esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Guidance Law for the Mother-Cabin of the Anti-UAV Cluster Mother-Son Missile
## 2 Description and Analysis of Cooperative Guidance of Multiple Mother-Cabins
### Missile-Target Relative Motion Model  
In the longitudinal plane, the relative motion model for the cooperative interception of multiple maneuvering targets by multiple missiles is shown in Figure 2.  
In Figure 2, both the missile and the target are in the inertial frame, where \(M_{1}\), \(M_{2}\), and \(M_{3}\) represent three missiles, \(T_{1}\), \(T_{2}\), and \(T_{3}\) represent three guidance targets, \(v_{i}\) and \(\alpha_{i}\) represent the velocity and normal acceleration of missile \(i\), \(\theta_{i}\), and \(q_{i}\) represent the trajectory inclination angle and LOS of the missile \(i\), \(v_{Ti}\), and \(\theta_{Ti}\) represent the velocity and trajectory inclination angle of the target \(i\), and \(r_{i}\) is the relative distance between the missile and the target.  
**Remark 2**.: _The trajectory inclination angle \(\theta\) and the LOS \(q\) rotate counterclockwise along the reference direction to be positive, and the value range is \(-180^{\circ}\sim 180^{\circ}\)._  
Although the angular relationship between different missiles and targets is different, the relative motion relationship between them can be expressed by the following general equation:  
\[\left\{\begin{array}{l}r_{i}\dot{q}_{i}=-v_{Ti}\sin(q_{i}-\theta_{Ti})+v_{i }\sin(q_{i}-\theta_{i})\\ \dot{r}_{i}=v_{Ti}\cos(q_{i}-\theta_{Ti})-v_{i}\cos(q_{i}-\theta_{i})\end{array}\right. \tag{1}\]By applying the derivative operation to \(\dot{r}\) and \(\dot{q}\) in Equation (1), we obtain:  
\[\left\{\begin{array}{l}\ddot{r}_{i}=r_{i}\dot{q}_{i}^{2}-u_{ri}+w_{ri}\\ \ddot{q}_{i}=-2\frac{\dot{r}\dot{q}_{i}}{\tau_{i}}-\frac{u_{gi}}{\tau_{i}}+\frac {w_{gi}}{\tau_{i}}\end{array}\right. \tag{2}\]  
where \(w_{ri}=\dot{v}_{Ti}\cos(q_{i}-\theta_{Ti})+v_{Tl}\dot{\theta}_{Ti}\sin(q_{i}- \theta_{Ti})\) and \(w_{qi}=-\dot{v}_{Ti}\sin(q_{i}-\theta_{Ti})+v_{Tl}\dot{\theta}_{Ti}\cos(q_{i }-\theta_{Ti})\) are the acceleration of the target in the direction of LOS and the normal direction of LOS, respectively, and \(u_{ri}=\dot{v}_{i}\cos(q_{i}-\theta_{Ti})+v_{i}\dot{\theta}_{Ti}\sin(q_{i}- \theta_{Ti})\) and \(u_{qi}=-\dot{v}_{i}\sin(q_{i}-\theta_{Ti})+v_{i}\theta_{Ti}\cos(q_{i}-\theta _{Ti})\) are the acceleration of the missile in the direction of the LOS and the normal direction of the LOS, respectively.  
Establish the nonlinear state equations of missile group member \(M_{i}\), where \(x_{1i}=r_{i}\), \(x_{2i}=\dot{r}_{i}\), \(x_{3i}=q_{i}-q_{d}\), \(x_{4i}=\dot{q}_{i}\), and \(q_{d}\) is the desired terminal LOS of the missile \(M_{i}\).  
\[\left\{\begin{array}{l}\dot{x}_{1i}=x_{2i}\\ \dot{x}_{2i}=x_{1i}x_{4i}^{2}-u_{ri}+w_{wi}\\ \dot{x}_{3i}=x_{4i}\\ \dot{x}_{4i}=-\frac{2x_{2i}}{x_{1i}}x_{4i}-\frac{u_{gi}}{x_{1i}}+\frac{w_{gi} }{x_{1i}}\end{array}\right. \tag{3}\]  
Using Equation (3) to describe the multi-missile system can directly express the motion state of the missile and facilitate the design of the cooperative guidance law.
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 1 Introduction
Thus, the DML minimizes the number of proxies, to reduce as much as possible the overall system resource consumption (the coherency of data that reside on proxies can be achieved in a variety of ways [13] and is beyond the scope of this paper). More specifically, our contributions are the following:  
* We propose a distributed data management approach to store data in a number of locations in an industrial environment, as opposed to the current industrial state-of-the-art approaches where all  
Figure 1: A typical industrial IoT network for condition monitoring. data are centrally stored and served from a unique location. We exploit the DML for minimizing the number of proxies in an industrial IoT network and to reduce as much as possible the overall system resource consumption. * We provide a multi-faceted performance evaluation, both through experiments and through simulations, for achieving scales much larger than what available experimental conditions allow. At first, we implement the DML with 95 real devices and evaluate its performance on the FIT IoT-LAB testbed [14]. Then, we use the simulation model, validate it against the experimental results and evaluate the DML performance in larger network sizes and more general topologies. * We demonstrate that the proposed method (i) guarantees that the access latency stays below the given threshold and (ii) significantly outperforms traditional centralized and even distributed approaches, both in terms of average data access latency and in terms of maximum latency guarantees. * We also demonstrate an additional flexibility of the proposed approach, by showing that it can be tuned both to guarantee that the average of the mean latency stays below \(L_{\text{max}}\) or that the average of the worst-case latency stays below \(L_{\text{max}}\). Roadmap of the paper: In Section 2, we provide a brief summary of concepts related to this paper. In Section 3, we provide the model of the settings we consider, as well as the necessary notation. In Section 4, we introduce the DML and the problem that it addresses. In Section 5, we evaluate the performance of the DML in comparison with two other methods used in industrial environments. We also validate the simulation model used afterwards. In Section 6, we present simulation results in scenarios that are not possible to evaluate with the available experimental testbeds. Finally, we conclude and provide insights for future work in Section 7.
## 6 Results and Discussion
### Effect of Dust Conditions on OAM/OCDMA-FSO System  
The performance of our proposed system is evaluated under various dust conditions, LD, MD, and HD, in this subsection. As dust storms pull the dust from the ground up into the air, this leads to smaller visibility range and higher attenuation value than that caused by fog conditions.  
This attenuation causes signal degradation during the transmission of information. The values of BER are plotted versus different FSO links under different dust conditions for our proposed model in Figure 8a-c.  
Comparing Figure 8 with Figure 9, one can notice that the maximum achievable FSO range for our proposed system is shorter in the case of various dust storms than in the case of fog conditions.  
The maximum propagation ranges for all six channels under LD, MD, and HD are 315 m, 105 m, and 40 m, respectively, with BER less than \(10^{-5}\).  
Table 7 summarizes the _log_(BER) values at maximum achieved propagation range for our proposed model under different dust conditions (315 m under LD, 105 m under MD, and 40 m under HD).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Channels**} & \multirow{2}{*}{**OAM Beams**} & \multicolumn{4}{c}{_log_(BER)} \\ \cline{3-5}  & & **LF** & **MF** & **HF** \\  & & **(600 m)** & **(425 m)** & **(340 m)** \\ \hline Channel \#1 & & \(-25.41\) & \(-25.22\) & \(-24.9\) \\ \hline Channel \#2 & \(LG_{0,0}\) & \(-5.18\) & \(-5.19\) & \(-5.73\) \\ \hline Channel \#3 & & \(-9.34\) & \(-9.72\) & \(-10.12\) \\ \hline Channel \#4 & & \(-25.08\) & \(-25.5\) & \(-24.75\) \\ \hline Channel \#5 & \(LG_{0,10}\) & \(-5.25\) & \(-5.34\) & \(-5.59\) \\ \hline Channel \#6 & & \(-9.06\) & \(-9.89\) & \(-9.9\) \\ \hline \end{tabular}
\end{table}
Table 6: _Log_(BER) values under different fog conditions for OAM/OCDMA-FSO system for six channels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Channels**} & \multirow{2}{*}{**OAM Beams**} & \multicolumn{4}{c}{_log_(BER)} \\ \cline{3-5}  & & **LD** & **MD** & **HD** \\  & & **(315 m)** & **(105 m)** & **(40 m)** \\ \hline Channel \#1 & & \(-25.49\) & \(-25.18\) & \(-24.92\) \\ \hline Channel \#2 & \(LG_{0,0}\) & \(-5.59\) & \(-5.01\) & \(-  
Figure 8: _Log_(BER) against FSO link for OAM/OCDMA-FSO system under (**a**) LD, (**b**) MD, and (**c**) HD.  
Figure 9: Eye diagrams for (**a**) channel #1, (**b**) channel #2, (**c**) channel #3, (**d**) channel #4, (**e**) channel #5, and (**f**) channel #6 under HD at 40 m FSO range.
# A Comprehensive Review on Music Transcription
## 7 Applications of Music Transcription  
A successful automatic music transcription (AMT) system would allow for a wide range of interactions between people and music. It is an enabling technology with clear potential for both economic and societal impacts. The most well-known application of automatic music transcription is enabling musicians to capture the notes of an improvisational performance so that they can replicate it afterwards. AMT is also useful for generating musical styles in the absence of a score. Due to the numerous applications associated with the area, such as the automatic search and annotation of musical information, interactive music systems (e.g., computer participation in live human performances, score following, and rhythm tracking), and musicological analysis, the problem of automatic music transcription has attracted considerable research interest in recent years. The various applications for AMT are highlighted below:  
Music education: It can be used to make a system that can help for the automatic tutoring of various types of instruments [104; 105].  
Music creation: It can be used to improve and create the music by automatically identifying the musical notes and accompaniment [106; 107].  
Music production: It can be used for music content visualization and intelligent content-based editing [108].  
Music search: It can be used for indexing the various genres of music such as pop, rock, metal, jazz, etc. It can also be used for recommendation of various musical lines such as the melody, chord progression, bass, and rhythm [109].  
Musicology: It can be used for analyzing jazz improvisations and unannotated music [110].  
Vocal synthesis: Transcribing vocals is crucial for vocal synthesis and harmonization technologies. It allows for the creation of realistic vocal performances or harmonies through software and synthesizers [111; 112].  
Music recommendation system: The data transcribed by an AMT system can be used for music recommendation systems as tools for suggesting relevant music to the users by analyzing musical trends and listening habits [113].  
Karaoke generation: Music transcription contributes to karaoke systems allowing users to sing along accurately to their favorite songs [114; 115].  
Music research: The transcribed examples using AMT can support research scholars in analyzing the different topics of music including melody structures, rhythmic patterns, and chord and harmonic progressions [116].  
Among the applications described above, AMT systems can also be used for cover song creation and detection, music archiving and restoration, music copyright and licensing, music arrangement and orchestration, and many more music analysis-related tasks.
###### Abstract  
When rockbursts and coal and gas outbursts simultaneously occur in a coal mine, changes in gas adsorption (concentration of ambient methane) and displacement of coal and rock must occur. The co-associated minerals in coal reservoirs can affect the mechanical properties and methane adsorption capacity, which are commonly disregarded. It is important to construct compound molecular structure models of coal and rock and conduct molecular dynamic simulations to gain a microscopic understanding of underground disasters. In this work, the molecular structure models of anthracite and coking coal-rock compound models containing different contents of calcite and kaolinite were constructed, and the methane adsorption amount and mechanical properties considering temperature, pressure, and mineral contents were simulated and analysed. The results showed that the methane adsorption amount of the compound models increased rapidly, then increased moderately, and stabilized eventually with increasing adsorption pressure, and the Langmuir fitting findings were good. The saturation adsorption amount of methane in the coal models linearly decreased with increasing temperature, while the methane adsorption heat increased. The presence of minerals adsorbed a certain amount of methane, and the methane adsorption amount increased with increasing mineral contents. The mechanical properties of coal molecules changed when mineral molecules such as calcite and kaolinite were present, which had opposite contribution effects. The addition of kaolinite minerals to the coal molecular model always increased the bulk modulus and shear modulus, while the addition of calcite decreased the bulk modulus of the anthracite, causing an increase in the brittleness of the models. The results of the study further explain the adsorption behaviour and mechanical properties of methane in coal and minerals.  
coal-rock compound molecular structure models; methane adsorption; mechanical properties; coal; mineral matter; coal and gas outburst +
Footnote †: journal: Applied sciences
## 2 Materials and Methods  
### Data Management  
For this study, ECG and LVEF data were provided by a tertiary university hospital. The Institutional Review Board at Samsung Changwon Hospital approved the study protocol (IRB No. 2023-06-005). Data were collected from patients exhibiting sinus rhythm and LBBB on their ECG who had undergone echocardiographic examinations within a 2-month interval from ECG acquisition. LBBB was defined as (1) QRS duration \(\geq\) 130 ms; (2) QS or rS in lead V1 and V2; and (3) mid-QRS notching or slurring in 2 or more of leads V1, V2, V5, V6, I, and aVL [20] and confirmed by an electrophysiologist. Each patient's ECG data consists of 10-s 12-lead recordings containing 5000 samples per lead. LVSD was defined as LVEF \(\leq\) 35%, while non-LVSD was defined as LVEF \(\geq\) 50%. In total, 189 patients were classified as LVSD and 319 as non-LVSD groups. From the entire dataset, 4944 data derived from 412 patients were used for training, while 1152 derived from 96 patients were used as the test set.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 4 Channel Estimation Method Based on Deep Learning
### The Super-Resolution Denoising Network Model Based on OMP Algorithm  
In the field of signal processing, SRCNN and DNCNN are integrated into the super-resolution denoising network (SDNet) model, aiming to give full play to the super-resolution advantage of SRCNN and the efficient denoising ability of DNCNN, thereby simultaneously improving the accuracy and robustness of channel estimation. Based on this, the SDNet channel estimation framework based on the OMP algorithm was further developed. This framework consists of two stages: discrete training and online estimation. The entire network structure proposed is shown in Figure 4. In the offline training stage, after obtaining the channel estimation result of the OMP algorithm, it is input as the training set into the neural network, and the standard deep neural network training process is used to obtain a well-trained model. Subsequently, in the online estimation stage, the test data is input into the well-trained SDNet to obtain the estimated channel coefficients. Although Figure 5 shows a general process, the proposed algorithm embeds a unique design in each step. In the training network of offline training, we used the specially designed OMP algorithm combined with the deep learning channel estimation algorithm to complete the channel estimation task. During the online testing stage, well-trained networks apply SRCNN and DNCNN to improve the accuracy of channel estimation in our specific scenarios. The overall process is shown in Figure 5 as follows:
###### Abstract
Examples of CNNs for MI-EEG classification are EEGNet, ShallowConvNet, and DeepConvNet, which, like SFBCSP, also extract spatial patterns from certain frequency bands [19]. Unlike SFBCSP, the above architectures unravel nonlinear, deeper, and more complex patterns [20]. Other DL models for EEG applications include autoencoders that embed EEG signals into a generative noise-reduced feature space [21], recurrent neural networks (RNNs) that exploit the sequential nature of EEG features [22], and, more recently, transformers that use their long-term memory to capture both global and local patterns [23]. However, there are two main concerns about the medical applications of the above DL models. Firstly, they become \"black boxes\", lacking interpretable information to understand each subject's neurological abilities [24]. Secondly, they ignore the closed link between neurological, physiological, and personal behaviors [25]. Analyzing how these factors influence the model provides users and scientists with valuable information to improve the results beforehand. Thus, multimodal and multidomain strategies can couple information from patients' moods and habits to understand their neurophysiological responses and exploit this additional information to improve model performance and interpretability. This work proposes multimodal and explainable deep learning (MEDL) as an approach to MI-EEG discrimination and physiological interpretability. Specifically, our proposal is threefold: (i) Different DL models are tested for subject-dependent MI-EEG discrimination. (ii) A CAM-based approach is used to quantify and visualize relevant MI-EEG features. (iii) A questionnaire-MI performance canonical correlation analysis (QMIP-CCA) strategy is introduced as a multidomain explainability stage for physiological and MI-EEG discrimination non-linear feature matching. Experiments are carried out with the GIGAScience MI dataset due to its relatively large number of subjects and the additional questionnaire that it offers for physiological subject information [26]. The results obtained demonstrate that shallow networks achieve acceptable MI discrimination results. In addition, our CAM-based method allows us to code MI group patterns and measure EEG features of the sensorimotor cortex in people who are getting better at MI. Finally, our QMIP-CCAallows for quantifying and visualizing relevant physiological questions from tabular data and matching them to MI-EEG performance measures. The agenda for this paper is as follows. Section 2 summarizes the related work. Section 3 describes the materials and methods. Section 4 describes the experiments and results, and Section 5 presents the discussion. Lastly, Section 6 outlines the conclusions and future work.
# An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis
## 2 The Multi-Fidelity Modeling Framework
Projecting the low-fidelity part \(s_{j}^{L}\) onto the low-fidelity part of orthogonal space \(\Phi^{L}\), we obtain the basis function coefficient vector \(\overset{\rightarrow}{\alpha}\) with the least-squares method:  
\[\overset{\rightarrow}{\alpha}=\left(\left(\Phi^{L}\right)^{T}\Phi^{L}\right) ^{-1}\left(\Phi^{L}\right)^{T}s_{j}^{L} \tag{3}\]  
Applying the basis function coefficient vector to the high-fidelity part of the orthogonal basis function space \(\Phi^{H}\), we obtain the prediction of high-fidelity responses:  
\[s_{j,prediction}^{H}=\Phi^{H}\overset{\rightarrow}{\alpha} \tag{4}\]  
In this way, for an incomplete sample, only the low-fidelity response \(s_{j}^{L}\) needs to be obtained through CFD simulations; then, its corresponding high-fidelity response \(s_{j}^{H}\) can be predicted. Considering that the computational cost of low-fidelity simulations is significantly lower than that of high-fidelity simulations, this approach will result in substantial savings in simulation costs.
## 2 Materials and Methods
### Three-Dimensional Printing Technology Applications in Outpost Construction
Based on the conducted research, the following considerations are necessary to take into account:  
* Some activities, such as collecting raw regolith and feeding it into the 3D printing machine, are integral parts of the construction process. Therefore, a proactive approach is needed to mitigate the dust, which can create formidable obstacles during the printing process [22]. * The duration of the outpost's construction will be remarkably long if only one printing machine is used for the entire project [103]. As a consequence, the temperature variations, dust, and other harsh environmental factors may negatively impact the 3D printing process. Thus, considering the importance of the number of the 3D printing machines during the outpost construction process is recommended. * For the evaluation of the performance of the 3D printing system, the diversity of the chemical and mineralogical properties of regolith should also be examined [104; 105]. Since the original samples of the in situ regolith are limited, regolith simulants are used in terrestrial experiments; however, the size, shape, and composition of those simulants are not completely matched with the in situ regolith [106]. The lack of np-Fe\({}^{0}\) in the simulant is another difference [107]. Furthermore, the original sample of the regolith is taken from just one specific point, and even if the simulant is made completely similar to the real sample, it still cannot represent the entire surface of the construction site [105; 108]. * To reduce the sintering time, the problem of low gravity should be addressed since it influences regolith densification. If the material is well compacted, not only will sintering times be minimized but the density and strength of the fabricated component also increase [109].
# Optimizing CNT Loading in Antimicrobial Composites for Urinary Tract Application
## 2 Materials and Methods
### CNT/PDMS Composite Synthesis  
The CNT/PDMS composites were produced through a bulk mixing process by directly incorporating CNT-O samples into the PDMS (Sylgard 184 Part A, Dow Corning, Midland, Michigan, USA; viscosity = 1.1 cm\({}^{2}\) s\({}^{-1}\); specific density = 1.03) matrix as described by [PERSON] et al. [23].  
As the first aim of this study was to determine the most effective CNTs load to reduce bacterial biofilm formation, CNTs-O were incorporated in the PDMS matrix at 0.1, 1, 2, 3, 4 and 5% (wt%). These CNT loading values were chosen in line with previous results [23; 24] that demonstrated a significant reduction in _E. coli_ adhesion after incorporation of 0.1 and 1 wt% CNTs-O. Subsequently, in order to answer the second aim, 1, 2, 3 and 4 h ball-milled CNTs (CNT-BM 1 h; CNT-BM 2 h; CNT-BM 3 h and CNT-BM 4 h, respectively) were incorporated into the PDMS matrix at the optimal loading value to evaluate the effect of milling times in the CNTs antimicrobial properties. The ball-milling periods were selected based on the knowledge that CNTs surface area increases with increasing ball-milled periods between 1 and 4 h [26], increasing the chance for CNTs to interact and penetrate in the membranes of living bacteria [22].  
The dispersion of CNT-O aggregates was first promoted by shear mixing with a magnetic bar at 500 rpm for 30 min and then using a sonication procedure (Hielscher UP400S, at 200 Watt and 12 kHz) for 1 h. The mixture was then placed in an ultrasound bath (Selecta Ultrasonics, Lisbon, Tecnilab Portugal) for 30 min to eliminate residual air bubbles and the clusters of CNTs-O that remained. After that, the curing agent (Sylgard 184 Part B, Dow Corning, Midland, MI, USA) was added to the base-polymer (in an A:B proportion of 10:1) and carefully stirred to homogenize the two components without reintroducing bubbles. The composite materials were deposited as a thin layer (with a uniform thickness of 31 \(\pm\) 3 um) on top of glass slides through the spin coating technique (Spin150 Polos(tm), Caribbean, the Netherlands) at 6000 rpm for 1 min. Coated coupons were incubated overnight in an oven at 80 \({}^{\circ}\)C to induce the curing process. Bare PDMS surfaces (with a uniform thickness of 29 \(\pm\) 3 um) were also produced to be used as control.