esa-sceva/satcom-chunk-collection huggingface dataset:
# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 2 Materials and Methods
### Proof of Concept
#### 2.2.1 Exosuit Platform and Actuation Unit
Once the SC is engaged, any subsequent movement by the user causes elongation of the EB by the normal motion of the limbs. This motion is the primary mechanism for the effective transfer of mechanical energy between the user's limbs and the exoskeleton system. The EB elongates, storing mechanical energy, during part of the gait cycle, and then, at the end of the elongation, it releases mechanical energy back to the user in a controlled manner during its contraction. The EBs selected for the current proof of concept have the following characteristics: EB material: latex; Nominal stiffness: 0.5 \(N\)/%; EB \(L_{0}\): 25 mm. The EBs stiffness characteristic is governed by a third order function of elongation percentage as in:  
\[f_{k}(\alpha_{k})=a_{3}\Delta L_{k}^{3}+a_{2}\Delta L_{k}^{2}+a_{1}\Delta L_{k} \tag{1}\]  
with \(a_{3}=3.66*10^{-6}\), \(a_{2}=-0.002\) and \(a_{1}=0.565\). Details of the EB design, setup, calibration and usage on the Xosoft prototype, are reported in [24]. The forces generated by the EBs are a direct function of its elongation. The torque generated on the assisted joint is proportional to the EB's force, \(f_{k}\) as shown in Equation (2). \[\tau_{k}=R_{k}(\alpha_{k})f_{k}=R_{k}(\alpha_{k})K_{EB}(\Delta L_{k}^{EB}) \tag{2}\]  
The torque provided by the actuation system is strongly influenced by the moment arm \(R_{k}(\alpha_{k})\) subtended by the cord \(L_{k}\), and by the cord elongation \(\Delta L_{k}\). The cord elongation is completely supported by the EB, whereas the \(R_{k}(\alpha_{k})\), function of the joint angle is expressed as in Equation (3). \[R_{k}(\alpha_{k})= \begin{cases}r_{k}&\text{if $\alpha_{k}$ is $<0$}\\ \frac{L_{k}^{2}L_{k}^{\prime\prime}}{L_{k}(\alpha_{k})}sin(\beta_{k})&\text{if $ \alpha_{k}$ is $\geq 0$}\end{cases}  \tag{3}\]  
For further details see [24].
# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 1 Introduction
These methods are particularly effective in detecting both common and rare cell types and elucidating complex biological processes, though they may involve increased computational overhead and require careful parameter tuning. A comparative study of machine learning techniques for cell annotation is essential for driving progress in the field. Such studies are significant because they help identify the strengths and limitations of different methods across diverse datasets, guide the selection of appropriate tools for specific biological questions, and promote the development of more accurate and generalizable models for cell type identification in scRNA-seq data. [PERSON] et al. [43] conducted a systematic benchmark study of 13 supervised machine learning algorithms for cell phenotype classification using scRNA-seq data, evaluating their classification accuracy, computational efficiency, and gene selection capabilities across 43 datasets of varying sizes. Their findings provide practical guidelines for selecting appropriate ML models based on dataset scale and task requirements. Systematic comparison of machine learning algorithms is essential for improving annotation accuracy, guiding method selection, and advancing computational methodologies in single-cell research. It helps identify the most accurate methods for labelling known and novel cell types while highlighting each method's strengths and limitations across different datasets. Comparative studies provide guidelines for selecting suitable approaches based on empirical evidence, making them invaluable for handling complex biological data. They also address biases introduced by computationally derived references, ensuring that evaluations better reflect real biological systems. Additionally, such comparisons drive the development of improved algorithms and hybrid methods, fostering innovation and enhancing the reliability of single-cell data analysis. Recognizing this need, in this study, we conducted a comparative study of machine learning techniques, including decision tree, SVM, kNN, random forest, and transformers, which have been applied in various computational tools for single-cell annotation. Using several datasets, we evaluated these techniques by comparing their F1 scores and accuracy to validate their performance.
## 2 Materials and Methods
### Feature Engineering  
Here, we extended the feature engineering approach from our papers [25; 26] by introducing additional features of the EEG signals. Each 60 s interval was considered as an event characterized by a vector of 300 values. These values correspond to the spectrum of WP in the 1-30 Hz band divided into bins with a 0.1 Hz step.  
To reduce the complexity of the data, we implemented principal component analysis (PCA) [32]. In PCA, it is important to assess the number of PCs for the output; however, those components must have high explained variance of the original data. Thus, we applied the following algorithm: we decomposed the data into several PCs, introduced a threshold for explained variance--90%, then chose a minimum number of components that could explain a high enough variance. The dependence of the minimum explained variance on the number of PCs is shown in Figure 1. The number of PCs was chosen to be four. Together four PCs could explain at least 98% and 90% of the variance in the original data for the 3 Hz and 30 Hz frequency bands, respectively. In practice, we do not need to use all 300 values of initial WP, but instead can use only four PCs to train the model. This approach simplifies the work of the model, since the number of features used for classification is reduced, while we lose a fairly low percentage of initial information.  
Based on the data, we proposed several features for the ML algorithm, including ones obtained through PCA:  
* 30 Hz raw--the vector of all 300 values of the WP in the 1-30 Hz band;
* 3 Hz raw--the sub-vector of 30 values of the WP in the 1-3 Hz band;
* 30 Hz mean--the mean value of the WP in the 1-30 Hz band;
* 3 Hz mean--the mean value of the WP in the 1-3 Hz band;
* 30 Hz PCA--PCA-based feature obtained after decomposing the WP in the 1-30 Hz band and considering four PCs that explain 90% of the variance;
* 3 Hz PCA--PCA-based feature obtained after decomposing the WP in the 1-3 Hz band and considering four PCs that explain 98% of the variance.  
We explain chosen features with the following reasoning. It is a well-known fact that epileptic seizures represent abnormal excessive neuronal activity in the brain [2]. Abnormality suggests that EEG signals during seizure episodes differ greatly from normal EEG signals. Hence, the properties of the EEG spectrum should reflect these differences as well. Thus, for spectrum properties, we considered:  
* the overall distribution of the WP across frequencies reflected by the feature 30 Hz raw;
* the mean value of the WP in the spectrum reflected by the feature 30 Hz mean;
* the WP values at dominant frequencies reflected by the feature 30 Hz PCA.  
Figure 1: The dependence of the minimum explained variance on the number of PCs for the 3 Hz (green line) and 30 Hz (blue line) frequency bands.  
Recent works have demonstrated that epileptic activity is associated with characteristic frequency bands in both animal [33] and human subjects [24; 25; 26]. For human epilepsy, the band is considered to be 1-3 Hz, so we added three features to reflect spectrum properties in this band: 3 Hz raw, 3 Hz mean, and 3 Hz PCA.
## 5 Results Analysis
### Analysis of Supporting and Grouting Pressure
#### 5.2.2 Foundation Settlement under Different Grouting Pressures  
Figure 15 shows the variation in ground deformation with excavation distance under different grouting pressures. During the excavation of the left tunnel, changes in grouting pressure have minimal influence on ground deformation. However, when the right tunnel is excavated, excavation of the left tunnel has already been completed. At this time, an increase in grouting pressure leads to reduced settlement caused by tunnel excavation. Specifically, when the grouting pressure at the tunnel crown is 0.9 times and 1.2 times the vertical stress of the same-depth ground layer after excavation stabilization, the difference in settlement is approximately 1 mm. Compared to changes in excavation support pressure, changes in grouting pressure have a more noticeable impact on ground settlement, which is consistent with the findings of references [31,32]. However, the large deformation modulus of the reinforced foundation appears to mitigate this impact to some extent in this study. The settlements for P1 ZD-S and P1 ZD-N range from 3 mm to 4 mm and 4 mm to 5 mm, respectively, while the settlements for P4 ZD-S and P4 ZD-N range from 1.4 mm to 1.7 mm and 1.7 mm to 2.4 mm, respectively.  
Figure 14: Transverse foundation settlement under different support pressures.  
Figure 13: Foundation settlement curve under different support pressures. (**a**) 1-hole; (**b**) 4-hole.  
Figure 16 shows the ground settlement beneath the axis of the 1-hole under different grouting pressures. It can be observed from the figures that ground settlement gradually increases with an increase in grouting pressure, which is consistent with the research conclusions of [PERSON] et al. [33]. The maximum difference in settlement caused by different grouting pressures is less than 0.5 mm. This indicates that different excavation support pressures and grouting pressures have a minimal effect on lateral ground settlement. Due to the influence of the excavation sequence, the maximum settlement has shifted approximately 5 m northward after the completion of the twin-line tunnel excavation, positioning it along the axis of the right tunnel.
## 2 Analysis of the Characteristics and Current State of Turnout Systems in Urban Railways  
### Definition of Functions for Main Locations in Turnout System  
As is shown in Figure 1, a turnout system is divided into a switch, closure, and crossing panels. On a railway, a switch panel is composed of a normal rail and switch toes, a closure panel is composed of a closure rail, and a crossing panel comprises crossing, check, and stock rails.  
The switch panel, as the only component with movable rails, is where switch toes are installed for changing the running direction of the train; the location of the rail is frequently moved by a mechanical system. Subsequently, the closure panel guides the train to run in the direction set on the switch panel. Regarding the closure panel: although the horizontal alignment is curved, the cant is not set to balance the curve, resulting in considerable side-to-side vibrations of the train and generating a large centrifugal force.  
Conversely, to enable trains to enter the through and diverging lines, a discontinuity zone inevitably exists with a partial discontinuation of the railway track. The nose of the crossing is also where the impact of the driving wheels is directly applied; hence, this is a vulnerable part frequently subjected to various types of damage. Additionally, the turnout system is a complex structure comprising rails and sleepers with diverse shapes. It includes rail joints used to connect different types of rails, as well as fastening systems and bolts for the connections between individual components.  
Figure 1: Various components of the turnout system: (**a**) front overview of turnout system and (**b**) components of the crossing panel.  
Although a turnout system is subject to large and small damages, it is difficult to accurately identify the causes of damage considering that the system comprises various components. Therefore, it is important to understand the components that are subject to recurrent damage as well as the causes of the damage to conduct more efficient systematic maintenance of the components vulnerable to damage.  
Figure 2 shows the typical non-rail components present in a turnout system. Note that A is the sole plate, B1 is the joint bolt, B2 is the joint plate, C1 and C2 are the stretcher bars, D is the crossing baseplate, E is the fastening component, F is the check rail bracket baseplate, and G is the sleeper.  
The wooden sleeper turnout analyzed in this study is a turnout with a manganese steel crossing installed on a concrete roadbed. The turnout numbers are F8, F10, and F12. F is a check rail bracket baseplate which fixes the check rail bracket, as shown in Figure 2. The dimensions of F8, F10, and F12 are 180 \(\times\) 371 mm, 180 \(\times\) 360 mm, and 180 \(\times\) 400 mm, respectively.  
Considering the types of turnout systems installed on the concrete roadbed, shown in Figure 3, there are three types installed on the main lines of urban railways: crossover, double junction, and scissors.
# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 2 Signal Model  
As shown in Figure 1, a typical PBR system based on DAB satellite contains a reference channel and a surveillance channel. The former's antenna points towards the illuminator to record the pure direct signal. Due to the high signal-to-noise ratio (SNR) from the antenna gain and the reconstruction technology, both the thermal noise and the multipath distributions can be ignored during signal processing [25]. Thus, the reference signal can be expressed as  
\[s_{\text{ref}}(t)=a_{\text{ref}}d(t), \tag{1}\]  
where \(a_{\text{ref}}\) represents the complex amplitude and \(d(t)\) is the normalized direct wave.  
On the other hand, the surveillance beam covers the target area, and its recorded signal comprises the direct wave, clutter, the target echo, and receiver noise, which can be modeled as  
\[s_{\text{sur}}(t)=\sum_{i=1}^{N_{c}}\theta_{i}d(t-\tau_{i}^{\text{clu}})e^{2 \pi f_{i}^{\text{clu}}t}+\sum_{i=1}^{N_{c}}\alpha_{i}d(t-\tau_{i}^{\text{tar}}) e^{2\pi f_{i}^{\text{tar}}t}+wgn(t), \tag{2}\]  
where \(\theta_{i}\), \(\tau_{i}^{\text{clu}}\), and \(f_{i}^{\text{clu}}\) represent the amplitude, relative bistatic delay, and Doppler frequency of the \(i\)th clutter while \(a_{i}\), \(\tau_{i}^{\text{tar}}\), and \(f_{i}^{\text{tar}}\) denote the same parameters as for the \(i\)th target. \(N_{c}\) and \(N_{t}\) are the number of clutters and targets, respectively. \(wgn(t)\) indicates the receiver noise. The direct wave is regarded as a stationary clutter at the origin in the formula above without losing generality.  
For reliable target detection, the 2D-CCF of the two signals above is calculated to achieve coherent integration, for which the expression can be described as follows:  
\[\chi(\tau,f_{d})=\int_{T_{0}}^{T_{0}+T}s_{\text{sur}}(t)s_{\text{ref}}^{*}(t- \tau)e^{-2\pi f_{d}t}dt, \tag{3}\]  
where \(()^{*}\) denotes a complex conjugate. \(f_{d}\) and \(\tau\) are the relative bistatic delay and Doppler frequency of the hypothesized target. \(T\) and \(T_{0}\) represent the CPI and the beginning of  
Figure 1: System diagram of PBR based on DAB satellite.  
accumulation, respectively. The result depicted in a range-Doppler map could be processed with a constant false-alarm rate (CFAR) detector.  
Unfortunately, most of the targets' echoes is masked by the clutter component after accumulation because of the continuous illumination and the targets' weak scattering, which must be resolved prior to the detection stage.
# Energy Optimization of Functionally Redundant Robots through Motion Design
## 4 Trajectory Optimization and Results  
In this section the dynamic and the energy model of the robot are used to measure and optimize a robotic operation under the hypothesis of functional redundancy. Since the robot has six degrees of freedom, any task in which the specified motion can be described by five or less degrees of freedom can be classified as a functionally redundant one. Functionally redundant tasks are quite common in industrial applications, which often include operation such as deburring, painting or welding. In all these applications functional redundancy is the result of the irrelevant rotation of the end-effector about the approach vector, as a result of its axial symmetry. The most basic example is spray painting with a gun that produces a conic spray pattern [35], resulting in task that is described by five degrees of freedom and 1 redundant degree of freedom. Similarly, welding can be performed by varying the orientation of the welding tool relative to the workpiece [36].  
In this work the energy consumption associated with a simple task is optimized to minimize energy consumption, referring to a case in which one degree of freedom is unspecified, and two degrees of freedom are partially specified. This occurrence might happen in a painting application, in which the rotation of the end-effector of the robot about the approach vector (i.e., the roll angle) is irrelevant to the task, and the pitch and yaw angles are specified within a range, given that they have a minor impact on the spray results. The proposed method however can be adapted to cope with other situations, simply by using wider on narrower bounds on the functionally redundant degrees of freedom, which can be adjusted to obtain the preferred trade-off between the optimization goal, i.e., energy consumption, and suboptimal spray paint coverage. It is assumed, therefore, that the each task is specified by the end-effector position as three positions in the operational space and three Euler angles (roll, pitch and yaw) as:  
\[\mathbf{P}_{T}(t)=[x_{T},y_{T},z_{T},\varphi,\theta,\psi]^{T} \tag{7}\]  
Once the motion of the end-effector position and orientation is fully specified, the corresponding motion of the robot joints can be found by using a suitable inverse kinematic algorithm, that for the robot under consideration, takes a simple closed-form expression [37]. If the roll angle does not affect the task execution, and the pitch and yaw angles can vary within a specified range, the latter can be used as optimization variables in a constrained optimization routine.
## 2 Governing Equations  
The Reynolds-averaged equation for conservation of mass and momentum form the dynamic flow problem. The energy equation must be considered to take into account the heat exchange mechanisms. The main parameter used to evaluate the convective heat exchange is the Nusselt number, defined as:  
\[Nu=\frac{hB}{\lambda} \tag{1}\]  
where \(B\) is the characteristic dimension of the jet (the diameter for a round jet impingement or the width for a slot jet), \(\lambda\) the thermal conductivity of the fluid, while \(h\) is the local or average coefficient of convective heat exchange, calculated as follows:  
\[h=\frac{(total\ heat\ flux-radiative\ heat\ flux)}{\big{(}T_{null}-T_{jet} \big{)}} \tag{2}\]  
The previous dimensionless parameter (\(Nu\)) usually depends on the Reynolds number (3) and Prandtl number (4):  
\[Re_{j}=\frac{U_{j}B}{\upsilon} \tag{3}\]  
\[\Pr=\frac{\upsilon}{\alpha}=\frac{\mu\cdot c_{p}}{k} \tag{4}\]  
Figure 2: Slot jet configuration.  
where \(U_{j}\) is the average velocity of the outgoing jet. The reference properties of the fluid are taken at the jet outlet. Three turbulence models have been compared: realizable k-\(\varepsilon\), k-\(\omega\) SST and v2f. The first and the second are based on the [PERSON]'s hypothesis to model the Reynolds stress tensor and are also called \"linear eddy viscosity models\". The v2f model is a \"non-linear eddy viscosity model\".
## 4 Discussion
### The Limitations and Future Directions of the Study  
Drawing on previous research, this study firstly considered LUTs, and then systematically analyzed ecological parameters and their trends. Subsequently, the study explored the correlation between ecological parameters of different LUTs and climate variables. However, upon scrutinizing the data, the researchers identified several pertinent issues and algorithms that necessitated resolutions and optimizations. These include: (1) A focus on the influence of LULC on ecological parameters in the context of climate change. Compared with previous studies, the impact of LULC was considered, but zoning planning was still based on administrative boundaries, and human intervention was not circumvented, and the zoning planning was carried out in a purely natural environment. (2) The climatic indicators used in this research to explore correlations were too homogeneous. We did not consider the influence of factors such as the terrain, elevation, and carbon emissions on ecological parameters. (3) Future research endeavors should aim to quantify the impact of human activities to facilitate a more in-depth analysis of correlation relationships.
# Life Cycle Assessment of Mortars Produced Partially Replacing Cement by Treated Mining Residues
## 1 Introduction
However, minimal research has been conducted to environmentally support the reuse of secondary resources in the roll out of sustainable cementitious construction products [21]. The life cycle assessment (LCA) has emerged as an important decision-making support tool in the construction industry [22]. This analysis technique provides an overview on the potential environmental impacts during a product/service life cycle. The LCA methodology is defined in ISO 14040 [23] and ISO 14044+A1 [24], and should be structured as follows: (1) goal definition and scope; (2) inventory appraisal; (3) impacts evaluation; and (4) interpretation [21]. The LCA provides an estimation of cumulative impacts that can also be used to quantify the embodied energy of products, considering environmental categories defined in EN 15804+A2 [25]. The inventory is the core of the LCA and, for this reason, reliable data from environmental product declarations (EPD), peer-reviewed scientific research works, in-site quantifications, and international databases should be preferentially used [26]. The built environment involves significant pressures on global resources. In this sense, the need to develop LCA methods, that critically appraise data on products environmental burdens to leverage new business opportunities, are a top priority. The present work aimsat determining the environmental impacts that result from the incorporation of non-treated and treated mining residues, in different proportions, for cement-based mortar production. Those mortars were previously tested [20,27], presenting suitable properties for several current applications. The environmental impacts estimated and determined, based on six environmental categories, were analysed in terms of: (1) mitigation potential from mining residues uses, comparing to traditional mortar production with cement, (2) contribution of mining residues management scenarios (direct disposal, ED process and ED plus thermal treatment) in mortar manufacture, and (3) contribution of all the mortar components and respective management scenarios for the mortar lifecycle.
## 4 Deconvolution
### Feature Model Decomposition
#### 4.3.1 Definition  
Feature mode decomposition (FMD) is a relatively new signal decomposition method that aims to decompose different modes through the design of adaptive finite impulse response (FIR) filters. FMD utilizes the superiority of correlation kurtosis, taking into account the impulsivity and periodicity of signals, and is suitable for feature extraction in mechanical fault diagnosis and other fields.