esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Algorithms for Offline Motion Preparation  
It could be shown on the example that the B-Spline algorithms investigated require a very high computational effort when they are used for offline motion processing. This is mainly because complex equation solvers have to be implemented in the algorithms. Due to these characteristics, the use of B-Splines cannot be recommended for offline motion preparation.  
For a given accuracy of 1 \(\upmu\)m, a recursive algorithm based on monomial splines (msRECUR) is evaluated as the most well performing. Compared to the iterative monomial spline algorithm (msITER), it saves up to 32.8% of the computing operations with only 4.8% more data.  
Furthermore, it has to be summarized that as expected in all experiments msITER generates less data, while msRECUR requires the least calculation operations. If the amount of data calculated by the algorithm is more important, msITER is preferable. If the effort is more important, msRECUR should be chosen.  
Through linearization of the non-linear mechanism, for the monomial spline algorithms the computational effort was reduced significantly by 19.1% with a minimal increase in the amount  
Figure 16: Comparison of the performance of the algorithms with decoupled drive coordinates. The values of the coupled calculation are shown transparent, cf. Figure 7. (**a**) Trend of the performance as a function of the accuracy. (**b**) Performance for the relevant accuracy of 1 \(\upmu\)m.  
of data by 3.3% (\(\varepsilon=1\) um). This shows that even the simplest optimization approaches can have a considerable positive effect on performance of the algorithms.
# Campus City Project: Challenge Living Lab for Smart Cities
## 3 Campus City Challenge--Smart Energy
### Discussion on the Challenge Implementation and Results  
The students made fair use of the instrumentation that was available to them. They also designed and manufactured an interconnected system of low-cost sensors to obtain CO2 concentration, temperature, and humidity data in the cloud. In addition, they compared this data with the recommended values defined by international organizations.  
It was observed that students focused on CO2 concentrations, while their analysis does not take into account temperatures or humidity concentrations, and gave more value to the health aspect than to the energy aspect. This situation was expected, since the challenge had double goals, which in this case was not only to propose alternatives to improve indoor air quality but also to validate the energy efficiency of these solutions. One possible explanation for the student' performance could be that the time available for the challenge was not enough, nor was the emphasis given to specific topics such as sensors and not energy aspects. This is an area of opportunity for the present challenge.  
In terms of motivation, the students appeared to be encouraged and willing to show their results to share the importance of making improvements in the ventilation of the classrooms. Proof of that was submitting their work to the _Conexion Tec_ contest. It is carried out every semester organized by Tecnologico de Monterrey to connect students and industry through their classroom projects. In the next version of this challenge, designers will include tools to evaluate the effect of the challenge on the students' engagement on the proposed topics. Finally, from the technology side, students demonstrated that the classrooms that they monitored had problems in their HVAC system and that those problems affect students' performance and will limit the use of these classrooms in the post-COVID era. Future challenges will focus on providing alternative solutions for Tecnologico de Monterrey administrators.  
The following section presents the overall findings and learnings of the implementation of the Campus City and Challenge Living Lab framework. This includes the stakeholders perspective and involvement, as well as the perspectives from the professors and students that carried out the challenges.
## 8 Conclusions  
A DL-based Cooperative communication channel model for WUSNs has been presented. The use of DL has been proven to be effective in terms of accuracy when trained with an enormous quantity of information to construct smart assessments in underground wireless environments. The proposed ECCAR, EHOR, non-Cooperative, and DER methods have been used to investigate the performance of WUSN to evaluate the QoS parameters such as transmission time, throughput, and packet loss. From the results, it is understandable that DL has been working well with the larger amounts of data on hand in terms of scalability. Its efficiency also increases as the datasets increase. Furthermore, comparison of the proposed model with the existing work is also made. From the simulation, it is also inferred that the unnecessary energy consumption by individual nodes is reduced as a result of the proper utilization of the Cooperative sensor nodes. The proposed Deep Learning-based Cooperative communication channel model for WUSNs is unique as it manages data inputs constantly to determine transparent decisions in the sharing of resources. Future work will focus on issues such as the Min-Max problem of the required Quality of Service (QoS) metrics, estimation of multi-hop routes using mobile relays, and the development of deterministic channel state models using DL-based WUSN.  
Conceptualization, K.R. and C.-M.W.; methodology, D.R. and M.U., software, D.R.; validation, O.I.K. and M.U.; formal analysis, K.R., investigation, C.-M.W., C.-L.C. and O.I.K.; resources, D.R. and K.R.; data curation, D.R. and C.-L.C., writing--original draft preparation, K.R.; writing--review and editing, O.I.K.; visualization, D.R. and M.U. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (No. 51808474), the Ministry of Science and Technology in Taiwan (No. MOST 110-2218-E-305-MBK and MOST 110-2410-H-324 -004 -MY2).  
Not applicable.  
The authors declare no conflict of interest.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 3 Results  
### Gross Primary Productivity of Maize in the NCP  
There are large spatiotemporal differences in the estimated GPP in the NCP regions. For example, in 2019, the high-value GPP zones are concentrated in the southeast of the NCP (AH and JS), with values greater than 1400 g C/m\({}^{2}\) in the majority of those regions, whereas the low-value areas are primarily located in the centre area of the NCP, with many values lower than 1000 g C/m\({}^{2}\) (Appendix A, Figure A1). The average GPP showed a declining tendency from 2001 to 2011, with a decrease of up to \(-15.67\pm 5.76\) g C/m\({}^{2}\)/year, and an upward trend from 2011 to 2019, with an increase of 14.52 \(\pm\) 10.00 g C/m\({}^{2}\)/year (Figure 2). Also shown in Figure 2 is the cumulative total GPP for the summer maize-growing season in the NCP region, which increased at an average annual growth rate of 2.30 Tg C/year, from 89.63 Tg C/year in 2001 to 133.34 Tg C/year in 2019. The increase in the total GPP is primarily due to an increase in the area planted with summer maize. Summer maize planting space increased from 7.03 \(\times\) 10\({}^{4}\) ha in 2001 to 11.09 \(\times\) 10\({}^{4}\) ha in 2019, growing at an average rate of 2.14 \(\times\) 10\({}^{3}\) ha/year.
# Multi-Switching Combination Synchronization of Three Fractional-Order Delayed Systems
## 5 Conclusions  
We extended previous work [32] to investigate multi-switching combination synchronization among three non-identical fractional-order delayed systems by introducing time-delays. Based on the stability theory for linear fractional-order systems with multiple time-delays, we designed appropriate controllers to obtain multi-switching combination synchronization among three non-identical fractional-order delayed systems. The simulations are in accordance with the theoretical analysis.  
On the one hand, when applying multi-switching combination synchronization of fractional-order delayed chaotic systems in secure communications, fractional-order and time-delay can enrich systems' dynamics. On the other hand, the origin information can be separated into two parts and embedded different parts in separate drive systems via combination synchronization scheme. Besides, because the switched states are unpredictable, this synchronization scheme can increase the security of the transmitted information in secure communication. Thus, the communication security will be enhanced, which makes multi-switching combination synchronization of fractional-order delayed chaotic systems able to find better applications in security communication.  
Methodology, X.Z.; Writing--Original Draft Preparation, B.L.; Writing--Review and Editing, Y.W.; and Funding Acquisition, X.Z.  
This work was supported by the Natural Science Foundations of China under Grant No. 61463050, the NSF of Yunnan Province under Grant No. 2015 FB113.  
The authors sincerely thank the anonymous reviewers for their constructive comments and insightful suggestions.  
The authors declare no conflict of interest.  
Figure 9: Synchronization errors among the systems in Equations (24), (27) and (30) with \(k_{1}=k_{2}=k_{3}=-40\): (**a**) \(e_{112}\); (**b**) \(e_{223}\); and (**c**) \(e_{331}\).
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
## 1 Introduction  
The successful return of Chang'E-5 (CE5) has marked China as the third country to retrieve moon samples after the United States and the former Soviet Union. Studies on the returned samples have been an interesting and hot topic in geochemistry recently [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. Elemental abundance is a basic topic in geochemistry, such as in the Earth [12; 13; 14; 15; 16]; therefore, the elemental composition of CE5 samples and moon samples from the Apollo and Luna missions is very attractive to geochemists.  
On the elemental compositions of moon samples, [PERSON] et al. [17; 18; 19; 20; 21] proposed the compositions from the Apollo missions. Samples in each Apollo mission were divided into the types of soil and rock, and the average elemental concentrations were used to represent their compositions. However, the analyzed samples or sample count from each mission was mostly less than 30, and items of major oxides, trace elements, and rare earth elements (REEs) were limited, such as 11 major oxides (including Cr\({}_{2}\)O\({}_{3}\)), 14 trace elements, and 3 REEs (Table 1). In addition, [PERSON] et al. [22] proposed a set of average elemental compositions in the lunar highland, including 8 major oxides (lack of P\({}_{2}\)O\({}_{5}\) and MnO), 16 trace elements, and 14 REEs based on 4 samples from Apollo 17 and 6 samples from Apollo 16 (Table 1). [PERSON] [23] proposed the compositions of Apollo 16 soils, including 8 major oxides, 12 trace elements, and 8 REEs based on 8 sampling stations with 2-6 samples in each station (Table 1). [PERSON] and [PERSON] [24] proposed the compositions of mare basaltsand highland regolith on the Moon. The compositions of mare basalts include 8 major oxides, 10 trace elements, and 5 REEs derived from the elemental averages of mare basalts from all the Apollo and Luna missions except Apollo 16 and Luna 20. The sample count of the mare basalts is less than 200 from the Apollo missions and less than 20 from the Luna missions. The compositions of highland regolith, including 9 major oxides (lack of P\({}_{2}\)O\({}_{5}\)), 12 trace elements, and 9 REEs, were derived based on the statistical average of soils from Apollo 16, 2 regolith breccias from Apollo 14, and 7 lunar meteorites (Table 1). Although the elemental data from CE5 samples have been reported recently [1; 2; 3; 5; 6; 7], the elemental compositions or abundances of moon samples are incomplete, which were derived based on only a few analytical samples with limited items (Table 1).  
In this paper, the analytical data reported in the literature on moon samples from Apollo, Luna, and CE5 missions were compiled firstly. Then the elemental abundances of moon samples were derived based on statistical distributions of the analytical data. Thirdly, the moon samples were classified into two types according to their concentrations of CaO, and the elemental abundances of each type were also calculated. Finally, the elemental compositions of CE5 samples were compared with the newly derived elemental abundances of moon samples.
## 3 Important Factors in Efficacy of Amphiphiles
### Role of Mixed Micelles and Polymeric Micelles  
It was shown that the mixture of a non-ionic surfactant and Sodium dodecyl sulfate (SDS) (negatively charged) can form mixed micelles [92; 93]. Protein partitioning in separation systems that contained these mixed micelles were shown to provide an effective combination for preservation of enzyme activity [94], e.g., polyoxyethylene surfactants (C\({}_{12}\)EO\({}_{5}\)) and SDS. [PERSON] _et al._[27] later showed that the hydrolysis of biomass in an Argimol-treated system was positively improved when a low quantity of SDS was mixed with Argimol. This author hypothesized that the formation of the mixed micelles of non-ionic surfactants and SDS will dilute the negative impact of the free SDS in solution. SDS was shown to reduce the irreversible adsorption of Ce7A to a higher extent than any other amphiphiles studied (Tween 20, Triton X-100, Triton X-114, DOTAB, Argimol) [27]. SDS is most prevalently known as a protein unfolding agent due to its application in the SDS-PAGE method. However, SDS has been reported to affect the refolding of protein [95] and decrease the loss of activity in enzymes [96]. Assistance in enzyme solubilization and reformation of protein secondary structure, specifically \(\alpha\)-helixes at specific concentrations (>75 mM), were also reported as benefits of SDS [50].  
Recently we used polymeric-micelles (PMs) of PEG-Tween 20, PEG-Brij 30, PEG-Triton X-100, and PEG-Casein for improvement of enzymatic hydrolysis in high (\(\sim\)20%) and low lignin (\(\sim\)7.2%) containing corn stover. According to [PERSON] _et al._[52], the ethanol yield of separate saccharification and fermentation of a PMs-treated solution of pretreated corn stover was significantly improved compared to one treated with micelles only. Benefits of PMs might include improved interfacial properties such as increased solubilization of colloidal carriers [97], improved rheological properties of the solution [98] by reduction of surface tension of the solution (Figure 4) [99], and reduced hydrophobicity of reverse micelles (e.g., Triton) by ethylene oxide groups of polymers such as PEG [99] or lignocellulosic [22]. It was found that these PMs were not effective for enzymatic hydrolysis of biomass lacking lignin or alkali pretreated corn stover (7.2% lignin). The main reasons for the enhanced cellulase activity observed due to PMs of PEG-casein, PEG-Tween and PEG-Tritonwere found to be (in order of importance) the enhanced cellulase solubilization, reformation of \(\alpha\)-helix sub-structure and combination of induced cellulase solubilization, \(\alpha\)-helix reformation and chemical changes in micro-structure of biomass.
## 5 Implementation
### Recognition and Tracking Module
#### 5.3.1 Background Subtraction  
Algorithm 1 presents the steps followed by the component in charge of performing background subtraction. The background is subtracted directly from the raw image. First, background subtraction transforms the raw image to an image in grayscale (line 2 in Algorithm 1). The grayscale image allows processing less information and results in a lower processing time. After that, the grayscale image is blurred (line 3). Blurring is a technique for reducing the noise presented in the image [33]. Blur operations are made by the application of filters. In the proposed system, a Gaussian filter is applied. A Gaussian filter applies a convolution in each point of the image using a Gaussian kernel and then returns the summation as the final result. The implementation of the Gaussian filter used in the system is included in the OpenCV library. After blurring the image, the background is subtracted (line 4). Two different methods were integrated into the system for this purpose: Improved Mixture of Gaussians (MOG) [34] and \(k\)-NN [35]. MOG and \(k\)-NN are robust methods that provide support for handling dynamic backgrounds too. Both methods are included in OpenCV and a parameter in the configuration of each instance of the Recognition and Tracking module indicates which method is applied.  
```
1:frame \(\leftarrow\) raw image
2:grey image \(\leftarrow\) BGRToGrey(frame)
3:blurred image \(\leftarrow\) GaussianBlur(grey image)
4:binary image \(\leftarrow\) BackgroundSubtractor(blurred image)
5:binary image without noise \(\leftarrow\) MorphologicalOperations(binary image)
6:output \(\leftarrow\) binary image without noise
```  
**Algorithm 1** Background subtraction steps  
A binary image is obtained after background subtraction. In the binary image, some elements are detected incomplete or are too close to others, generating a not-desired union of blobs. Morphological operations are applied (line 5) to mitigate these problems. A variety of morphological operations are included in OpenCV: _erosion_ allows separating elements that appear together by small contact areas; _dilatation_ allows joining nearby elements by applying edge thickening; _opening_ consists in applying first erosion and then dilatation; and _closing_ is the result of applying first dilatation and then erosion. The result obtained after applying morphological operations is an image with less noise and better identified elements. In the proposed system, two operations are applied: erosion and dilatation. A sample of the processing is presented in Figure 3.  
Figure 3: Background subtraction steps. (**a**) Raw image. (**b**) \(k\)-NN before morphological operations. (**c**) \(k\)-NN after morphological operations.
## 5 Performance Evaluation
### Decision Model Results Analysis
#### 5.3.2 Performance of the Forgetting Factor \(\gamma\)  
In this part, we discuss the effect of different \(\gamma\) on the results and we conduct experiments for the system performance when \(\gamma\) is 0.3, 0.5, 0.7, and 0.9. As shown in Figure 7, the system performs better than the baseline method for different \(\gamma\).  
An extensive \(\gamma\) means paying more attention to historical information, while a small \(\gamma\) means paying more attention to current information. The training curves show that the convergence is faster when \(\gamma\) is smaller. This indicates that the introduction of the estimation model reduces the difficulty of deciding for the agent and allows it to focus on current information.  
Based on the throughput in Table 7, which is also the reward in the DRL framework, it can be seen that our proposed method improves 22.9% over the BER method and 3.13% over the PER method. We will explore the reasons why performance exceeds the baseline in Section 5.3.3.  
Figure 6: The system does not converge when using decision models to process information from the environment directly and make decisions.  
Figure 7: System converges under different \(\gamma\) and throughput improves with the adoption of the estimation model.
# Enhancing Diffusion-Based Music Generation Performance with LoRA
## 2 Related Work
### LoRA in Transformer-Based Large Language Models  
The effectiveness of LoRA has been validated across various transformer-based language models, such as RoBERTa, DeBERTa, and the GPT family. The primary focus of this method lies in modifying the self-attention mechanism, specifically the query (\(W_{q}\)) and value (\(W_{v}\)) projection matrices. Although AudioLDM is fundamentally designed as a diffusion model, its U-Net backbone integrates self-attention layers that resemble those in transformer decoders. This architectural similarity offers a clear rationale for adapting LoRA techniques--initially developed for language models--to the attention layers in AudioLDM [10]. In terms of target modules, prior research has consistently found that applying LoRA to \(W_{q}\) and \(W_{v}\) preserves contextual modeling abilities while keeping the feedforward network and other components frozen. This strategy effectively minimizes the computational overhead without compromising performance. Several critical studies support the practicality and efficiency of LoRA. For instance, in RoBERTa [20], LoRA achieved an accuracy score within 0.3% to 0.7% of the full fine-tuning accuracy on GLUE tasks, outperforming other lightweight methods, such as adapters and BitFit, in terms of parameter efficiency. In the case of DeBERTa XXL (1.5B) [21], rank-8 LoRA maintained state-of-the-art performance in classification and natural language inference tasks. The LoRA method also displayed strong results on GPT-2 [22], surpassing prefix tuning and adapter-based approaches in various tasks (e.g., E2E NLG and DART), even with only 0.35 million trainable parameters, which is comparable to the performance of the fully finetuned 774 million-parameter GPT-2. Moreover, when applied to GPT-3 (175B) [23], LoRA demonstrated impressive scalability, achieving superior performance over full fine-tuning in datasets (e.g., WikiSQL and MNLI), with only 4.7 million trainable parameters (rank \(r=1\) or 2). These results collectively validate the applicability of LoRA to the attention layers of AudioLDM, establishing a strong foundation for efficient genre-specific adaptation in text-to-music generation.
## 4 Experimental Evaluation
### Discussions
However, it is difficult to determine how much the communication quality is actually degraded from the results of the IP network layer. In addition, in the wireless network layer, it is difficult \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline \multirow{2}{*}{**Mb/s**} & **STA1** & **STA2** & **STA1** & **STA2** & **STA1** & **STA2** & **STA1** & **STA2** \\  & **10 m** & **10 m** & **10 m** & **30 m** & **10 m** & **50 m** & **10 m** & **70 m** \\ \hline \multirow{2}{*}{\(\mathrm{N/A}\)} & N/A & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 27.5 & 1.00 & 0.16 & 0.41 & 1.83 & 1.83 & 5.58 & 3.75 & 13.83 \\  & 385.0 & 0 & 0 & 0.08 & 0 & 1.00 & 0 & 3.50 \\  & 770.0 & 0.16 & 0 & 0 & 0.83 & 0.08 & 0.58 & 0.08 & 25.66 \\  & 962.5 & 0.08 & 0 & 0 & 0.50 & 0.16 & 0.91 & 0.08 & **54.83** \\  & 1155.0 & 0.08 & 0 & 0 & 0.83 & 0.08 & 5.25 & 0 & 2.16 \\  & 4.16 & 1.16 & 0 & 7.41 & 0.33 & **69.66** & 0.08 & 0 \\  & 1925.0 & 1.83 & 21.00 & 1.16 & 25.00 & 5.25 & 16.83 & 1.83 & 0 \\  & 2310.0 & **92.66** & **77.66** & **98.41** & **63.50** & **92.25** & 0.16 & **94.16** & 0 \\ \hline \multicolumn{8}{c}{**UDP Communications**} \\ \hline \multirow{2}{*}{**Mb/s**} & **STA1** & **STA2** & **STA1** & **STA2** & **STA1** & **STA2** & **STA1** & **STA2** \\  & **10 m** & **10 m** & **10 m** & **30 m** & **10 m** & **50 m** & **10 m** & **70 m** \\ \hline \multirow{2}{*}{\(\mathrm{N/A}\)} & N/A & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 27.5 & 0.08 & 0 & 0.16 & 0 & 0.08 & 0.50 & 1.25 & 10.66 \\  & 385.0 & 0 & 0 & 0 & 0.16 & 0 & 0.16 & 0 & 5.25 \\  & 770.0 & 0 & 0 & 0 & 0 & 0.50 & 0 & **45.16** \\  & 962.5 & 0 & 0 & 0.25 & 0.33 & 0 & 1.41 & 0 & 38.58 \\  & 1155.0 & 0.16 & 0.25 & 0.16 & 1.33 & 0 & 15.91 & 0 & 0.33 \\  & 1540.0 & 0.50 & 1.50 & 0.50 & 19.16 & 0.91 & **81.50** & 0.66 & 0 \\  & 1925.0 & 1.50 & 3.50 & 0 & **70.33** & 7.41 & 0 & 2.33 & 0 \\  & 2310.0 & **97.75** & **94.75** & **98.91** & 8.66 & **91.58** & 0 & **95.75** & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Tx bitrates detected at STA1 and STA2 (Scenario 3). to determine the degradation only from RSSI. Thus, by checking the Tx bitrate, it is possible to assess the degree of degradation from the mode of Tx bitrate.