAQUS finite element platform, the modified suction is regarded as a stress component, and the \(S_{\text{e}}\) is regarded as a strain component. Three positive stress components, three shear components and the modified suction component are combined into a \(1\times 7\) generalized stress vector. Three positive strain components, three shear components and the effective degree of saturation component are combined to form a \(1\times 7\) generalized strain vector. Therefore, the stress and strain tensors are given as the following vectors:  
\[\vec{\sigma}^{*}=(\sigma_{11}^{*},\sigma_{22}^{*},\sigma_{33}^{*},\tau_{12}, \tau_{13},\tau_{23},s^{*})^{T} \tag{23}\]  
\[\vec{\epsilon}=(\varepsilon_{11},\varepsilon_{22},\varepsilon_{33},\gamma_{1 2},\gamma_{13},\gamma_{23},-S_{\text{e}})^{T} \tag{24}\]  
The incremental expression of the stress-strain relationship is given by:  
\[\text{d}\vec{\sigma}^{*}=\mathbf{D}_{\text{e}}^{*}\text{d}\vec{\epsilon}^{ *}=\mathbf{D}_{\text{e}}^{*}\left(\text{d}\vec{\epsilon}-\text{d}\vec{ \epsilon}^{*}\right)=\mathbf{D}_{\text{ep}}^{*}\text{d}\vec{\epsilon} \tag{25}\]  
where \(\text{d}\vec{\sigma}^{*}\) is the increment form of the stress vector, \(\mathbf{D}_{\text{e}}^{*}\) is the \(7\times 7\) generalized elastic matrix, \(\text{d}\vec{\epsilon}^{*}\) is the increment form of the elastic strain vector, \(\text{d}\vec{\epsilon}\) is the increment form of the total strain vector, and \(\mathbf{D}_{\text{ep}}^{*}\) is the \(7\times 7\) elastic-plastic matrix. The \(\mathbf{D}_{\text{ep}}^{*}\) form is determined by the plastic mechanism of the loading process. The plasticity mechanism is divided into yielding on LC only, yielding on SI/SD only and yielding on LC and SI/SD at the same time.
## 5 Results and Analysis
### EIR Global Path Planning Results and Analysis  
The Enhanced Inverse Reinforcement Learning (EIRL) algorithm was evaluated in FrozenLake-v1 using expert demonstrations (Figure 7), with expert dependency coefficient \(\alpha\in\{0.3,0.5,0.7,0.9,1\}\) modulating the expert-exploration tradeoff (\(\alpha=1\) indicates full expert reliance).  
Two optimized path types emerged (Figure 12):  
1. Low-dependency mode (\(\alpha=0.3\)): Action sequence \([\downarrow,\rightarrow,\rightarrow,\downarrow,\downarrow,\rightarrow, \rightarrow,\downarrow,\rightarrow,\rightarrow,\downarrow,\downarrow]\).  
2. High-dependency mode (\(\alpha\geq 0.5\)): Action sequence \([\rightarrow,\rightarrow,\rightarrow,\rightarrow,\downarrow,\rightarrow, \rightarrow,\downarrow,\downarrow,\downarrow]\).  
Figure 11: The Q-learning algorithm global planning path.  
Experimental results demonstrate that EIRL converged to Manhattan-optimal paths across all \(\alpha\) validating theoretical optimality.
# Topological Phase Diagram of BiTeX-Graphene Hybrid Structures
## 4 Conclusions  
In summary, we have explored the intriguing topological phase diagrams of bismuth tellurohalide/graphene heterostructures by means of first principles calculations. We showed that the in-plane uniaxial tensile strain opens a topologically non-trivial band gap in graphene in the vicinity of a material features strong inherent spin-orbit coupling. On the other hand, the compressive stress on the device promotes a trivial band gap. The BiTeBr-graphene and BiTeCl-graphene systems can be tuned from topological to trivial state and vica versa by applying mechanical stress. Furthermore, the BiTeI-graphene structure can be tuned from topological state to metallic. These systems lead to a novel realization of the time reversal invariant topological insulating phase, thus making these heterostructures potential candidates for quantum technology applications without the necessity of low temperature.  
Conceptualization, J.K. and L.O.; methodology, Z.T.; software, Z.T. and D.V.; validation, D.V.; writing-original draft preparation, Z.T.; writing-review and editing, J.K., L.O. and Z.T.; supervision, J.K. and L.O.  
This research was supported by the National Research, Development and Innovation Fund of Hungary within the Quantum Technology National Excellence Program (Project Nr. 2017-1.2.1-NKP-2017-00001); Grants No. K112918, K115608, FK124723 and K115575. This work was completed in the ELTE Excellence Program (1783-3/2018/FEKUTSRAT) supported by the Hungarian Ministry of Human Capacities. J.K. acknowledge the Bolyai and Bolyai+ program of the Hungarian Academy of Sciences.  
We acknowledge [NIIF] for awarding us access to resource based in Hungary at Debrecen.  
The authors declare no conflict of interest.
# Isocyanide Multicomponent Reactions on Solid Phase: State of the Art and Future Application
## 9 Non-Traditional Ugi Reaction
### Alternative Acid Input
#### 9.2.4 Azide  
Four-component reactions involving aldehydes, amines, isocyanides, and trimethylsilyl azide, leading to the formation of tetrazoles, are referred to as Ugi-azide-4 CRs. Tetrazoles are considered bioisosteres of carboxylic acids and were shown to preserve or improve biological activities. The first example is the previously mentioned repetitive Ugi reaction (cf. Scheme 58), and the second example is the solid-phase Ugi-azide-4 CR generating 5-substituted tetrazoles by using convertible Rink-isocyanide resin [6]. Resin-bound tetrazoles **432** underwent acidic cleavage to produce the target 5-substituted tetrazoles **433** (Scheme 73). Notably, secondary amine was used instead of traditional primary amine. This is an example of traceless synthesis [90].  
[PERSON] et al. used Ugi-azide-4-CR to synthesize a library of tetrazole-peptiddomimetics, which are potent inhibitors of the _Escherichia coli_ M1-aminopheneptidase [5]. \(\alpha\)-Amino acid attached to Wang resin **434** (Scheme 74) was reacted with aldehyde, isocyanide, and trimethylsilyl azide **345**. Four different amino acids, five aldehydes, and three isocyanides were used to generate a set of 1,5-disubstituted tetrazoles **438**. The products were obtained as a mixture of two diastereomers (dr was from 1:1 to 4.7:1).
## 4 Concluding Remarks  
This paper proposes isomorphic circuits with independent amplitude control function VM NBPFs and VM fully uncoupled QSOs. The proposed VM NBPFs and VM fully uncoupled QSOs employ three LT1228 commercial ICs, two grounded capacitors and three resistors. The proposed isomorphic circuits exhibit low-output impedance and independent amplitude control, which are important for easily cascading the VM operation and independent control of the amplitude gain. The Q and f\({}_{0}\) parameters of all the proposed NBPF responses are orthogonally adjustable. The passband gain of all the proposed NBPFs can be adjusted independently without affecting Q and f\({}_{0}\). The measured passband gains of the first proposed NBPF response can be independently tuned to 27.86 dB, and the measured phase noise is less than \(-\)83.86 dBc/Hz at a 30-Hz offset. The measured spurious-free dynamic range of the first proposed NBPF response was approximately 40.79 dB, and the calculated FoM was 3.07 \(\times\) 10\({}^{6}\). The measured passband gains of the second proposed NBPF response can be independently tuned to 27.58 dB, and the measured phase noise is less than \(-\)73.87 dBc/Hz at a 30-Hz offset. The measured spurious-free dynamic range of the second proposed NBPF response was approximately 40.86 dB, and the calculated FoM was 3.08 \(\times\) 10\({}^{6}\). By feeding back each input signal to the NBPF response, the VM fully uncoupled QSO could be obtained. During FO tuning, the voltage amplitude ratio of the quadrature output was equal. The FO could be electronically and linearly controlled by the bias current of the LT1228 IC, and one sinusoidal waveform could be independently controlled without any additional active devices. The experimental measurements confirmed the workability of the isomorphic circuits. Since the proposed isomorphic circuits have independent amplitude control functions for the NBPFs and QSOs, these isomorphic circuits are expected to have a wide variety of applications in the future, such as in phase-sensitive detection, signal processing, instrumentation, telecommunication and power systems.  
[PERSON] and [PERSON] conceived and designed the theoretical verifications; [PERSON] revised the manuscript to improve the quality of English; [PERSON] analyzed the results and wrote the paper; [PERSON] performed the simulations and experiments. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 5 Conclusions  
The thermal performances of a certain kind of propeller driver are analyzed, mainly using the method of numerical simulation. Firstly, the model of the driver with a direct-board heat sink is built and analyzed with the numerical method. Then, the experiment is carried out to verify the feasibility of the numerical model. We then design a novel cylindrical heat sink and study the effects of its base thickness, fin length, and fin numbers on heat dissipation. Finally, the impact levels of three structure variables for the driver are accessed through the orthogonal experiment. The conclusions are listed as follows:  
1. Compared to the initial straight-board heat sink, the cylindrical heat sink structure has a good heat dissipation ability. Under the same working conditions, the maximum temperature of the propeller driver can be reduced by 22.3%.
2. For the studied cylindrical heat sink, 29 simulation cases are carried out to analyze the influences of driver structure variables, such as base thickness, fin length, and fin numbers. It is shown that, generally speaking, the heat dissipation performance is strengthened by the increases in base thickness and fin length, as did the fin numbers. Considering the economics, there is an optimal solution for the cylindrical heat sink, that is, the heat sink with a base thickness of 40 mm, a fin length of 32 mm, and a fin number of 24.
3. Through orthogonal experiments, the base thickness has a very small impact on the working temperature of the driver. The fin numbers and the fin length have relatively greater impacts on the working temperature of the driver.  
Methodology, N.L. and M.L. and H.P.; simulation and experiment, N.L.; validation, N.L.; writing--original draft preparation, N.L.; writing--review and editing, M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
This work was supported by the Key R&D Program of Zhejiang Province (No. 2021C03013).  
The authors declare
## 2 Path Planning
### Pathfinding Using Grids  
A grid is a connection or a network of vertices or points through edges in order to form a graph (Figure 2). The performance of the pathfinding Algorithms is determined by attributes of the graph that is formed by the grid. The grids can be regular or irregular in pattern. Regular patterns can be of triangular, hexagonal, square, or cubic falling under 2D or 3D categories. Waypoints, mesh navigation, and visibility graph come under irregular patterns of grid system. In this paper, pathfinding Algorithms in gaming have been experimented and analyzed on a 2D Square Grid (Octile) system.