esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Experiments
### Comparison with Existing Distributed Algorithms
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance Type**} & \multicolumn{3}{c}{**CNP**} & \multicolumn{3}{c}{**DETS**} & \multicolumn{3}{c}{ADMM} & \multicolumn{3}{c}{**PITO**} \\ \cline{2-11}  & \(aRV\) & \(aCT\) & \(aRT\) & \(aRV\) & \(aCT\) & \(aRT\) & \(aRV\) & \(aCT\) & \(aRT\) & \(aRV\) & \(aCT\) & \(aRT\) \\ \hline \(\left[\)E, 50, high, normal\(\right) & 0.1037 & 250 & **0.1814** & 0.0965 & 545.00 & 0.8223 & 0.0387 & 1964 & 1907.0651 & **0.0002** & **217** & 32.2933 \\ \(\left[\)E, 100, low, \(\right) & 0.2919 & 500 & **0.8558** & 0.1197 & 1153.46 & 1.9715 & 0.0713 & 3744 & 2251.2133 & **0** & **337.34** & 90.3036 \\ \(\left[\)E, 100, low, normal\(\right) & 0.2711 & 500 & **0.9952** & 0.1438 & 1088.29 & 2.0296 & 0.0751 & 3744 & 2362.8377 & **0** & **373.81** & 158.2195 \\ \(\left[\)E, 100, high, \(\right) & 0.3003 & 500 & **2.1974** & 0.1355 & 1278.63 & 3.0104 & 0.0905 & 3858 & 2634.2013 & **0** & **358.68** & 147.7549 \\ \(\left[\)E, 100, high, normal\(\right) & 0.3068 & 500 & **2.2224** & 0.1204 & 1278.98 & 2.7709 & 0.0871 & 3924 & 2756.0978 & **0** & **358.64** & 156.2090 \\ \(\left[\)E, 50, low, emergency\(\right) & 0.0439 & 250 & **0.0931** & 0.0537 & 445.00 & 0.4262 & 0.0330 & 2574 & 1393.1036 & **0.0082** & **164** & 34.4894 \\ \(\left[\)F, 50, low, normal\(\right) & 0.0449 & 250 & **0.1209** & 0.0494 & 445.00 & 0.6396 & 0.0312 & 2574 & 1445.8109 & **0.0070** & **164** & 35.5420 \\ \(\left[\)F, 50, high, \(\right) & 0.0625 & 250 & **0.1138** & 0.0472 & 383.92 & 0.4567 & 0.0251 & 2552 & 2783.5166 & **0.0028** & **192.31** & 37.9846 \\ \(\left[\)F, 50, high, normal\(\right) & 0.0550 & 250 & **0.1523** & 0.0165 & 441.85 & 0.4779 & 0.0150 & 2816 & 2609.7868 & **0.0024** & **191.38** & 60.5440 \\ \(\left[\)F, 100, low, \(\right) & 0.1388 & 500 & **0.4792** & 0.1054 & 936.68 & 1.2663 & 0.0473 & 4092 & 3436.2467 & **0.0006** & **316.96** & 163.3933 \\ emergency\(\left[\)F, 100, low, normal\(\right) & 0.0859 & 500 & **0.3493** & 0.0483 & 1008.56 & 1.3893 & 0.0305 & 4224 & 3792.8678 & **0.0006** & **389.23** & 256.1243 \\ \(\left[\)F, 100, high, \(\right) & 0.2422 & 500 & **0.5703** & 0.0874 & 1008.98 & 1.3540 & 0.0443 & 4224 & 3851.9423 & **0** & **332** & 236.6883 \\ \(\left[\)F, 100, high, normal\(\right) & 0.2377 & 500 & **0.6161** & 0.0909 & 1008.29 & 1.6362 & 0.0420 & 4686 & 4013.7642 & **0** & **332** & 221.7397 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._
## 4 Interval Parameter Identification Procedure  
This section introduces a novel interval parameter identification method, which adopted the improved optimization strategy. Similar to the traditional parameter identification, the system with uncertainty can be implemented effectively by converting into an inverse optimization problem, which seeks the minimum relative error between the calculated value and experimental value. The mathematical model of the optimization process can be expressed in the following general form:  
\[\begin{array}{ll}Find&\mathbf{x}=\left\{\left[\underline{x_{i}},\overline{x_{i }}\right]\right\}_{m}\\ \min&f(\mathbf{x})=\sum\limits_{i=1}^{n}\left(\left|\left(T_{i}^{P}(\mathbf{x})-T_{i }^{M}\right)/T_{i}^{M}\right|\right)\\ &=\sum\limits_{i=1}^{n}\left(\left|\left(\underline{T_{i}}^{P}(\mathbf{x})- \underline{T_{i}}^{M}\right)/\underline{T_{i}}^{M}\right|+\left|\left( \overline{T_{i}}^{P}(\mathbf{x})-\overline{T_{i}}^{M}\right)/\overline{T_{i}}^{M }\right|\right)\\ s.t.&\underline{\mathbf{x}}\leq\mathbf{x}\leq\overline{\mathbf{x}}\end{array} \tag{26}\]  
where \(\mathbf{x}\) denotes the system unknown parameter vector, \(m\) and \(n\) is the number of unknown parameters and system features, respectively, \(f(\mathbf{x})\) describes the relative error of system, and the superscript \(P\) and \(M\) stands for the predicted and measured value, respectively.  
As seen in Figure 6, the process of the interval parameter identification is clearly shown. Among them, several judgment conditions are set to ensure the effective operation of the procedure. Specifically, the \"Condition met\" in the step of construct the surrogate model guarantees the accuracy of the ATP model. While, the judgment follows the step of updating strategy is set to ensure that the ATP model can maintain high accuracy throughout the optimization process. The proposed interval parameter  
Figure 5: Maximum relative error of unbiased estimation method: (**a**) Uniformly distributed; (**b**) Normal distribution.  
identification method roughly consists of two parts: (1), searching the initial value of the variable; (2), performing the rough and fine search to obtain the identified result.
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 7 Conclusions  
In light of this research, the notable potential of artificial intelligence in terms of automating the extraction of relevant parameters of buildings can be confirmed, although there is still a long way to go in the field of computer vision.  
There is a lack of high-quality datasets with urban street imagery for tasks such as image segmentation, especially in the case of imagery from developing countries. Moreover, the datasets currently available have been created mainly for self-driving car technology, not for uses related to the construction sector.  
It is possible to estimate the average height of the buildings in pilot images with errors of just a few metres, under conditions that are versatile and which allow for easy matching without the need for point clouds or any kind of image depth estimation. The semantic segmentation model achieves noteworthy results and is more versatile as well as faster than other models, successfully addressing the issues of lack of global information extraction and image detail loss. However, the model still needs improvements in order to function adequately for non-European cities due to the lack of suitable datasets.  
Further research could address the lack of training data for the segmentation model through domain adaptation, with a model that converts street images to the style of a German city. However, it is crucial that datasets are created for other regions of the world.  
Figure 9: Example of the method with an image from the Cityscapes test set. (**a**) Input image. (**b**) Model output segmentation. (**c**) Building height vector considering car height 1.  
A promising line of research includes the development of building height estimation classifier models. Classifier machine learning models are among the most studied fields in artificial intelligence. These models will assign the number of floors to each building as a categorical class. This information is essential for the assessment of seismic exposure and for the calculation of building vulnerability, an urgent issue in many regions of the world.  
Conceptualisation, M.U.-P., R.M.-M. and M.M.-S.; methodology, M.U.-P. and R.M.-M.; software, M.U.-P.; validation, M.U.-P. and M.M.-S.; writing--original draft preparation, M.U.-P.; writing--review and editing, B.G.-R., R.M.-M. and M.M.-S.; visualisation, M.U.-P.; supervision, R.M.-M. and B.G.-R. All authors have read and agreed to the published version of the manuscript.  
This research received public funds from a grant for the completion of industrial doctorates (IND2020/TIC-17528) funded by Comunidad de Madrid.  
Not applicable  
The authors gratefully acknowledge the KUK_AHPAN project (RTI2018-094827-B-C21/C22) and the Universidad Politecnica de Madrid (www.upm.es, accessed on 10 February 2023) for providing computing resources on Magerit Supercomputer.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
[23] investigated the effect of surface finish and synthesis parameters on the corrosion properties of DMLS-printed M300 steel parts. It was found that DMLS-produced M300 steel is susceptible to corrosion in the caseof operation in corrosive environments. [PERSON] et al. [PERSON] et al. (2018) applied L-PBF to manufacture stamping tools with a honeycomb inner structure. Experimental tests have shown that 3D printing significantly improves material utilisation. However, AM results in higher costs in unit production compared to conventional tool manufacturing methods. [PERSON] et al. [PERSON] et al. (2018) investigated AMed tools for hot sheet metal forming. It was concluded that AM enables the increase of the cooling rate in hot forming tools by providing suitably optimised cooling channels. [PERSON] et al. [PERSON] et al. (2018) applied DMLS to the production of tool inserts with the intention to eliminate the complicated casting process of these components. Tests of the tools under real industrial conditions led to the conclusion that AM provides significant reduction in tool preparation time. DMLS technology has been studied extensively by [PERSON] et al. [PERSON] et al. (2018), [PERSON] et al. [PERSON] et al. (2018), [PERSON] (2018), and [PERSON] et al. [PERSON] et al. (2018), and it was used in the production of stamping tools or inserts assembled on stamping tools. [PERSON] et al. [PERSON] et al. (2018) provided a comprehensive review of the applications of L-PBF in tool manufacturing. Reducing the time of tool preparation is becoming particularly important in the automotive industry, where the forming of metal sheets and profiles is one of the main manufacturing processes of body panels. With the rapid introduction of new products as a result of increasing competitiveness among automotive manufacturers, it is becoming necessary to ensure rapid tool manufacturing via AM. The aim of the research presented in this article is to provide knowledge on the wear of DMLSed 20 MnCr5 steel countersamples. Among many existing metal powders, the wear-resistant 20 MnCr5 steel is held to be applicable in tool making for stamping operations. This study focuses on tool material intended for cold forming. To the best of the authors' knowledge, this is the first time that a tribological study of DMLSed 20 MnCr5 steel countersamples tested against samples made of typical materials with various mechanical and tribological properties (Inconel 625, titanium-stabilised stainless steel 321, EN AW-6061 T0 aluminium alloy and pure copper) is presented. The motivation for conducting the research was to determine the wear resistance of maraging steel elements, which can potentially be used in the form of inserts or as a working layer made in 3D printing technology, using the DMLS method on a substrate made of substantially cheaper steel constituting the basis of the stamping tool. The advantage of such a solution is the reduction in costs related to materials intended for tools, tool production, and their regeneration, which will consist in printing the worn working layer.
## 1 Introduction  
Traditional herbal prescriptions consist of two or more medicinal herbs; they are very diverse and contain many ingredients. Therefore, standardization is required for efficient quality control.  
Sogunjung-tang (SGJT), also known as Xiaojanzhong-tang in Chinese and Shokenchu-to in Japanese, is an oriental medical prescription. According to Donguibogam (the Principles and Practices of Eastern Medicine), it is recorded that SGJT has been used to treat symptoms such as abdominal pain, wet dreams, and melalgia. SGJT consists of a combination of six herbal medicines: Paeoniae Radix, Cinnamomi Ramulus, Glycyrrhiziae Radix et Rhizoma, Zingiberis Rhizoma Recens, Zizyphi Fructus, and Oryzae Gluten [1].  
In studies on the biological activity of SGJT, [PERSON] et al. [2] reported its inhibitory effects on Type I hypersensitivity and inflammatory reactions, and [PERSON] et al. [3] reported its effects on cell proliferation and immune activity. In addition, [PERSON] et al. [4] reported the results of genotoxicity studies such as bacterial reverse mutation and micronucle tests.  
The major components contained in the six herbal medicines that make up SGJT include monoterpenoids (e.g., albiflorin, ALB; paeoniflorin, PAE; and benzoylpaeoniflorin, BPAE) and phenolic compounds (e.g., gallic acid, GA; and benzoic acid, BA) from _Paeonia lactiflora_[5, 6], coumarins (e.g., coumarin, COU) and phenylpropanoids (e.g., cinnamic acid, CINA; and cinnaldehyde, CINAD) from _Cinnamomum cassia_[7], triterpen saponins (e.g., glycyrrhizinic acid, GLYA) and flavonoids(e.g., liquiritin apioside, LIAP; liquiritin, LIQ; and liquiritigenin, LIQG) from _Glycyrrhiza uralensis_[8,9], phenols (e.g., 6-gingerol, 6-GIN) from _Zingiber officinal_[10], flavonoids (e.g., spinosin, SPI) from _Zizyphus jujube_[11], and saccharides (e.g., maltose) from Oryza Gluten [12].  
Thus, a range of biological activities of SGJT and of many of the individual herbal medicine components constituting SGJT have been reported, and analytical methods for quality control based on high-performance liquid chromatography (HPLC) or liquid chromatography-mass spectrometry have also been reported [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. However, no standardization study for quality control of SGJT composed of combinations of these herbs has been reported.  
In this study, five of the constituent medicinal herbs of SGJT, excluding saccharide-containing Oryza Gluten, were mixed, extracted, and used for HPLC analysis. These extracts were used to develop and verify a method for the simultaneous analysis of 12 marker compounds for quality assessment of SGJT using HPLC separation combined with photodiode array detection for the simultaneous detection of the marker analytes. For the quality assessment of SGJT using the developed analysis, the following 12 marker analytes were assayed: GA, ALB, PAE, BA, and BPAE (Paeoniae Radix); COU, CINA, and CINAD (Cinnamomi Ramulus); and LIAP, LIQ, LIQG, and GLYA (Glycyrrhizae Radix et Rhiza).
## 2 Materials and Methods
### Model Fitting
#### 2.4.2 Two Patterns of the CCTM Model  
Two patterns of the CCTM model have been proposed in this paper. One is the seasonal time-series pattern, another is the yearly time-series pattern. The seasonal time-series pattern is to divide the yearly time series into four seasons, and each seasonal time series is fitted by the CCTM model, so this pattern has 24 coefficients. The yearly time-series pattern is to directly fit the yearly time series by using the CCTM model, and this pattern has 6 coefficients.  
The CCTM model in the yearly time-series pattern is shown as Equation (13):  
\[\begin{array}{c}\widehat{\rho}\left(i,x\right)_{simple}=\ \ \ a_{0,i}\times x+b_{0,i}+a_{1,i}\times cos \big{(}\frac{2\pi(x-min)}{365.25}\big{)}+b_{1,i}\times sin\big{(}\frac{2\pi(x -min)}{365.25}\big{)}+\\ \In the seasonal time-series pattern, the yearly time series can be represented as the sequence of the four seasonal CCTM models (Equation (14)):  
\[\begin{array}{c}\widehat{\rho}\left(i,x\right)=\left[\widehat{\rho}\left(i,1,x \right)_{simple},\widehat{\rho}\left(i,2,x\right)_{simple},\ \widehat{\rho}\left(i,3,x\right)_{simple},\ \widehat{\rho}\left(i,4,x\right)_{simple}\right]\\ \widehat{\rho}\left(i,k,x\right)_{simple}=\begin{array}{c}a_{0,i}\times x+b _{0,i}+a_{1,i}\times cos\big{(}\frac{2\pi\left(x-min\right)}{max-min}\big{)}\\ +b_{1,i}\times sin\big{(}\frac{2\pi\left(x-min\right)}{max-min}+a_{2,i}\times e \frac{x-max}{10}+a_{3,i}\times\ln(1\\ +\frac{x-min}{max-min}\ \left(e-1\right)\big{)}\end{array} \tag{14}\]  
where, \(x\) is the Julian day; max is the maximum day of seasonal date interval; min is the minimum day of seasonal date interval; k represents the season, where 1 = spring, 2 = summer, 3 = autumn, and 4 = winter; \(i\) is the year; \(a_{0,i},b_{0,i}\) are the coefficients of the linear model; \(a_{1,i},b_{1,i}\) are coefficients of the harmonic model which captures the unimodal variation; \(a_{2,i}\) is the coefficient of the index model that captures the accelerated variation in a year; and \(a_{3,i}\) is the coefficient of the logarithmic model that captures the decelerated variation in a year.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 6 Conclusions  
This study aims to enhance Inter-SubNet, a state-of-the-art speech enhancement framework, by incorporating Huber loss and temporal acoustic parameter (TAP) loss. Our experiments on the VoiceBank-DEMAND dataset demonstrate that both replacing MSE loss with Huber loss and integrating TAP loss with MSE loss improve Inter-SubNet's performance across various metrics. Importantly, these modifications do not increase the model's complexity for inference, making them practical for real-world applications. As for the future research direction, we plan to apply Huber and TAP losses to the other state-of-the-art speech enhancement frameworks and other speech processing tasks (such as voice activity detection, speech separation, and speaker diarization) to evaluate their effectiveness. In addition, we will explore novel loss functions that benefit the training of neural networks.  
\begin{table}
\begin{tabular}{c} 2.2928 M \\ (+5.2106 M) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: The memory usage, training time, inference time, and model size of Inter-SubNet trained using different loss functions. The programming language used was PyTorch, and the desktop computer conducting the experiments was equipped with an AMD Ryzen 5 3550X 6-Core Processor and an NVIDIA GP102 GPU. Note: The TAP estimator used in TAP loss contains 5.2106 M parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{
\begin{tabular}{c} **Revised** \\ **Inter-SubNet** \\ \end{tabular} } & **SEGAN** & **CMGAN** & **MP-SENet** & **MANNER** & **SE-Conformer** \\ \hline NB-PESQ & **3.61** & 2.16 & 3.41 & 3.50 & 3.21 & 3.13 \\ \hline STOI & 0.95 & 0.92 & **0.96** & **0.96** & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The NB-PESQ and STOI performance scores (rounded to the nearest two decimal places) of the revised Inter-SubNet (with Huber and TAP losses), SEGAN [24], CMGAN [45], MP-SENet [46], MANNER [47], and SE-Conformer [48].  
**Author Contributions:** Methodology, [PERSON]-W.H., [PERSON]-[PERSON] and [PERSON]-Y.L.; Software, [PERSON]-[PERSON] and [PERSON]-Y.L.; Validation, [PERSON]-W.H., [PERSON]-[PERSON] and [PERSON]-Y.L.; Formal analysis, [PERSON]-[PERSON] and [PERSON]; Investigation, [PERSON]-W.H. and [PERSON]-[PERSON]; Resources, [PERSON]-W.H.; Data curation, J.-W.H.; Writing--original draft, [PERSON]-W.H., [PERSON]-[PERSON] and L.-Y.L.; Writing--review & editing, J.-W.H.; Visualization, J.-W.H.; Supervision, J.-W.H.; Project administration, J.-W.H.; Funding acquisition, J.-W.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Treadmill Deck Performance Optimization Design Based on Muscle Activity during Running
## 2 Materials and Methods
### Determine the Objective Function  
According to biomechanical hypotheses, the CNS is said to be able to maintain constant impact peak and activity peak by altering muscle activity in the LLST [27]. On the other hand, under typical working conditions, the peak value of GRF is affected by the treadmill deck parameters \(k_{t}\) and \(\zeta_{t}\). Thus, the CNS may be viewed as a controller that, in order to satisfy the biomechanical assumptions, minimizes the objective function for each introduced pair of treadmill deck hardness parameters to determine the best tuning parameters (\(k_{2}\) and \(c_{2}\)). The objective function described below, proposed by [PERSON] and [PERSON], seeks to reduce the disparity between peak values of the GRF and the amplitude of leg soft tissue vibrations from their respective experimental constants during running.  
\[J_{f\sigma}=\left|\frac{P_{1}(k_{ti},\zeta_{ti})-P_{10}}{P_{10}}\right|+\left| \frac{P_{2}(k_{ti},\zeta_{ti})-P_{20}}{P_{20}}\right|+\left|\frac{\Lambda_{i}(k _{ti},\zeta_{ti})-\Lambda_{0}}{\Lambda_{0}}\right| \tag{3}\]  
\(P_{1}\) and \(P_{2}\) refer to the values of the first and second peaks in the GRF waveform, correspondingly. The default settings for GRF peaks are \(P_{10}\) and \(P_{20}\) (\(P_{10}\) = 1436.8 N, \(P_{20}\) = 2026.4 N), which are determined according to specific parameters in the LNZN model. \(\Lambda\) represents the magnitude of oscillation in the lower-limb mass, which can be determined by fitting a sine curve to the \(x_{2}\) graph. Nonetheless, research indicates that the oscillations of the wobbling mass in the lower limb do not conform to a sine wave approximation [24]. Thus, the maximum \(x_{2}\) value is regarded as the amplitude, and the default amplitude is \(\Lambda_{0}\) = 10.1 cm.  
In order to normalize the objective function, we need to determine the \(P_{1}\), \(P_{2}\), and \(\Lambda\) values determined after optimisation for the different treadmill deck parameters and the difference between their corresponding default values, and then divide by the default values. As a result, different boundaries for the regulation parameters \(k_{2}\) and \(c_{2}\) are explored to obtain the outcomes presented in this paper.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 5 Implementation Walkthrough
### Observability and Verification  
The implementation provides comprehensive traceability through token lifecycle logging, decision audit trails, and performance metric collection. Each processing step, from initial topic extraction to final action execution, is logged with metadata, enabling the complete reconstruction of the decision rationale. The formal Petri net foundation allows for static analysis to verify properties such as reachability, liveness, and fairness.  
While the TB-CSPN framework is based on Colored Petri Net semantics (see [39]), we acknowledge that the ecosystem for automated CPN verification is still evolving. CPN Tools [40] is a robust, proprietary tool, while open-source alternatives, such as SNAKES [41], support formal analysis and simulation but may lack scalability for large-scale or industrial applications.  
To address these limitations, our implementation uses a modular, multi-engine architecture that separates coordination logic from execution backends. The architecture exposes a common interface for token semantics and transition structures, enabling interchangeable execution by rule engines, Petri net simulators, and formal verification tools. Coordination logic can be validated through declarative rules or selectively tested via token tracing and localized invariants. Ultimately, the multi-engine design enhances the system's flexibility, robustness, and extensibility in both prototyping and production environments.  
This multi-engine architecture is realized in our financial news processing implementation, which is available in the public repository. The system demonstrates how modular coordination logic and layered agent interaction can be applied in practice. It generates detailed execution reports capturing LLM interactions, topic distributions, rule firings, and token generation, providing both operational insight and compliance documentation for production deployment. Complete documentation, usage examples, and benchmarking code are included to support community validation and facilitate extension of the architectural principles.