esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Mechanical Feasibility Study  
In alignment with the objectives of this work, a finite element analysis was performed to represent the behavior under static load forces, considering the hand prosthesis developed and fabricated through additive manufacturing. Studies like [32] provide an important theoretical foundation for understanding elastoplastic contacts in rigid materials, contributing to the accurate modeling of mechanical structures. This type of approach allows for the evaluation of stress and strain distribution, optimizing the device's performance in real-world usage scenarios.  
This analysis was performed through computational simulation of static stresses, aiming to evaluate the mechanical responses of the prosthesis under the application of compression forces when gripping an object. The execution of the simulations involved important steps such as geometric modeling, boundary conditions and acting forces, boundary constraints, reduction and adaptation of the prosthesis model to create the mesh of the object, defining the materials that compose the prosthesis, and determining both the conditions for the fixation of the applied forces (elements and/or regions of application) and the acting forces themselves on the object.  
The three-dimensional model of the hand prosthesis was initially obtained in the \".stl\" format, a widely used standard for 3D printing, which represents the model's surface through a triangular mesh. However, for the simulations to be conducted, it was necessary to convert this mesh into a structure more suitable for computational analysis.  
Initially, the triangular mesh was converted into a quadrilateral mesh (quad mesh), which offers advantages in terms of element regularity, reducing distortions and facilitating the application of local refinements where higher stress gradients are expected. The conversion to quad mesh was performed in a way that preserved the geometric details of the prosthesis, ensuring that the critical parts of the structure, such as the finger joints and the region of connection to the arm, maintained their structural integrity.  
Next, the quadrilateral model was transformed into a parametrized solid body in the \".f3d\" format, allowing for more direct manipulation of material properties and precise definition of boundary conditions. This solidification process is essential for the simulation to account for the internal distribution of stresses, enabling a more realistic assessment of the prosthesis's mechanical responses. Additionally, this parametrization facilitates the adaptation of the model to different configurations and loads, making the study more flexible.  
Figure 15 illustrates the mesh transformation process: (a) shows the model in triangular mesh format (.stl), while (b) presents the model after conversion to a solid body in \".f3d\" format, highlighting the increased regularity of the mesh and the accuracy in the geometric representation of the prosthesis. The highlight in Figure (b) illustrates the refinement applied to the region with the highest concentration of stresses, allowing for a detailed analysis of this critical area.
## 4 Results
### Results of Wound Classification Model
#### 4.2.2 Hyperparameter Search and Model Selection  
After selecting the oversampling technique for each model, Table 6, reports the best hyperparameters for the selected ML algorithms while optimizing the F-score metrics and the number of ideal feature components selected by PCA.  
The three best ML algorithms were selected for each type of classifier; RF, SVM, and KNN achieved the best scores for CW, DW, and LW, respectively. The best algorithms remained the same for each wound type, but the optimal hyperparameters varied within the optimization metric. There are combinations of hyperparameters that achieve a better performance when giving preference to the F2 metric and others for F1.  
Figure 7: Class distribution over the three wound types, before and after the approaches to address class imbalance: (**a**) CW type; (**b**) DW type; (**c**) LW type.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Metric** & **No SMOTE** & **SMOTE** &
\begin{tabular}{c} **SMOTE +** \\ **Undersampling** \\ \end{tabular} \\ \hline \multirow{2}{*}{CW} & F1\% & \(47.5\pm 6.1\) & \(52.9\pm 5.3\) & \(56.6\pm 5.7\) \\  & F2\% & \(52.8\pm 6.4\) & \(54.3\pm 7.2\) & \(59.7\pm 5.0\) \\ \hline \multirow{2}{*}{DW} & F1\% & \(32.1\pm 6.9\) & \(32.7\pm 5.7\) & \(34.1\pm 5.5\) \\  & F2\% & \(32.2\pm 5.3\) & \(34.8\pm 6.0\) & \(56.4\pm 7.2\) \\ \hline \multirow{2}{*}{LW} & F1\% & \(56.4\pm 5.2\) & \(67.0\pm 4.0\) & \(63.4\pm 5.8\) \\  & F2\% & \(71.7\pm 4.5\) & \(77.3\pm 3.7\) & \(76.9\pm 4.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Models’ performance for the oversampling techniques regarding both optimization metrics and their respective score.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 4 Experiments Results and Discussion
### Calibration of the Binocular Camera  
Before conducting the positioning test, it is necessary to calibrate the distortion parameters of the camera. The calibration process mainly involves accurate calculation of parameters such as radial and tangential distortion, along with determining the baseline length of the camera. For this purpose, a calibration board with a checkerboard size measuring 19 mm \(\times\) 19 mm and containing 6 \(\times\) 9 corners is utilized. Table 1 presents the resulting calibration parameters.
## 1 Introduction
Here, we demonstrate the use of a transmission grating beam-splitter to split the FEL into practically identical signal and reference beams, both of which are analyzed in-parallel with the same spectrometer after interaction with the sample. Thus, both FEL fluctuations, as well as possible nonlinearities in the detector are exactly reproduced in both signals and can thus be renormalized. Unlike comparable schemes with monochromatic beams [13; 14; 15; 16], our method places the grating for spectral analysis after the sample interaction, so that the full SASE bandwidth contributes to the measurement. For a given FEL fluence on the sample, measuring the collimated specular reflection as opposed to isotropic fluorescence provides much stronger signals. The signal intensity can even be tuned by adjusting the angle of incidence to optimally exploit the detector dynamic range, since varying the angle will generally alter the spectral shape but not change the spectroscopic information. Here, we analyze the sensitivity of this experimental scheme for spectral (e.g., pump-induced) changes by evaluating pairs of simultaneously recorded reflectivity spectra of NiO at the oxygen \(K\)- and nickel \(L_{2,3}\)-edges, respectively.
## 4 Material Characterization
### Concrete Mechanical Properties  
Concrete mechanical properties were obtained at 28 and 225 days. At the age of 28 days, only compressive strength (\(f_{c,150}\)), measured on cubes with a side length of 150 mm, was determined according to [36]. At the second age, concrete was fully characterized in terms of compressive strength (\(f_{c}\)) and modulus (\(E\)), splitting tensile strength (\(f_{t,spl}\)), and total fracture energy (\(G_{F}\)). Contrary to the 28 days tests, for the later age, compressive strength was obtained both from standard cubes (\(f_{c,150}\)) and cylinders (\(f_{c,spl}\)) with 150 mm diameter and 300 mm height according to [36]. Brazilian splitting tests [37] were performed to obtain the indirect tensile strength (\(f_{t,spl}\)) on cylindrical specimens with a diameter of 150 mm and a height of 70 mm.  
The total fracture energy was obtained from three point bending tests controlled by crack mouth opening displacement (CMOD) using an extensometer of type Epsilon 3542-050M-025-HT2. Notched prisms with dimensions of \(100\times 100\times 400\) mm, a relative notch depth equal to 30 mm, notch width of approx. 4 mm, and a span of 300 mm were tested with a constant opening rate equal to 0.0001 mm/s. The displacement field of the specimen was recorded by means of 3D digital image correlation (DIC). The fracture energy was determined following the work of fracture method, using the displacements measured by DIC. The relative load point displacement is determined as the difference between the displacement field under the load point and the two displacement fields above the supports, as explained in detail in [38].  
All experimentally obtained mechanical properties are listed in Table 2 together with the corresponding coefficients of variation (CoV). Each mean value represents a mean value of 4-5 tests.
## 4 Conclusions  
This study shows that the application of a BER is an effective method of phosphorus removal from hydroponic effluent.  
Investigation showed that--depending on the pH value--dephosphorylation can be effected by electrocoagulation (in an acidic environment) or phosphorus precipitation with calcium and magnesium ions (in an alkaline environment). The total phosphorus removal efficiency increased with increasing electric current density. The lowest value was 95.1%, whereas the highest was 99.1%.  
Denitrification of hydroponic effluent is possible only with an external carbon source. Moreover, the use of an electric current of a suitable density helps to reduce the nitrite concentration, thereby increasing the rate and efficiency of denitrification. It was noted that the nitrogen removal efficiency decreased with increasing current density.  
The current flow in BER also had an impact on the macro- and micro elements' concentration. The physicochemical analysis of hydroponic effluent showed that it cannot be discharged into the environment, not only because of high nitrogen or phosphorus content, but also because the permissible concentrations of molybdenum, boron, potassium and sulphates were exceeded. Because of the disks' steel chemical composition, part of the Mn\({}^{2+}\), Cu\({}^{2+}\), Fe\({}_{\text{tot.}}\) and Mo\({}^{6+}\) ions were supplied to the effluent of the BERs, as a result of electrode corrosion. During bio-electrochemical treatment of hydroponic effluent, significant secondary contamination of hydroponic wastewater, with molybdenum ions, occurred.  
Future research should include the physicochemical analysis of the other micro elements and macronutrients in the effluent, after treatment, for a better understanding of the processes involved.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON]; investigation, [PERSON], [PERSON] and [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work was accomplished within research project no. 2019/33/N/ST8/00955 financed from the funds of the National Science Center. The study was financially co-supported in the framework of a Project no. 29.610.023-300 of the University of Warmia and Mazury in Olsztyn, Poland.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 2 Materials and Methods
### Testing Environment and Methods
#### 2.3.2 Testing Methods  
The test scenarios will consist of the following.  
1. For static obstacle testing: * The obstacle (square or cylinder) is placed on a designated marker for position on the floor (completely in front of the sensor directivity, so it covers only half of the sensor directivity and so it covers only a quarter of the sensor directivity). * The prototype and the laptop are placed on a specific distance marker. * The test for each distance and position is conducted for 30 min, so the log file will have sufficient entries to create a confusion matrix
2. For different materials testing: * Each of the materials is placed completely in front of the sensor. * The WAVE prototype and laptop are placed on the 50 cm marker. * The test will be conducted for each of the materials for 10 min.  
Since the goal is to reproduce an actual situation for a user that is visually impaired, the conducted tests will fall under the category of end-to-end black box testing. Black box testing [14] is a method of software testing where the tester examines the functionality of a system without knowing the internal workings of the prototype or its code. The testing method is chosen to execute end-to-end testing [15; 16] since the main goal is to verify the entry (in this case, the distance between the prototype and the obstacle) with the expected exit (in this case, the audio warning that the user will hear). By choosing the distances 70, 140, and 210 cm, the edge case testing [15] scenarios for the prototypes are covered; this is not the case for the HC-SR04 sensor [13] itself.  
The design and analysis of the tests took into account the channel models found in the existing literature [17].  
For each situation, the following parameters will be taken in consideration and highlighted:  
* The confusion matrix predictions--as a graphic and a table in the appendix.
* The accuracy for each distance--as a graphical representation--calculated using the following formula: \[Accuracy=\frac{\text{Number of correct readings}}{\text{Total number of readings}}\quad\times 100\] (1)
* The overall accuracy of each of the prototype calculated using the following formula: \[OverallAccuracy=\frac{\sum_{n=1}^{7}Accuracy[n]}{7}\] (2)  
Figure 6: Positioning of the square obstacle so it covers only a quarter of the measuring angle of the sensor—view from above.  
* The critical accuracy (for the 50, 70, 100, and 140 cm distances) of each of the prototype calculated using the following formula: \[CriticalAccuracy=\frac{\sum_{n=1}^{4}Accuracy[n]}{4}\] (3)
# A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites
## 3 Results
### Time-Varying Characteristics of the IFCB
To verify and compare the accuracy of the forecast model proposed, the IFCB of the day \(n+7\) was directly predicted with the day \(n\) without accounting for the IFCB period change as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Satellites**} & \multicolumn{3}{c}{**Primary Period**} & \multicolumn{3}{c}{**Secondary Period**} \\  & \(a\) & \(b\) & **RMS** & \(a\) & \(b\) & **RMS** \\ \hline G01 & \(-3.04\) & 1438.66 & 0.89 & \(-3.03\) & 721.41 & 1.11 \\ G06 & \(-2.99\) & 1442.88 & 1.49 & \(-3.02\) & 723.86 & 1.91 \\ G08 & \(-2.42\) & 1439.69 & 4.61 & \(-2.40  
In Figure 7, the computational method of both types of residuals was all relative to the real IFCB. As shown, when the IFCB prediction was implemented using the first method, the prediction residue was relatively large, and the statistics displayed that the maximum IFCB prediction residue was approximately 7.5 cm for all satellites. Especially for the G24 and G30 satellites, the RMS values reached 2.23 and 1.67 cm, respectively. The reason for this is that the peak and peak moment on different days differed greatly in the IFCB time series, causing the moment corresponding to the wave peak lagging 7 days to deviate significantly from the moment corresponding to the first day. However, when the second approach was adopted, the periodic change characteristics in the lag case was considered, and the resulting predicted residuals were significantly reduced, especially for the G24 and G30 satellites, effectively remedying the defects of the first prediction method. The average RMS of the predicted residual for all satellites was less than 0.50 cm, the minimum RMS was approximately 0.21 cm and the maximum RMS was approximately 0.79 cm. Compared to the first prediction method, the average RMS of all satellites decreased by approximately 52.5% when the second method was adopted, which better meets the requirement of high-precision for real-time multi-frequency PPP and PPP network solutions.
# Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
## 2 Converter Model and System Description
### Proposed FCS-MPC Scheme  
Using the voltage reference track as the only objective in the grid-forming converter is inadequate with a second order plant. Therefore, in order to improve the capacitor voltage quality, an improved CF is discussed here. As discussed in the previous subsection, the inability to control the derivative of capacitor voltage is a fundamental cause of high THDv. Therefore, the ideal regulator should track both the voltage reference and its derivative simultaneously, which can be included as two signals treated as two separate references as follows:  
\[V_{cf}^{*}(t)=V_{ref}^{*}sin(\omega_{ref}t)+jV_{ref}^{*}cos(\omega_{ref}t) \tag{16}\]  
where \(V_{ref}^{*}\) and \(\omega_{ref}\) are the amplitude and frequency of the reference signal, respectively. The voltage derivative reference is obtained as follows:  
\[\frac{dV_{cf}^{*}(t)}{dt}=\omega_{ref}V_{ref}^{*}cos(\omega_{ref}t)-j\omega_{ ref}V_{ref}^{*}sin(\omega_{ref}t) \tag{17}\]  
Tracking of \(V_{cf}^{*}(t)\) is already accomplished by the presented CF in Equation (15). Therefore, what remains is to design a CF, which ensures that \(\frac{dV_{c}(t)}{dt}\) tracks \(\frac{dV_{c}^{*}(t)}{dt}\). In order to ensure that the voltage derivative tracks the reference, the predicted value is needed and can be taken from the predicted currents, which are calculated directly from the discrete model of the converter:  
\[\frac{dV_{cf}(t)}{dt}=\frac{i_{L\alpha}(t)-i_{\alpha\alpha}(t)}{C_{f}}+j\frac{ i_{L\beta}(t)-i_{\alpha\beta}(t)}{C_{f}} \tag{18}\]  
It can be seen that the voltage derivative trajectory will be well tracked if the respective differences between the first and second term from Equations (17) and (18) are minimized. These equations are explicitly formulated as the following CF:  
\[g_{I}=(I_{f\alpha}(k+1)-I_{\alpha\alpha}(k)+C_{f}\omega_{ref}(V_{ref\beta}^{*}( k)))^{2}+(I_{f\beta}(k+1)-I_{\alpha\beta}(k)+C_{f}\omega_{ref}(V_{ref\alpha}^{*}( k)))^{2} \tag{19}\]  
The term \(g_{I}\) can now be added to the conventional CF in Equation (20), and the weight \(\lambda_{d}\) controls its effect:  
\[g=(V_{ref_{\alpha}}^{*}-V_{cf_{\alpha}}^{p}(k+1))^{2}+(V_{ref_{f\beta}}^{*}-V_ {cf_{\beta}}^{p}(k+1))^{2}+(\lambda_{d}*g_{I}) \tag{20}\]  
As introduced in this paper, another contribution is to find out the suitability of the frequency control techniques with both conventional and improved CFs. Therefore, in the next section, three different frequency control techniques such as, simple penalization (SP) control, Notch (N) control and Periodic (P) control will be discussed and redesigned in order to be incorporated with the improved and conventional CFs as well.
# Discrete-Time Sliding Mode Filter with Adaptive Gain
## 4 Numerical Examples  
This section reports the performance of AG-PSMF according to the following sinusoidal input and triangular input:  
\[u=\begin{cases}\sin(10t)+0.008\varepsilon(t)&\text{if $t\leq 2\,\mathrm{s}$,}\\ 3\sin(10t)+0.008\varepsilon(t)&\text{if $2\,\mathrm{s}<t\leq 4\,\mathrm{s}$,}\\ \sin(30t)+0.008\varepsilon(t)&\text{otherwise,}\end{cases} \tag{8}\]  
\[u=\begin{cases}\arcsin(\sin(10t))+0.008\varepsilon(t)&\text{if $t\leq 2\, \mathrm{s}$,}\\ 2\arcsin(\sin(10t))+0.008\varepsilon(t)&\text{if $2\,\mathrm{s}<t\leq 4\, \mathrm{s}$,}\\ \arcsin(\sin(30t))+0.008\varepsilon(t)&\text{otherwise,}\end{cases} \tag{9}\]  
where \(\varepsilon\sim\mathcal{N}(0,1)\) is the unit white Gaussian noise with zero mean. Parameters \(F=1000\), \(H=3\), \(N_{\text{max}}=5\), \(N_{\text{c}}=4\) and \(R=0.00075\) are used. In addition, the sampling interval \(T=0.001\) s is applied.  
Figures 9 and 10 show the outputs of AG-PSMF. For comparison, the outputs of PSMF with \(F=1000\) and \(H=3\) are also included in the figures. The initial states of both filters are zeros at \(t=0\) s. The results show that both filters effectively remove the noise component for a slowly varying input, but AG-PSMF produces smaller phase lag than PSMF does, as shown in Figures 9b,c and 10b,c. For a fast varying input, i.e., the input after \(t=2\) s, the output amplitude of AG-PSMF is closer to that of signal components of inputs compared with that of PSMF, as illustrated in Figures 9d-g and 10d-g. In addition, the phase lag of AG-PSMF is smaller than that of PSMF. Such advantages of AG-PSMF is attributed to the use of adaptive gain, which optimizes the tradeoff between filtering smoothness and suppression of delay.  
Figure 9: (**a-g**) Comparison between the outputs of AG-PSMF and PSMF for a sinusoidal input that is corrupted by white Gaussian noise. (AG-PSMF: \(F=1000\), \(H=3\), \(N_{\text{max}}=5\), \(N_{\text{c}}=4\), \(R=0.00075\); PSMF: \(F=1000\), \(H=3\); \(T=0.001\) s).  
Figure 10: (**a–g**) Comparison between the outputs of AG-PSMF and PSMF for a triangular input that is corrupted by white Gaussian noise. (AG-PSMF: \(F=1000\), \(H=3\), \(N_{\rm max}=5\), \(N_{\rm c}=4\), \(R=0.00075\); PSMF: \(F=1000\), \(H=3\); \(T=0.001\) s).
## 4 Empirical Study  
We conducted an empirical study to ascertain whether our proposed Dynamic Feature Selection System (DFSS) yields feature selection algorithms that exhibit optimal performance and stability across different sectors. The comprehensive process of DFSS is illustrated in Figure 1 below.  
Figure 1: DFSS Process.  
DFSS initiates by identifying the specific sector for which feature selection is to be conducted, followed by the collection of relevant data for that sector. Subsequently, the collected data undergo a performance evaluation using a range of feature selection algorithms to determine the most effective combination. Ultimately, based on the outcomes of this performance evaluation, rankings are assigned to discern the optimal feature combination.
## 5 Numerical Results
### Packet Size  
In order to study the effect of packet size on optimal total transmission rate, total transmission power and total transmission delay, we vary the packet size from 100 Bytes to 1000 Bytes [1]. The results for the simulations using parameters in Table 1 and the cases in Table 2, are given in Figure 3.  
Figure 3: Effect of packet size on the optimal rate, power and delay.  
Figure 2: Effect of changing maximum interference \(\text{BER}_{\text{max}}\), on the optimal rate, power and delay.  
From Figure 3a-c, it can be observed that generally, an increase in packet size leads to an increase in the total transmission rate, total power and total delay, for all the four cases of Table 2. This is attributed to the fact that as packet size increases, the chances of interference with PUs also increases and also an increase in packets being corrupted due to noise becomes more likely. This therefore leads to an increase in transmission delay and also an increase in total system power and rate, due to more re-transmissions as a result of failures or collisions with PUs.  
Putting more weight on rate (case 4) yields the highest rate, power and delay, as packet size is increased.  
As observed from Figure 4a-c, using case 4 leads to a higher total power and total rate of the system, but at the expense of a higher transmission delay. However, using case 3 leads to the lowest power and rate, and a less transmission delay than for case 4.