esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Results under High-Pass Filtering  
The results obtained when applying high-pass filtering to the DESED Public evaluation set are shown in Figure 3. Three separate graphs are presented, one for each cutoff frequency (\(f_{c}\)). As expected, the general performance decreases for every class and every system when the cutoff frequency of the high-pass filter increases. In terms of macro \(F_{1}\) score, the multiresolution systems _3 res_ and _5 res_ achieve the best results for \(f_{c}=500\) Hz, similarly to the clean set. However, for \(f_{c}=1000\) Hz and \(f_{c}=2000\) Hz the highest macro \(F_{1}\) scores are obtained with some of the single-resolution systems, \(BS\) and \(T_{+}\), respectively.  
Figure 2: \(F_{1}\) scores of single-resolution systems (\(F_{++},F_{+},BS,T_{+},T_{++}\)) and multiresolution systems (_3 res_, _5 res_) over DESED Public Evaluation set. “Macro F1” shows average score over 10 target categories. Best viewed in color.
## 2 Materials and Methods
### Assessing Motion Accuracy of Baduanjin
#### 2.4.1 Classifiers
The basic architecture diagram is shown in Figure 4.  
* A BPNN has a large amount of calculation and processing time [45]. The RBFNN is also a kind of feedforward network that is trained by using a supervised training algorithm, but the calculation and processing time is lower than that of BPNN. The main advantage of the RBFNN is that it has only one hidden layer and uses the radial basis equation as the activation equation [46]. The basic architecture of RBFNN is the same as the basic architecture of BPNN, which contains three layers, namely input, hidden and output layers, as shown in Figure 4. Unlike BPNN, the Gaussian equation is used as the basic equation; therefore, in RBFNN, the general formula for the output of the RBFNN is expressed as follows: \[\mathrm{f(x)}=\sum_{i=1}^{M}\mathrm{w_{i}}\exp(\frac{\left\|x-c_{i}\right\|^{2} }{\mathrm{d}^{2}})\] (11)
* 1D-CNN  
CNN, one of the representative deep learning algorithms, is a Feedforward Neural Network with convolutional calculations and deep structure [47]. The main structure of CNN includes the input layer, hidden layer and output layer. There are main structures in the hidden layer: the convolutional layer, the pooling layer and the fully connected layer [47]. CNN can process data of different dimensions (1D-CNN, 2D-CNN and 3D-CNN). Among them, 1D-CNN can receive one-dimensional vectors through the input layer [48]. In this study, the extracted features were input into 1D-CNN as one-dimensional vectors for supervised learning. The basic architecture diagram of 1D-CNN is shown in Figure 5.  
Figure 4: The basic architecture diagram of BPNN.
## 4 Results and Discussion
### Further Discussion on the Cavity Profile Optimization
#### 4.3.2 DEP-Type Cavity Profile  
Figure 19a,b shows the influence of design parameters (Mb and Nb) on the DEP-type cavity profile under the first-stage geometric design parameters (R = 0.315 m, H = 0.009 m), while Figure 19c,d shows corresponding parameter effects under the second-stage geometric design parameters (R = 0.223 m, H = 0.0061 m). The results identified an optimal reference design range (Mb \(\in\) [1, 1.9] and Nb \(\in\) [1,15]) where minimum stress optimization was achievable. Meanwhile, the diaphragm swept cavity volume (Vmo) exhibited a progressive increase with elevated Mb and Nb values. Figure 19e,f shows the stress distributions for the first-stage and second-stage DEP-type cavities under minimum stress design conditions. Specifically, the first-stage optimized DEP-type cavity profile achieved a maximum principal stress of 179.96 MPa at the Mb of 1.1 and the Nb of 6.2617, while the second-stage optimized DEP-type cavity profile reached 175.15 MPa at the Mb of 1.1 and the Nb of 4.8523.  
Figure 17: Theoretical simulation results of diaphragm stress at the TDC and BDC positions under the case #3 condition. **(a)** 1s-DC: TDC position. **(b)** 2s-DC: TDC position. **(c)** 1s-DC: BDC position. **(d)** 2s-DC: BDC position.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### Ground-Based Observation  
Ground-based remote sensing technology is an effective tool for monitoring atmospheric greenhouse gas column concentrations calibrating satellites and studying carbon sinks. Conventional atmospheric CO2-detection methods are mostly based on ground-based greenhouse gas-observation systems, which are capable of accurate and high-frequency observations of atmospheric CO2 concentrations. Ground-based observations can be divided into two ways: ground-based observations of global atmospheric CO2 background concentration and CO2 column concentration. In the past, ground-based observation data mainly come from the WDCGG global near-surface observation network and the TCCON ground-based observation network. In recent years, the emerging COCCON network for carbon column observation has utilized portable FTIR spectrometers (EM27/SUN) to study greenhouse gas concentrations and emissions [15]. This network serves as an effective supplement to TCCON sites.
# Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm
## 5 Application of SFEDBO Algorithm
### The Pressure Vessel Design (PVD) Problem  
The PVD problem refers to finding optimal variables of the pressure vessel that satisfy the constraint conditions and utilizing these design variables to calculate the manufacturing cost of the pressure vessel. The specific definition of the PVD problem is as follows:  
\[\text{Min}f(s) =0.6224s_{1}s_{3}s_{4}+1.7781s_{2}s_{3}^{3}+3.1661s_{1}^{2}s_{4}+ 19.84s_{1}^{2}s_{3} \tag{13}\] \[g_{1} =-s_{1}+0.0193s_{3}\leq 0,\] \[g_{2} =-s_{2}+0.00954s_{3}\leq 0,\] \[g_{3} =-\pi s_{2}^{2}s_{4}-\frac{4}{3}\pi s_{3}^{3}+1296000\leq 0,\] (14) \[g_{4} =s_{4}-240\leq 0\] \[s_{1,2} \in[0,99],s_{3,4}\in[0,200]\]  
In the formula, \(s_{1}\) represents the wall thickness, \(s_{2}\) represents the head thickness, \(s_{3}\) represents the inner radius, \(s_{4}\) represents the vessel length excluding heads, and \(g_{i}(i=1,2,3,4)\) represents the four constraint conditions. \(f(x)\) is the fitness function, which indicates the manufacturing cost of the pressure vessel.  
The test results of PVD problems are presented in Table 5. The optimization outcomes obtained through the SFEDBO algorithm surpass those of the other four algorithms. The four parameters calculated by the SFEDBO algorithm result in a lower manufacturing cost compared with the other algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \(s_{1}\) & \(s_{2}\) & \(s_{3}\) & \(s_{4}\) & **Cost** & **Rank** \\ \hline SFEDBO & 0.778169 & 0.384649 & 40.31962 & 200 & 5885.332773 & 1 \\ DBO & 0.786808 & 0.387446 & 40.36973 & 199.3149 & 5949.580262 & 5 \\ GWO & 0.779234 & 0.390725 & 40.37375 & 199.4158 & 5906.994621 & 4 \\ MSADBO & 0.778976 & 0.385048 & 40.36146 & 199.4184 & 5886.715 & 2 \\ SGMDBO & 0.780421 & 0.385763 & 40.43633 & 198.3816 & 5889.194 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The result of pressure vessel design problems.
## 4 Conclusions  
Photovoltaic tiles based on yellow and green DSSCs for wavelength-selective greenhouses were first tested in outdoor conditions with a maximum air temperature of \(44.9\pm 0.9\)\({}^{\circ}\)C. Even if the yellow tile transmitted a maximum temperature that varied by only approximately 12 \({}^{\circ}\)C above the air temperature compared to 20 \({}^{\circ}\)C for the Si tile, the green tile surprisingly lowered the temperature by 3 \({}^{\circ}\)C. It was experimentally demonstrated that the DSSC based on green dye functioned as a filter to prevent heating, by the absorption of near-infrared light due to the green dye and its conversion into electrical energy. Moreover, even if the air temperature variations were significant, \(P_{max}\) underwent moderate variations in the case of DSSCs compared to Si solar cells. This result suggests that yellow and green tiles can contribute to the reduction of the cost of crop production by reducing the energy consumption of conventional sources. The photovoltaic tile based on green DSSCs could also be proposed as a smart solution to reduce the temperature inside the greenhouse, a small step in developing a strategy to adapt agriculture to advancing climate change. Furthermore, based on the values of \(\gamma\), which were \(-0.003\%/^{\circ}\)C for the Si tile, \(-0.0017\%/^{\circ}\)C for the yellow tile, and \(-0.0004\%/^{\circ}\)C for the green tile, the thermal stability of the green and yellow tiles is clearly higher than that of the Si tile under the maximum air temperature conditions.  
As a final conclusion, the photovoltaic tile concept based on yellow and green DSSCs has been experimentally validated, opening a new perspective for the development of a  
Figure 7: \(P_{max}\) vs. \(\Delta_{Irradiance}\) and temperature vs. \(\Delta_{Irradiance}\) for the tiles based on (**a,d**) yellow DSSCs; (**b,e**) green DSSCs; and (**c,f**) Si solar cells.  
wavelength-selective greenhouse and, furthermore, a geodesic dome greenhouse, energy independent and combined fully automated arboretum, as a promising agrivoltaic system.  
Future work will be focused on monitoring the microclimate of the mini greenhouses based on yellow and green tiles, respectively, as well as the plant response from the perspective of optimal plant growth and high production.  
Conceptualization, D.U. and M.M. ([PERSON]); software and formal analysis, E.I, R.R., M.M. ([PERSON]) and S.B.; validation, M.M. ([PERSON]) and A.G.; investigation, M.V. and D.U.; writing--original draft preparation, D.U.; writing--review and editing, D.U., M.M. ([PERSON]) and M.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Romanian National Authority for Scientific Research and Innovation, UEFISCDI Project No. PN-III-P2-21-PED-2019-2091, PNCDI III, and project PN 23 27 01 03.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 4 Empirical Study
### The Empirical Process  
We performed an empirical study to compare the effectiveness of five maximal formula groups, \(ER_{1}^{{}^{\prime}}\), \(ER_{5}\), GP02, GP03 and GP19. It has been proven by [PERSON] et al. [14] that all formulas in the same maximal formula group have the same performance. Therefore, it is sufficient to select only one representative formula from each group for study. In other words, if another formula is chosen, we would still observe the same results. We chose Naish1 (abbreviated as N1) from \(ER_{1}^{{}^{\prime}}\) and Wong1 (abbreviated as W1) from \(ER_{5}\) because they are the simplest formulas in their own groups. We chose GP02, GP03, and GP19 from the last three groups because there is only one maximal formula contained in each group.  
The empirical study aimed to compare the performance of these formulas in a pairwise manner. As a result, we have 10 comparison pairs. For ease of reference, we use CP1-CP10 to denote these 10 pairs, in which CP1 denotes the comparison pair between N1 and W1, CP2 for N1 and GP02, CP3 for N1 and GP03, CP4 for N1 and GP19, CP5 for W1 and GP02, CP6 for W1 and GP03, CP7 for W1 and GP19, CP8 for GP02 and GP03, CP9 for GP02 and GP19, and finally, CP10 for GP03 and GP19.  
For the selected 11 C programs, there were, altogether, 48 faulty versions selected for the empirical study. For each selected faulty version, it was executed with respect to its corresponding test suite. All of the executions of faulty programs with respect to their test suites were performed using Fedora 20 64-bit virtual machine with one processor and 4 GB of memory. After the execution, all information related to the program spectrum was recorded; namely, \(e_{f}^{i}\), \(e_{p}^{i}\), \(n_{f}^{i}\), and \(n_{p}^{i}\), for every statement \(s_{i}\) in the selected faulty version. Once all information had been collected, we then applied the five maximal risk evaluation formulas, each from its own formula group, to calculate the risk values for each statement and get the corresponding ranking lists. As a reminder, statements with the same risk value were then ordered according to their corresponding statement IDs in the faulty program. For each maximal formula \(R\) in [N1, W1, GP02, GP03, and GP19], we then divided its ranking list into three mutually exclusive subsets; namely, \(S_{B}^{R}\), \(S_{F}^{R}\), and \(S_{A}^{R}\). We then calculated the expected faulty location, \(EFL\), of the risk evaluation formula \(R\) using the formula \(EFL(R)=n_{B}^{R}+\frac{1}{2}n_{F}^{R}\).  
Once we calculated all the expected faulty locations of these five formulas, we performed our analysis on the pairwise comparison between these formulas. For each comparison pair of formulas \(R_{1}\) and \(R_{2}\), we conclude that \(R_{1}\) performs better than \(R_{2}\) on that instance of the selected faulty version when \(EFL(R_{1})<EFL(R_{2})\). When \(EFL(R_{1})=EFL(R_{2})\), the two formulas have the same performance. When \(EFL(R_{2})<EFL(R_{1})\), we conclude that \(R_{2}\) performs better than \(R_{1}\).
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
###### Abstract
The proposed graph structure is built upon the DDI corpus to model the sentence and drug mention relations and is further semantically enhanced by domain-adapting a BERT-based LM pre-trained on large-scale domain-specific corpora that generates the initial state of the graph nodes. Finally, the interactions are classified by utilizing a sub-graph, taking the context of the sentence into consideration. We evaluated our proposed approach for the classification of DDIs according to the SemEval 2013 shared task [4] on the DDI Extraction 2013 dataset. Experimental results indicate that our KG and GNN-based classification model achieves a state-of-the-art F1-score of 79.14% on the four positive classes, outperforming other methodologies. Additionally, we show that the KG information in combination with negative instance filtering can enhance the performance of our model. Table 13 shows a comparative analysis of the related studies presented in this work and our proposed approach. The remainder of this paper is organized as follows: in Section 2, we elaborate on the dataset used and describe our proposed approach in detail. In Section 3, we present the experimental setup and results and elaborate on the effectiveness and limitations of our proposed model. Finally, in Section 4, we present our conclusions and directions for future research.
# Robot Joint Vibration Suppression Method Based on Improved ADRC
###### Abstract  
This article proposes an innovative joint vibration suppression method for six-axis collaborative robots. A permanent magnet synchronous motor (PMSM) and harmonic reducer are considered as a whole system in the design. A novel active disturbance rejection control (ADRC) scheme has been implemented by applying vibration suppression measures to the low-speed end of joint reducers. The mechanism of vibration generated by robot joints was analyzed. A vibration model for harmonic reducers was built. A new extended state observer model was designed to analyze the position disturbance data of two encoders in robot joints. Vibration signals were extracted and input into the ADRC algorithm to suppress joint vibration. A six-axis robot experimental platform was built and used to explore the changes in robot vibration trajectories under different speed conditions by applying the ADRC algorithm. The experimental results clearly show that the fluctuation amplitude of the trajectory of the robot has been reduced. The experimental results clearly show that by applying the new vibration suppression algorithm, the amplitude, vibration velocity, and acceleration of the robot at low speed have decreased 0.533 mm, 10.12 mm/s, and 0.49 mm/s\({}^{2}\), respectively, and at the same time, the velocity stability of the PMSM has been improved. This article accurately evaluates the vibration suppression performance of the ADRC algorithm at different speeds of robots, effectively suppressing the vibration of robot joints.  
vibration suppression; robot joints; extended state observer; ADRC
# Implicit Algorithm of the SBSP-R Model for Predicting the Non-Unique Critical State of Soils
## 4 Influence of Material Parameters
### Case Studies  
A list of case studies is conducted to show the influence of the parameters (see Table 2), and the accuracy of the implicit algorithm of the SBSP-R model is analyzed.  
The implicit algorithm of the SBSP-R model reflects the influences of the structure strength \({p^{\prime}}_{s}\) on stress-strain and strength of normally consolidated samples (see Figure 4). The residual strength decreases as \({p^{\prime}}_{s}\) increases when OCR = 1, while the peak strength is not obviously influenced by \({p^{\prime}}_{s}\). The sample with a larger \({p^{\prime}}_{s}\) (\(c_{\text{b}}\) = 20 kPa) shows strain-softening behavior, and the sample with a smaller \({p^{\prime}}_{s}\) (\(c_{\text{b}}\) = 0 kPa) shows strain-hardening behavior. This observation is in accordance with experimental tests on intact soil with bonding [33]. Reconstituted soils without bonding prefer to show strain-hardening [6]. Note, that the strain increment in the implicit algorithm does not influence the calculated results while the results from the explicit algorithm are affected by strain increment.  
Figure 5: Influence of the reference strain \(\varepsilon_{r}\) on (**a**) stress paths and (**b**) stress-strain relationships.  
Figure 6: Structure strength decay influencing by the reference strain \(\varepsilon_{r}\).  
Figure 7: Influence of spacing ratio \(r\) on (**a**) stress paths and (**b**) critical states.  
The values of \(r\) reflect the \"distance\" between CSL and ICL. That is, the critical state (point A1) is close to the initial state (point A0) if the spacing ratio \(r=1.5\), while it is far away from the initial state (point A3 and A0) if the spacing ratio \(r=2.5\) (see Figure 7). When the spacing ratio \(r>2.5\), the implicit algorithm of the SBSP-R model has advantages in computation time and accuracy as it shows higher accuracy and consumes less computation time. The consumed computation time decreases with the increase in strain increment (see Figure 8a), and the stress-strain and stress paths are stable (see Figure 8b). The previous explicit algorithm is obviously influenced by the strain increment. The previous explicit algorithm will consume much computation time if the strain increment is small, and its results are not right if the strain increment is 1.0 \(\times\) 10\({}^{-5}\), such as point A in Figure 8a and point B in Figure 8b. Compared with the previous explicit algorithm, the implicit algorithm runs well, even the strain increment is as large as 2.0 \(\times\) 10\({}^{-3}\).
## 2 Materials and Methods
### Reference UM-VLC Channel Model
#### 2.3.6 Reference Shadowing Model  
In an LOS link with an arbitrary TX position, \(\mathbf{A}=[x_{a},y_{a},z_{a}]\), and an arbitrary RX position, \(\mathbf{B}=[x_{b},y_{b},z_{b}]\), the shadowing weight \(\Psi\), as defined in [7], is given by  
\[\Psi=\exp(-eT_{s}\cdot\Xi(\mathbf{A},\mathbf{B}))\,, \tag{55}\]  
where \(\epsilon\) is known as the intensity parameter of the Poisson process which models the number of shadowing objects in a given period of time, \(T_{s}\), and \(\mathcal{S}(\mathbf{A},\mathbf{B})\) is the expected value of the probability that the LOS link is blocked by a shadowing object in the time period \(T_{s}\). \(\Xi(\mathbf{A},\mathbf{B})\) can be expressed analytically as  
\[\Xi(\mathbf{A},\mathbf{B})=\int_{0}^{L_{y}}\int_{0}^{L_{x}}\biggl{(}\int_{ \mathcal{F}(x,y;\mathbf{A},\mathbf{B})}^{\infty}\int_{2\mathcal{D}(x,y; \mathbf{A},\mathbf{B})}^{\infty}g_{s}(u,v)\,du\,dv\biggr{)}f_{s}(x,y)\,dx\,dy, \tag{56}\]  
where \(g_{s}(u,v)\) is the joint probability density function (PDF) that describes the possible widths, \(u\), and heights, \(v\), of the shadowing obstacles. \(f_{s}(u,v)\) is the joint PDF that describes the possible \(x\)-axis coordinates, \(x\), and \(y\)-axis coordinates, \(y\), of the obstacles' projection midpoint in the \(x\)-\(y\) plane. The function \(\mathcal{D}(x,y;\mathbf{A},\mathbf{B})\) is defined as  
\[\mathcal{D}(x,y;\mathbf{A},\mathbf{B})=\frac{|(y_{a}-y_{b})x-(x_{a}-x_{b})y-x _{b}y_{a}+x_{a}y_{b}|}{\sqrt{(y_{a}-y_{b})^{2}+(x_{a}-x_{b})^{2}}}\,, \tag{57}\]  
whereas function \(\mathcal{F}(x,y;\mathbf{A},\mathbf{B})\) is defined as  
\[\mathcal{F}(x,y;\mathbf{A},\mathbf{B})=\frac{(y_{a}-y_{b})^{2}+(x_{a}-x_{b})^ {2}+(x-x_{b})^{2}+(y-y_{b})^{2}-(x-x_{a})^{2}-(y-y_{a})^{2}}{2\sqrt{(y_{a}-y_ {b})^{2}+(x_{a}-x_{b})^{2}}}+z_{b}\,. \tag{58}\]  
With these definitions, \(\Psi_{\text{LOS}}\) can be expressed as  
\[\Psi_{\text{LOS}}=\exp(-\epsilon T_{s}\cdot\Xi(\mathbf{T},\mathbf{R}))\,, \tag{59}\]  
and \(\Psi_{\text{NLOS}}^{(w)}\) as  
\[\Psi_{\text{NLOS}}^{(w)}=\exp(-\epsilon T_{s}[\Xi(\mathbf{T},\mathbf{R}_{w})+ \Xi(\mathbf{R}_{w},\mathbf{R})])\,, \tag{60}\]  
where the shadowing weights were calculated independently for the TX-reflector link and for the reflector-RX link and multiplied together, as in [7].
## 4 Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D)
### Phases of AZTRM-D
#### 4.1.1 Planning Phase  
The Planning Phase is the foundation of AZTRM-D. In this stage, we establish security, risk management, and compliance strategies _before_ any development even starts [37]. This approach ensures that security is built in from the very beginning. We do this by integrating Zero Trust (ZT) principles [26] and Risk Management Framework (RMF) methods [33] into the development process.  
A key activity here is automated threat modeling. As shown in Figure 8, the AI Analytics and Orchestration Engine ingests crucial data like system requirements and architecture blueprints, regulatory standards, and sensitive data classifications. Using these inputs, AI-driven tools analyze system requirements and architecture to predict potential attack paths and security risks [29; 71]. Instead of relying on fixed policies, AI algorithms use historical attack data and real-time threat information to dynamically adjust security parameters and controls, outputting dynamic security policies and compliance mappings [8].  
At the identity level, AI-driven analysis of user roles helps create least-privilege access policies [56]. For device security, AI-driven endpoint analytics check device health before granting access to development environments. Network security involves planning micro-segmentation strategies. Applications and workloads are designed with security in mind, and automated validation enforces these principles. Finally, for data security, AI automatically finds and classifies sensitive data, then applies the right encryption and access control policies right from the start [66].