esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analytical Calculation Procedure
### Calculation of the Magnetic Flux Density  
The calculation procedure is based on the Fourier Spatial Series approach, in which all the harmonics of the magnetic flux density are described by expressions that are based on the physical parameters of the components of the machine [27].  
The temporal and spatial distribution of the magnetic flux in the air gap due to the permanent magnets can be expressed as (1):  
\[B(t,\theta)=2\sum_{k=1}^{\infty}B_{k}\cos[pk(\omega_{\rm m}t+\theta)] \tag{1}\]  
where \(\theta\) and \(t\) are the angular position and time, \(p\) is the number of pole pairs, and \(\omega_{\rm m}\) is the rotational speed of the machine. \(B_{k}\) are the coefficients of the Fourier series, which are calculated using Equation (2):  
\[B_{k}=\frac{2\hat{B}(1-\cos(k\pi))\sin(kp\frac{\hat{\rho}_{\rm m}}{2})}{k\pi(1+ (akp)^{2})} \tag{2}\]  
where \(\hat{\rho}_{\rm m}\) is the angle pitch, and \(\hat{B}\) is the magnitude of the flux density created by the magnets, which can be calculated using Equation (3):  
\[\hat{B}=\frac{B_{\rm r}}{1+\frac{\hat{B}\mu}{l_{\rm m}}} \tag{3}\]  
where \(l_{\rm m}\) is the height of the magnets, \(\mu_{\rm r}\), \(B_{\rm r}\) are their relative permeance and remnant field, and \(g\) is the length of the air gap.  
The fringing coefficient (\(a\)) from Equation (2) is used to shape the ideal rectangular distribution of the flux density, in order to obtain a more realistic waveform, as can be seen in Figure 1. The fringing coefficient can be expressed in the function of the machine dimensions, as shown in Equation (4):  
\[a=\frac{\sqrt{g(g+\frac{h_{\rm m}}{h_{\rm r}})}}{D_{\rm ri}+2l_{\rm m}+g} \tag{4}\]  
where \(D_{\rm ri}\) is the internal diameter of the rotor.  
The expression defined in Equation (1) represents the magnetic flux density in an ideal slotless machine. However, due to the stator slots, the distribution of the air gap becomes non-uniform, which introduces a strong harmonic content to the air gap permeance. The eccentricity of the machine also modifies the air gap permeance, but as previously mentioned, the model employed in this work is limited to ideal machines, and the eccentricity is neglected. Thus, only the effect of the stator slots is considered, which can be modelled by defining a relative permeance function:  
\[\lambda(\theta)=\lambda_{0}+\sum_{p=1}^{\infty}\lambda_{v}\cos(Q_{\rm s}v\theta) \tag{5}\]  
Figure 1: Magnetic flux density waveform, with and without the fringing coefficient, from [27].  
where \(Q_{\rm S}\) is the number of stator slots, \(\lambda_{0}\) is the mean value of the permeance variation, and \(\lambda_{\rm p}\) is the Fourier coefficient of order \(v\).  
Therefore, the magnetic flux density for a slotless machine can be multiplied to the relative permeance function in order to obtain the magnetic flux density in a slotted machine.  
\[B_{\rm S}(t,\theta)=B(t,\theta)\lambda(\theta) \tag{6}\]
# A Long-Distance Communication Architecture for Medical Devices Based on LoRaWAN Protocol
## 3 The Proposed Medical Device Architecture Based on LoRaWAN Protocol
In addition, based on these data, analyses of the evolution over time of patients using the ECG acquisition device can be performed. For this analysis to be effective, devices for measuring heart rate, blood sugar, oxygen saturation, or other medical parameters that can be acquired at home without qualified personal assistance can be integrated into the m-IoT architecture.
# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
## Materials and Methods
### Wind Vector Reconstruction and Scanning Strategies
In contrast to profiling lidars, the ML-technique uses a combination of multiple lidars, which are located around the measurement location and focus their laser beams to intersect in a single location (Figure 1). Since multiple independent measurements of the radial velocity are made at the point of interest, the assumption of horizontal homogeneity of the mean flow among the different beam locations is not needed. The effect of a potential inhomogeneity within the probe volume of the ML measurement is expected to be much smaller and the beam separation problem inherent in a profiling lidar measurement is potentially overcome.
###### Abstract  
As a satellite's critical load-bearing structure, the large-scale space deployable mechanism (LSDM) is currently assembled using ground precision constraints, which ignores the difference between the ground and space environments. This has resulted in considerable service performance uncertainties in space. To improve satellite service performance, an assembly error model considering the space environment and a tolerance dynamic allocation method based on as-built data are proposed in this paper. Firstly, the factors influencing the service performance during ground assembly were analyzed. Secondly, an assembly error model was constructed, which considers the influence factors of the ground and space environment. Thirdly, on the basis of the assembly error model, the tolerance dynamic allocation method based on as-built data was proposed, which can effectively reduce the assembly difficulty and cost on the premise of ensuring service performance. Finally, the proposed method was validated in an assembly site, and the results show that the pointing accuracy, which is the core indicator of the satellite service performance, was improved from 0.068\({}^{\circ}\) to 0.045\({}^{\circ}\) and that the assembly cost was reduced by about 13.5%.  
assembly error modeling; tolerance dynamic allocation; as-built data; service performance; large-scale space deployable mechanism +
Footnote †: journal:  
0000-0001-9999-2230][PERSON]  
0000-0001-9999-2230][PERSON]  
0000-0001-9999-23  
ESA, etc., have designed many advanced gravity compensation devices or methods. Some of them, such as the Drop Tower Test [6] and Airplane Parabolic Flight Test [7], can simulate the perfect microgravity environment for a short time. However, they cannot be used for LSDM assemblies that require long working times. Other devices or methods, such as the Zero Spring Rate Mechanism [8] and Air Bearing Device [9], can only implement gravity compensation for some components of the product, which cannot completely eliminate the effect of gravity during the ground assembly process [10; 11; 12]. Therefore, gravity's unavoidable impact is introduced to the LSDM during the ground assembly process.  
The assembly error model can build a relationship between errors of form and position in the assembly process and the product's final quality index [13]. Hitherto, no relevant report that considers the gravity factor in an assembly error model to improve service performance has been found, especially for spacecraft products.
## 2 Results and Discussion
### Comparison of the Binding Abilities of ANAC074 to NRS1, NRS2, MybSt1, and NACRS  
Since the NACRS motif, containing the \"CACG\" core sequence, has been identified to be recognized by a lot of NAC TFs [13; 19; 20]. Our results showed that NAC TFs can also bind to NRS1, NRS2, and MybSt1. Therefore, the binding abilities of ANAC074 to these cis-acting elements  
Figure 4: ChIP analysis of ANAC074 binding to NRS1, NRS2, and MybSt1. PCR products amplified from the promoters of _AT2G21320_ (**A**), _AT2G31880_ (**B**), and _AT3G13470_ (**C**) were size fractionated by gel electrophoresis. The promoter of _AT2G21320_ contains NRS1 motifs and without NRS2, Mybst1 and NACR motifs; the promoter of _AT2G31880_ includes NRS2 and no NRS1 and MybSt1 and NACRS; and the promoter of _AT3G13470_ contains only MybSt1 and no NRS1, NRS2, and NACRS. Black lines represent the promoter regions for PCR amplification, and their positions in promoters are also shown. Input, Input DNA was used as the control; CHIP+: chromatin immunoprecipitated with anti-GFP antibody and PCR amplified; CHIP-: chromatin immunoprecipitated with HA antibody and PCR amplified.  
were further compared using transient expression assays in vivo. Schematic diagram of the effector and reporter constructs used in vivo transient expression assays is shown in Figure 5A. GUS activity analysis showed that ANAC074 displayed the lowest binding ability to NRS1, followed by MybSt1, NRS2, and NACRS (Figure 5B). But no remarkable differences existed in binding activity between NRS2 and NACRS (Figure 5B). These results demonstrated that ANC074 binds to NACRS, NRS1, NRS2, or MybSt1 with different affinities.
## 4 SA Enablers and Interface Design Parameters  
Following the findings by [PERSON] and [PERSON] [13], there are three design features to consider when presenting information for enabling SA in AR/MR environments, i.e., three SA enablers, which are as follows:  
1. _Amount (scope) and detail of information_. This refers to what information to present to support SA. 2. _Location of information_. This refers to where to place the information to increase SA. 3. _Design of information_. This concerns how to display and interact with information in AR/MR. These SA enablers represent the design challenges that need to be addressed to achieve effective SA in AR/MR. To translate these SA enablers into actionable design parameters, a thematic content analysis of the SA and IA theoretical frameworks was conducted. This process involved mapping the specific theoretical components within the SA and IA areas to the corresponding SA enablers they address, as depicted in Figure 2.  
From Figure 2, the amount (scope) and detail of the information enabler is directly addressed by the SA requirements analysis area from the SA framework, which defines  
Figure 2: Defining the SA-enabling AR/MR interface design parameters. the information needed for decision-making and goal attainment. This analysis yields the description and type parameters for the \"content\" of the interface, which define and categorize the required information, respectively. The location of information and the design of information enablers are addressed by three of the six areas of the IA framework, as shown in Figure 2. The situated analytics area, which focuses on associating digital information into the physical world, directly underpins the spatial association parameter for the \"presentation\" of the interface. Likewise, the IA areas of spatial immersion (concerning visual representation) and embodied data exploration (concerning natural interaction) provide the theoretical foundation for the visualization and the AR/MR interaction parameters, respectively, which define the \"interaction\" aspects of the interface. This association led to the proposal of six interface design parameters, grouped into three categories, as presented in Table 1.  
Design guidelines for the proposed parameters are outlined in the SA framework and the associated IA areas from the IA framework, as detailed in Section 5.3. The parameters and their design guidelines steer designers toward developing design concepts that specify how to present the information that will be included in the SA-oriented AR/MR interface. The categories, parameters, and information required for decision-making (i.e., SA requirements) are organized into a matrix, as illustrated in Figure 3.  
The matrix is the core of the proposed interface design method. The specifics and application of the proposed method are explained in detail in the next section.
## 3 Results and Discussion
### Multi-Objective Optimization of Stress-Relief Groove Parameters
#### 3.2.1 SEGA Optimization Machine Learning Hyperparameters  
The machine learning hyperparameters optimized by SEGA are listed in Table 11, while Table 12 shows the best hyperparameters along with the results for both the training and testing sets. The observations indicate that the SVR model provides the best predictive performance. It can be seen that the SVR model optimized by SEGA works better, which is related to its kernel selection aspect that can more accurately match the nonlinear features of the data, improving the model's performance on complex data. Meanwhile, SEGA adjusts the regularization parameter of SVR to optimize the generalization ability of the model, reduce the risk of overfitting, and ensure the stability and accuracy of the model on new data. Therefore, the SVR model will be selected as the objective function. Figure 16 presents the SEGA-optimized models' iteration process for achieving the average values of MSE and R2, demonstrating the optimization effectiveness of SEGA. Overall, the SEGA-optimized SVR model performs well, with R2 values exceeding 0.9237.  
Figure 16: SEGA optimization of SVR iterative process.  
Following the above procedure, the SVR model was used to construct the mapping relationship for each objective function. Figure 17 shows the fitting effect between the predicted values and the actual values for a subset of the data, indicating a good agreement between the predictions and the true values.
## Appendix 1 Deduction of the Mean Footprint Reflectance (HCRF)  
The footprint reflectance is computed from measured downward irradiance and upward radiance. Measuring the hemispherical irradiance \(E\) [umol\(\cdot\)s\({}^{-1}\cdot\)m\({}^{-2}\)] is quite straightforward by using a 180\({}^{\circ}\)-FOV upward-looking sensor. A cosine correction diffuser is used to ensure that the quantum response of the sensor to the photon spectrum is the same in all hemispherical directions across the sensor aperture. Derivation of the upwelling reflected radiance measured by an obliquely downward-looking sensor is less straight-forward. We will discuss this in detail in the following section.
# Influence of Non-Uniform Rail Loads on the Rotation of Railway Sleepers
## 1 Introduction
### Basic Assumptions  
* A linear, two-layer track model in the vertical plane was analysed;
* The load of both rails \(q_{1}\) and \(q_{2}\) for any moment in time \(t\) and \(x\) coordinate can be presented as a sum of two elements: average load \(q_{av}\) (symmetric) and antisymmetric \(\Delta q\), i.e., (Figure 1): \[\begin{array}{l}q_{av}(x,t)=\frac{q_{1}(x,t)+q_{2}(x,t)}{2}\\ \Delta q(x,t)=q_{1}(x,t)-q_{2}(x,t)=-[q_{1}(x,t)-q_{2}(x,t)]\end{array}\] (1)
* The rails are modelled as Euler-Bernoulli beams and sleepers as stiffblocks, and fastening systems and a sleeper bed are treated as viscoelastic bonds (Figure 2)
* The assumption is that the distribution of the sleeper mass along its axis is symmetric in relation to the centre of mass, and its rotations in relation to the centre of mass are small, i.e., the vertical displacement of the sleeper under the rail can be presented as a product of the rotation angle \(\alpha\) and the distance between the cross-section directly under the rail from the sleeper's centre of mass b (Figure 1).  
Figure 1: Division of rail load into symmetric and antisymmetric components.
## 3 System Model
### The Fiber Link Model  
Due to the absorption and scatter attenuation, the signal in the fiber link decays. The link from the ONU to the OLT or the Internet is fiber. The received signal vector of the OLT or the Internet, \(\mathbf{y}_{r}\) from ONU \(j\) via fiber links is  
\[\mathbf{y}_{r}=\left(\frac{\beta}{L_{rj}}\right)*\mathbf{y}_{j} \tag{4}\]  
where \(\beta\) is the fiber attenuation coefficient, \(L_{rj}\) is the distance from ONU \(r\) to the OLT or the Internet.
# Decimal Multiplication in FPGA with a Novel Decimal Adder/Subtractor
## 3 Decimal Adder/Subtractor
### BCD/Excess-6 Subtractor  
BCD subtraction can be implemented with a similar approach. Considering two BCD digits, \(w\) and \(z\), the subtraction is correct if \(w-z\geq 0\), otherwise the result is in excess-6 and must be corrected by subtracting six to obtain a BCD number. The borrow out bit from each digit subtraction is always correct independently of whether the result as to be corrected or not. The borrow-out bit of each digit subtraction also informs if the result digit is BCD or excess-6, that is, a borrow out of 1 means that correction is needed.  
Considering again the two BCD digits, \(w\) and \(z\), the subtraction can be implemented with an adder as follows  
\[w-z=w+9^{\prime}z+1=w+(9-z)+1 \tag{3}\]  
where 9'z is the nine's complement of \(z\). Using the BCD/exccess-6 adder to execute this addition, we must add six to the equation:  
\[w-z=w+(9-z)+6+1=w+(15-z)+1=w+\overline{z}+1 \tag{4}\]  
From this equation, subtraction of two BCD digits, \(w-z\), is accomplished by adding \(w\) with the 1's complement of \(z\), \(\mathbb{Z}\), plus one.  
The decimal subtractor can be made generic with operands independently represented in BCD or in excess-6 specified by the _isbcd_ input. In this case, the inputs have to be converted according to Table 1.  
The complete BCD/excess-6 adder/subtractor is implemented with a chain of N single-digit BCD/excess-6 adder/subtractors. The carry-in of the single-digit BCD/excess-6 adder/subtractor associated with the least significant digit is connected to the operation selector. The carry-in is '0' if the selected operation is addition, and '1' otherwise. The result is the decimal addition or subtraction and an extra output for each digit to specify if it is BCD or excess-6.
## 7 IoMT Healthcare Services and Applications
### Chronic Disease Management, Medication Management, Telemedicine, and Drug Delivery  
IoMT helps manage chronic diseases by providing continuous monitoring of patients' health status and enabling early intervention. A smartphone-based platform that utilizes sensors to monitor patients with chronic obstructive pulmonary disease (COPD) was able to detect early signs of exacerbation and alert healthcare professionals, resulting in reduced hospitalizations and improved patient outcomes. Medication management systems ensure the accurate and safe delivery of medications to patients. They also provide a way to document and store patient records, ensuring that the right medications are prescribed and administered. Additionally, they ensure that patients receive the correct medications at the correct dosages. Medicine boxes are intelligently packaged for IoT-based use in medication management [93]. RFID tags are used in an e-health service architecture for medication control through an IoT network [94]. These systems also reduce the time healthcare professionals spend filling prescriptions and streamline the process of ordering and administering medications. By automating certain processes, they reduce costs and improve efficiency.  
Telemedicine also significantly improves the quality of medical care, as medical practitioners have access to medical data from various locations and can deliver better diagnoses and treatment plans for their patients. This, in turn, leads to a reduction in medical costs as doctors provide more efficient and accurate medical advice. IoMT improves drug delivery by providing real-time monitoring of drug levels and enabling personalized dosing. A smart pill that is ingested and controlled wirelessly allows healthcare professionals to monitor drug levels and adjust dosages as needed. This technology has the potential to improve medication adherence and reduce adverse effects.
## 1 Introduction
### Scope of the Research and Outline  
In the field of IFD that is related to rotating machinery, only few works deal with the SHAP value as an instrument to assess the importance of features [9; 17; 49; 78; 79]. Additionally, the literature presents no evidence of XAI analysis performed for industrial applications characterised by medium-sized bearings and the assessment of the effects of SHAP-based feature selection on the testing accuracy of classifying algorithms. To the best of the authors' knowledge, this is the first work dealing with this topic for industrial cases, involving medium-sized spherical roller bearings for high-duty applications.  
For that reason, the present work is aimed at investigating the capabilities of the SHAP method to analyse feature contributions, making their selection univocally justifiable and trackable to achieve more robust and reliable ML algorithms for IFD of industrial bearings in rotating machinery for industrial interest. To this aim, specific experimental activities were conducted on the test rig for medium-sized industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino [80].  
This paper is organised as follows: The introduction provides and overview of IFD and XAI by exploring the existing literature. Additionally, the aim of this work is described. The second section provides insight into the theoretical arguments underlying SVM, kNN, and Shapley values. The third section describes the test rig and the experimental activity conducted on medium-sized bearings. Section 4 includes the application of the SHAP analysis to the trained IFD models, whereas the fifth section provides the results and discussion on the explainability of diagnosis models and feature selection abilities. Finally, the sixth section includes the concluding remarks.
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 3 Legendre-Galerkin Matrix Method
### Solution of a Calculation Example  
Consider the following equation as a SDOF structure without damping under a prescribed load:  
\[\left\{\begin{array}{ll}0.1y^{\prime\prime}(t)+4y(t)=4(e^{-t}-e^{-15t}),\\ y(0)=1,\ \ \ y^{\prime}(0)=-1.\end{array}\right. \tag{42}\]  
The exact solution of this system is:  
\[\begin{array}{ll}y(t)=&-0.3618\sin(6.3245t)+0.1753\cos(6.3245t)\\ &+0.9756e^{-t}-0.1509e^{-15t}.\end{array} \tag{43}\]  
Choosing \(N=5\), the LGM method is used to solve the system. The matrix system can be obtained as below:  
\[(\mathbf{MAD}^{2}+\mathbf{KA})\mathbf{\Phi}_{N}(t)=\mathbf{P}\mathbf{\Phi}_{N}(t), \tag{44}\]  
where \(\mathbf{M}=0.1\) and \(\mathbf{K}=4\). Furthermore:  
\[\mathbf{D}^{2}=\left[\begin{array}{cccccc}0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 12&0&0&0&0&0\\ 0&60&0&0&0&0\\ 40&0&140&0&0&0\\ 0&168&0&252&0&0\end{array}\right],\ \ \ \mathbf{\Phi}_{N}(t)=\left[\begin{array}{c}1\\ 2t-1\\ 6t^{2}-6t+1\\ 20t^{3}-30t^{2}+12t-1\\ 70t^{4}-140t^{3}+90t^{2}-20t+1\\ 252t^{5}-630t^{4}+560t^{3}-210t^{2}+30t-1\end{array}\right], \tag{45}\]  
\[\mathbf{A}=\left[\begin{array}{cccccc}a_{0}&a_{1}&\ldots&a_{5}\end{array} \right],\ \ \mathbf{P}=\left[\begin{array}{cccccc}2.2618&-0.5503&-0.6653&0.7842&-0.6008 \end{array}\right],\]  
where \(\mathbf{\Phi}_{N}(t)\) is the shifted Legendre vector on the interval [0,1].  
Using the Galerkin matrix method, the following algebraic system of equations is obtained:  
\[\left[\begin{array}{cccccc}4&0&1.2&0&4&0\\ 0&1.33&0&2&0&5.6\\ 0&0&0.8&0&2.8&0\\ 0&0&0&0.5714&0&3.6\\ 1&-1&1&-1&1&-1\\ 0&2&-6&12&-20&30\end{array}\right]\left[\begin{array}{c}a_{0}\\ a_{1}\\ a_{2}\\ a_{3}\\ a_{4}\\ a_{5}\end{array}\right]=\left[\begin{array}{c}2.2618\\ -0.1834\\ -0.1331\\ 0.1120\\ -1\end{array}\right] \tag{46}\]  
Solving the above algebraic equations, the unknown Legendre coefficients are finally obtained as follows:  
\[a_{0}=0.60901,a_{1}=0.07098,a_{2}=0.27735,a_{3}=-0.40712,a_{4}=-0.12676,a_{5}= 0.09574.\]  
By replacing the obtained values in Equation (17), the approximate solution for \(y(t)\) is:  
\[y(t)\cong 24.1271\,t^{5}-69.1914\,t^{4}+63.2204\,t^{3}-17.6372\,t^{2}-t+1. \tag{47}\]Figure 2 illustrates the results of the exact solution as a function of time, along with its approximation. The accuracy of the LGM method compared to the exact solution can be noticed in the whole time interval.