esa-sceva/satcom-chunk-collection huggingface dataset:
# Trace CH\({}_{4}\) Gas Detection Based on an Integrated Spherical Photoacoustic Cell
###### Abstract  
This paper presents an integrated spherical photoacoustic cell (SPAC) for trace methane (CH\({}_{4}\)) gas detection. Theoretical analysis and analogue simulations are carried out to analyze the acoustic field distribution of the SPAC at resonant and non-resonant modes. The finite element simulation results based on COMSOL show that the first-order radial resonant frequency and second-order angular resonant frequency are 24,540 Hz and 18,250 Hz, respectively, which show good agreements with the formula analysis results. The integrated SPAC, together with a high-speed spectrometer and a distributed feedback (DFB) laser source, makes up a photoacoustic (PA) spectroscopy (PAS) system, which is employed for CH\({}_{4}\) detection. The minimum detection limit (MDL) is measured to be 126.9 parts per billion (ppb) at an average time of 1000 s. The proposed SPAC has an integrated, miniaturized and all-optical structure, which can be used for remote and long-distance trace gas detection.  
photoacoustic spectroscopy; spherical photoacoustic cell; CH\({}_{4}\) detection; finite element analysis +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of the Solution pH  
The effect of initial solution pH on Pb(II) sorption by non- and acid-modified clinoptiolite-rich tuffs is illustrated in Figure 10. It can be observed that the curves presented in Figure 10a, all show a similar shape. For the first group, the removal percentage of Pb(II) increased by only 1.08%, 8.60%, and 9.47% in the pH range of 2-6, when the Si/Al ratio was 4.37, 5.31, and 7.91, respectively, while the maximum Pb(II) uptake percentage reached 97.92% to 99.58% for these sorbents at a solution pH of about 5. The lower removal capacity of Pb(II) at pH = 2 is due to a lower selectivity of clinoptiolite at a low pH and the strong competition between the Pb\({}^{2+}\) and the H\({}^{+}\) for the exchangeable sites on the clinoptiolite framework. The decrease in Pb(II) sorption at higher pH values can be explained by the formation of hydrolyzed Pb(II) species (\(K_{\text{ps}}\) of Pb(OH)\({}_{2}\) = 1.43 \(\times\) 10\({}^{-20}\)) which could precipitate or by the electrostatic repulsion between a possible negative Pb(II) species (Pb(OH)\({}_{3}\)\({}^{-}\) or Pb(OH)\({}_{4}\)\({}^{2-}\)) and the negatively charged surface of the HCLI-0.5 M and HCLI-1.0 M zeolitic materials [48].  
Figure 10: Effect of solution pH on Pb(II) sorption by samples with (**a**) Si/Al = 4.37, 5.31, and 7.91 and (**b**) Si/Al = 9.05 and 9.51.  
Figure 9: Effect of dosage of non- and acid-modified clinoptiolite-rich tuff on Pb(II) removal efficiency.  
For the second group (Figure 10b), which corresponds to sorbents with a Si/Al ratio of 9.05 and 9.52, solution pH has a significant influence on the Pb(II) uptake percentage. In the pH range of 2-6, the removal percentage of Pb(II) increases progressively from 22.22% to 82.02% and from 19.48% to 67.48% for Si/Al ratio of 9.05 and 9.53, respectively. At higher pH values, Pb(II) sorption increased continuously up to 93% and 70% for HCLI-0.5 M and HCLI-1.0 M, respectively. This can be attributed to the high acidity of these materials which has a beneficial effect, disfavoring the hydrolysis of the metal ions in the solution [12].
# The Status of Space Weather Infrastructure and Research in Africa
## 2 Distribution of Space Weather Infrastructure in Africa
### Space Weather Monitoring
#### 2.1.1 Global Navigation Satellite Systems Receivers
The NOAA/CORS network in Benin included 7 stations (BJKA, BJNA, BJNI, BJPA, BJSA, BJAB, BJCO). The network was shut down in January 2020. The NIGNET network (Nigerian GNSS Reference Network) in Nigeria. It included up to 14 stations distributed in each region (BKFP, HUKP, MDGR, RECT, FPNO, ABUZ, CGGT, FUTY, OSCF, ULAG, UNEC, RUST, CLBR, GEMB). The measurements were used for geodesy, troposphere, and ionosphere. The network was shut down in 2017. The data of these two networks are available on the AFREF website. (j)OMNISTAR Differential GNSS  
OmniSTAR provides GNSS receivers for their space-based GNSS correction services that can improve the accuracy of precise positioning applications. The OMNISTAR (Trimble Private Network) GPS receivers are mainly deployed along the African coast to improve the accuracy of GPS positioning for ocean navigation, but they are still useful for space weather studies. OMNISTAR is a private commercial network of satellite-based augmentation services. The data from this network are not freely available [27].
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 3 Seismic Resolution Improvement
### Structure-Oriented Filtering  
The structure-oriented filtering technique is a significant approach for leveraging the dip and azimuth information of geological targets, thereby enhancing the attribute prediction accuracy and target detection capabilities [39]. By maintaining the essential characteristics of the original seismic signal while improving its signal-to-noise ratio, this method effectively emphasizes the discontinuity along the in-phase axis. Consequently, it yields a more pronounced image effect of faults and enhances fault identification, even for fine faults. This technique proves valuable in its ability to highlight fault-related features and supports the accurate identification of subsurface structures.  
The principle of construction-guided filtering is to add a parameter \(\varepsilon\) that is sensitive to the discontinuous region of the structure based on the tensor diffusion model. The value range is close to 1 in smooth regions and close to 0 in special structure regions, such as faults and pinch-offs [40]. First, the structure tensor is used to extract the local structure information of the image. Then, the diffusion tensor is designed according to the structure tensor. The formation azimuth and dip are obtained by multi-window dip scanning, and directional filtering is performed along the formation by parameter \(\varepsilon\), which is called the structure-oriented filtering equation [41]:  
\[\left\{\begin{matrix}\frac{\partial u}{\partial t}\left(x,y,t\right)=div\cdot \left(\varepsilon\cdot D\left(J_{p}\right)\
abla u\left(x,y,t\right)\right)\\ u\left(x,y,0\right)=u_{0}\left(x,y\right)\end{matrix}\right. \tag{4}\]  
where \(\varepsilon=\frac{Tr\left(S_{0}S_{p}\right)}{Tr\left(S_{0}\right)Tr\left(S_{p} \right)}\) represents a diffusion continuity factor, \(Tr\) represents the trace of the matrix, and \(S_{0}\) represents the initial structure tensor matrix. \(S\rho\) represents the gradient structure tensor for the current number of iterations. The equation was discretized, and the iterative formula of filtering was obtained as follows: \(u_{\left(n+1\right)}=u_{n}+\Delta t\cdot div\left(\varepsilon_{n}D_{n}\ \
abla u_{n}\right)\).  
\(n\) represents the number of iterations, and \(\Delta t\) represents the time step of the iteration. Generally, noise in seismic data can be effectively removed after 5-10 iterations. Comparing the original seismic profile and seismic profile after PCA+ structure-oriented filtering, the latter shows that the background noise is better suppressed, the breakpoint is clearer, and the fault location is clearer (Figure 3b).
Pulinets [29] have reported three main ionospheric features marked by low-latitude earthquakes: magnetic conjugacy for major cases, longitudinal asymmetry of ionospheric abnormity related to the vertical projection of epicenters, and an equatorial orientation anomaly reaction (i.e., 30\({}^{\circ}\)-40\({}^{\circ}\) latitude). And these large-scale configurations imply only one mechanism of gaseous emanations, such as radon, from the Earth's crust. Though the right amplitude and space size of a seismo-ionospheric variation have not been established, perturbations with a magnitude less than 100% and a space size less than 120 s can well depict the temporal-spatial characteristics of the seismo-ionospheric effect [27]. These promising results could contribute to the development of more reliable earthquake prediction methods and be integrated into existing earthquake prediction frameworks to enhance its accuracy. The CSES satellite was successfully launched on 2 February 2018. Some data have been tentatively considered as candidates during earthquake prediction practice to search for possible precursory signals preceding seismic activities in China since the end of April 2020. The contouring method was primarily utilized to process weekly plasma data measured via the CSES satellite. Obvious ionospheric irregularities were successfully put forward 14 days prior to the 22 May 2021, Maduo \(M_{\rm S}\) 7.4 EQ, and 11 days prior to the 8 January 2022, Menyuan \(M_{\rm S}\) 6.9 EQ [24]. However, the contouring method has gradually shown its shortcomings during the CSES tracing work: high-amplitude anomalies could cover low ones, that is, the contouring map cannot reveal all positive and negative anomalous points in the total map. Under these conditions, an automatic detection method has been alternatively put into service after the last Wushi \(M_{\rm S}\) 7.1 earthquake occurred on 23 January 2024, and obvious plasma variations were successfully detected during its aftershocks. So, in this paper, the CSES satellite is introduced in Section 2. In Section 3, the automatic detection method and its use in data processing are presented. The total tracing process of possible CSES plasma precursors before several strong aftershocks of the Wushi \(M_{\rm S}\) 7.1 earthquake by the automatic detection method is described in detail in Section 4. The Discussion and Conclusions are provided in Sections 5 and 6, respectively.
## 2 Background  
### Frame Fundamentals  
**Definition 1** ([1]).: _A collection of vectors \((f_{j})_{j\in I}\) in a Hilbert space \(\mathcal{H}\) is called a frame if there exist two constants \(0<A\leq B<\infty\), such that for all \(f\in\mathcal{H}\),_  
\[A\|f\|_{2}^{2}\leq\sum_{j\in J}|\langle f,f_{j}\rangle|^{2}\leq B\|f\|_{2}^{2}, \tag{1}\]  
_where \(J\) is an countable index set [44], \(\langle\cdot,\cdot\rangle\) denotes inner product, and \(\|\cdot\|_{2}\) denotes \(l_{2}\)-norm._In this paper, we consider \(\mathcal{H}=\mathbb{R}\) or \(\mathcal{H}=\mathbb{C}\). If the frame bounds are \(A=B\), the frame is called an \(A\)-tight frame and, if the frame bounds are \(A=B=1\), the frame is a normalized tight frame. Furthermore, if the frame \((f_{j})_{j\in J}\) meets the condition for being a tight frame and if \(\left\|f_{j}\right\|_{2}=1,\forall j\in J\), this frame is called a unit-norm tight frame.  
**Definition 2** ([8]).: _Suppose that a frame \((f_{j})_{j\in J}\) has the frame vectors \(f_{j}\in\mathbb{R}^{m}\) or \(\mathbb{C}^{m}\) for \(\forall j\in J\), where the index set size is \(\left|J\right|=N\). Then, the frame \((f_{j})_{j\in J}\) is called an equiangular tight frame if it satisfies following conditions._  
1. \(\left\|f_{j}\right\|_{2}=1\)_,_
2. \(\left|\left\langle f_{j},f_{k}\right\rangle\right|=\sqrt{\frac{N-m}{m(N-1)}}\)_, for_ \(j\
eq k\) _where_ \(\forall j,k\in J\)_._
# Elevator Fault Diagnosis Method Based on IAO-XGBoost under Unbalanced Samples
## 4 XGBoost Parameter Optimization Method Based on IAO Algorithm
### XGBoost Model  
The XGBoost model is an implementation of the gradient boosting tree algorithm, which is used to construct a model by combining multiple decision trees. In this way, models can be constructed with higher accuracy. XGBoost model performance is improved by iteratively training a series of decision trees. Each new decision tree is trained on the residuals of the previous decision trees to reduce the error of the model on the training data. XGBoost model:  
\[\widehat{y}_{i}=\sum\limits_{k=1}^{K}f_{k}(x_{i}),f_{k}\in F \tag{10}\]  
\[F=\left\{f(x)=\omega_{q(x)}\right\},q:R^{m}\to T,\omega\in R^{T}, \tag{11}\]  
where (\(x_{i}\),\(y_{i}\)) is the dataset; xi is the feature vector of the \(i\)th sample; \(y_{i}\) is the label of the \(i\)th sample; \(f_{k}\) corresponds to an independent tree model; \(F\) is the set of all trees; \(q\) is the mapping of the feature vectors to the corresponding leaf nodes; and \(\omega_{q(x)}\) denotes the leaf weights. The objective function of the model is as follows:  
\[O=\sum\limits_{i=1}^{n}l(y_{i},\widehat{y}_{i})+\sum\limits_{k=1}^{K}\Omega(f _{k}) \tag{12}\]  
\[\Omega(f)=\gamma T+\frac{1}{2}\lambda\omega^{2}, \tag{13}\]  
where \(\sum\limits_{i=1}^{n}l(y_{i},\widehat{y}_{i})\) is the training loss function; \(\widehat{y}_{i}\) is the predicted value; \(y_{i}\) is the test value; \(\sum\limits_{k=1}^{K}\Omega(f_{k})\) is the penalty function; and \(\gamma\) and \(\lambda\) are the regularization parameters.
# A Novel Robust Control System Design and Its Application to Servo Motor Drive
## 1 Introduction
Pole placement [3; 11; 15] or the linear quadrature method [5; 14; 15; 30; 31] can be used to determine the locations of system poles. Second, it is necessary to determine the control law to cause the system to move onto the sliding surface, stay on it, and slide to the desired location. Typically, a sliding surface is designed to have the performance to fit the system's requirements. However, the chattering phenomenon due to fast switching inputs cannot be avoided to keep the states on the sliding surface, which may degrade system performance [12]. Chattering eliminations are considered in [5; 8; 9; 23; 26; 27; 32]. Estimating the load torque or determining the control law through the boundary layer can reduce this chattering [9; 23; 24; 27]. A servo motor control system usually constitutes the external load or disturbance, and nonlinear phenomena such as backlash or dead-band also give rise to steady-state error. The proposed control structure is thus used for the position control of the servo motor system. The design process and simulation results are given to show the performance of the proposed system. The other contents of the paper are as follows. Section 2 presents the integral state feedback control design. Section 3 adds the sliding mode control to the existing state feedback system to improve the invariance property. The determination of SMC control law is shown in Section 4. The developed control system applied to the position control of the servo drive system is shown in Section 5. The simulation and experimental results and discussions are given in Sections 6 and 7. Finally, the conclusions are given in Section 8.
## 4 Experimentation, Performance Evaluation, and Discussion
### Parameter Settings  
The proposed HFA-K-means algorithm was set up using the following parameter settings: the population size is set using the established statistical tradition of a population size \(\geq\) 30. Therefore, a population size of \(n=40+2\)k is adopted for the FA algorithmand for the size of the dataset subsets used in the K-means phase of the HFA-K-means algorithm. The _MaxFE_ (maximum function evaluation) is 50,000; mutation coefficient \(m\) is 2; attraction coefficient \(\beta\) is 2, the coefficient for light absorption \(\gamma\) is 1; and the damping ratio \(\alpha\) for the mutation coefficient is 1. The parameter configuration for the different algorithms is detailed in Table 1.
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
Footnote †: The escalating volume of space operations and the proliferation of satellites underscore the urgent need for a pivotal shift towards sustainable space use. This paper highlights the importance of addressing space sustainability through a holistic framework. Robust international commitments, aligned with the United Nations Sustainable Development Goals (SDGs) 12 (Responsible Consumption and Production) and 13 (Climate Action), alongside the implementation of legislative measures, are essential for fostering responsible and sustainable practices in space activities. Furthermore, innovative technology advancements can potentially convert this space sustainability problem into an opportunity for the space sector. Mitigating the risk of debris is no longer sufficient. This article advocates for prioritizing sustainability in the design phase of the new missions and constellations. To achieve this objective, it is crucial to develop a comprehensive understanding of how various design parameters--such as orbital altitude, the number of satellites, the inclusion or exclusion of inter-satellite links, system interoperability, and reconfigurability--impact the sustainability of space systems. Hence, the investigation suggests creating innovative Key Performance Indicators (KPIs) that specifically target space sustainability. These KPIs would enable the evaluation of specific missions/constellations and the comparison of different design alternatives. The absence of current research on these KPIs requires the creation of new ones. This research introduces a preliminary framework for establishing these novel metrics, which can be vital for governments and companies to develop and oversee a sustainable future in space. By implementing a holistic strategy combining robust policy frameworks with cutting-edge technology solutions, we can guarantee the ongoing, secure, and environmentally responsible space utilization for future generations.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]  
1 Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy; [EMAIL_ADDRESS] (S.Q.); [EMAIL_ADDRESS] (M.D.S.); [EMAIL_ADDRESS] (T.R.); [EMAIL_ADDRESS] (M.R.)  
2 Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi PO. Box 127788, United Arab Emirates; [EMAIL_ADDRESS]  
2 Correspondence: [EMAIL_ADDRESS]  
3 Presented at the 14 th EASN International Conference on \"Innovation in Aviation & Space towards sustainability today & tomorrow\", Thessaloniki, Greece, 8-11 October 2024.
## 3 Results and Discussion
### Relevance for the Production of Volatile Compounds during Alcoholic Fermentation  
The volatile profiles of wines resulting from the alcoholic fermentation of _S. cerevisiae_ on the medium containing different concentrations of both thiamine and biotin were analyzed by HS-SPME-GC/MS, with the aim of assessing any potential effect held by those vitamins on the wine volatilome. Significant differences between the various vitamin doses have been investigated through Scheirer-Ray-Hare nonparametric tests, and hierarchical clustering of the compounds that displayed a significant reliance for either biotin or thiamine was performed using the average concentrations for those compounds at each considered vitamin level (Figure 2). Amongst the 31 compounds that were analyzed, 13 displayed a significant influence (\(p<0.05\)) of the initial biotin composition, 11 were significantly correlated (\(p<0.05\)) to the initial thiamine concentration, and only 1, methyl heptone, was significantly impacted by the interaction between both vitamins, but interestingly, not by thiamine or biotin considered individually (Tables 5 and 6). It is also relevant to note that only isovaleric acid was impacted by both initial thiamine and biotin individually.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 2 Implementation of Tracking Component
### Learning and Update  
Although the optimal filter is obtained at the original frame, it is necessary to consider the robustness in the following time instances due to the latent variation of the target's appearance and size. Usually, this can be achieved by weighted averaging the filter of all training samples but in our case, it is more reasonable to update the correlation filter at the new target position and scale after detection. An adjustment is made for the classical machine learning algorithm \(w_{t}=(1-\eta)w_{t-1}+\eta w_{t}\) where \(\eta\) is the learning rate and \(t\) represents the index of tracking view frame sequence:  
\[\hat{w_{t}^{l}}=\frac{\hat{x_{t}^{l}}\odot\hat{y}}{\sum_{k=1}^{d}(\hat{x_{t}^{ k}})^{*}\odot\hat{x_{t}^{k}}+\lambda}=\frac{A_{t}^{l}}{B_{t}+\lambda}\qquad l=1,2,3 \ldots d \tag{7}\]  
\[A_{t}^{l}=(1-\eta)A_{t-1}^{l}+\eta\hat{x_{t}^{l}}\odot\hat{y}\qquad l=1,2,3 \ldots d \tag{8}\]  
\[B_{t}=(1-\eta)B_{t-1}+\eta\sum_{k=1}^{d}(\hat{x_{t}^{k}})^{*}\odot\hat{x_{t}^ {k}}\qquad l=1,2,3\ldots d \tag{9}\]  
Equations (8) and (9) are used to update the correlation filter both for the translation and scale.  
Figure 2 has shown the tracking processing in our implementation.
## 3 Results and Discussion  
### System Development to Fabricate Long Tubular Products  
The diffusion bonding system developed in this study consists of an upper heating zone and a lower cooling zone. Figure 5 shows installed heating and cooling chambers on the shop floor. The total height of the system is 8.3 m, and spaces of heating and cooling chambers are 2.0 m \(\times\) 2.0 m \(\times\) 2.5 m, and 2.5 m \(\times\) 2.0 m \(\times\) 2.5 m, respectively.  
In the heating zone, the stacked tubes are diffusion bonded by heating under a controlled atmosphere. Figure 6 provides photographs of the upper heating zone and 180 KW moly heating elements. As the moly heating element in nitrogen atmosphere can be deteriorated at high temperature, the environmental control in the heating zone is mainly required at temperatures above 760 \({}^{\circ}\)C.  
In the cooling zone, the bonded tubular products are rapidly cooled by circulating nitrogen gas. Figure 7 provides photographs of the lower cooling zone. The diffusion-bonded product is moved to the cooling zone and cooled by nitrogen circulation. The stacked tubes positioned at the heating zone are moved to the cooling zone and cooled by nitrogen circulation.  
Figure 5: Vertical diffusion bonding system: (**a**) Upper heating zone; (**b**) Lower cooling zone.  
Figure 6: (**a**) Upper heating zone; (**b**) Moly heating elements.  
During diffusion bonding, the stacked tubes are heated and cooled under a controlled atmosphere. For the control of the workpiece movement and environmental atmosphere, the refractory valve seat, which is a movable gate valve that mates with the valve face, was used.  
The unit tubes are stacked by directly putting them together \"face-to-face.\" For tight joining, the surfaces to be joined are machined so that the outside diameter of one (proturded) is equal to the inside diameter of the other (sunken). Figure 8 shows joint preparation for best fit of contacting tubes. Unexpected power unbalances, vibrations, biased stacking, and non-uniform temperature distribution during diffusion bonding may cause distortion or buckling in stacked tubes. Figure 9 shows a sample of process-induced distortion and buckles in stacked tubes observed during preliminary tests.  
Figure 8: Joint preparation for best fit: (**a**) protruded; (**b**) sunken.  
Figure 7: Workpiece movement; (**a**) workpiece in the upper heating zone; (**b**) move down to lower cooling zone; (**c**) workpiece in the lower cooling zone.  
Figure 9: Sample of distortion and buckles in stacked tubes.  
Therefore, proper alignment or location needs to be secured with suitable support, or by applying light pressure. In this study, special supports are designed and applied to maintain the straightness and concentricity of the long tubular part. As shown in Figure 10, three round molybdenum bars are located inside of the stacked tubes, and the alignment of the bars is maintained by triangular alumina cores.