esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Results
#### 4.3.1 Pre-Processing Evaluation  
Figure 8 shows the original and pre-processed images. The experimental results demonstrate the effectiveness of the image enhancement methodology, as presented in Table 1. The metrics used for evaluation are the Structural Similarity Index (SSIM) [44] and Peak Signal-to-Noise Ratio (PSNR) [45], which are widely regarded as reliable indicators of image quality in enhancement tasks. Higher values of both the SSIM and PSNR indicate the better preservation of structural information and improved image clarity after pre-processing.  
Figure 8: 3 DIRCADb dataset. (**a**) Original CT scan; (**b**) pre-processed image.  
A comprehensive analysis of the model's performance is provided both quantitatively and qualitatively. The quantitative evaluation uses performance metrics such as accuracy, sensitivity, the Dice coefficient, and the Jaccard coefficient. The attention mechanism is also evaluated qualitatively, focusing on its impact on tumor detection.
## 3 Materials and Methods
### Methods  
Among the different strategies which can be used to evaluate information and communication technologies is an \"assessment by and from users\" [57], where the users/addressees of these technologies are explicitly requested to offer their views about the quality of them. This work is organized from this perspective. Figure 3: Image of the gigapixel of the artistic figure from the Annunciation. Source: developed by the authors. Figure 2: An example of the questions appearing in the produced software. Source: developed by the authors. The research was performed with students enrolled in a Master's degree in \"Art: idea and production\" at Seville University. The total number of participants amounted to 20--the number of students registered for that Master's degree; therefore, no sampling whatsoever was carried out; all the students belonging to the addressee population took part in the initiative. The type of study developed can be categorized as one of an experimental and exploratory nature with a single group. We developed an ad hoc information collection instrument structured in two separate parts. The first one aimed to ascertain the degree of acceptance that both AR and VR had created in the subjects after their participation in the experience; as for the second, it sought to ascertain the assessment that students made of the presented objects. We diagnosed the extent to which AR and VR technologies were accepted from the \"Technology Acceptance Model\" (TAM). It is a robust and powerful theoretical framework of users' acceptance and usage of technology [58; 59]. It is considered appropriate to use this model, which is based on the theoretical foundation of social psychology theories, including the theory of reasoned action and the theory of planned behavior. In addition to this theoretical basis, it is worth highlighting the numerous studies that have been carried out using this model. This is an added value, as it has been sufficiently tested. This model, originally formulated by [PERSON] [60], collects information from five dimensions (see Figure 4): perceived usefulness (PU); perceived ease of use (PEU); perceived enjoyment (PE); attitude towards its use (AU); and intention to use it (IU). [PERSON]'s proposal suggests that our predisposition towards the use of any technology depends on our attitude towards it, which in turn is influenced by the \"perceived ease of use\", the \"perceived usefulness\" and the \"perceived enjoyment\". This model has been utilized to analyze the degree of acceptance of various technologies, and more specifically for AR [14; 59; 60; 61; 62; 63; 64; 65; 66]. It is likewise worth stressing that the TAM model proved to be truly robust when evaluated by means of structural equations [67]. This first instrument consisted of 14 items with a Likert-type construction (4 for perceived usefulness; 3 for perceived ease of use; 3 for perceived enjoyment; 2 for the attitude towards its use; and 2 for the intention to use it), with seven response options, ranging from \"Extremely unlikely/I totally disagree\" (1) to \"Extremely likely/I totally agree\" (7).
## 3 Metabolic Profiling of Urine: Recent Trends
### MEPS and SPME Advantages for the Metabolic Profile of Urine
This allows for a sophisticated therapeutic drug monitoring that is critical for the success of the treatment of many diseases, particularly CVDs, OD and NDDs [69]. Several relevant reports on this subject are described in Table 2. Additionally, some applications using SPE will be also highlighted, which would greatly benefit from an upgrade to the MEPS methodology. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Target diseases or analytes**} & **LOD** & **LOQ** & \multirow{2}{*}{**Analytical method**} & **Reference** \\ \cline{2-4}  & \multicolumn{2}{c}{**(ng\(\cdot\)mL\({}^{-1}\) by default)**} & & \\ \hline
**CVDs** & & & & \\ \hline F2-Isoprostanes (oxidative & - & - & SPE/LC-MS & [70, 71, 72, 73] \\ \cline{2-4} damage biomarker) & - & - & SPE/GC-MS & [74, 75] \\ \hline \(\alpha\)-KG, L-CAR and acetyl-L-CAR & & & & \\ (cardiac cell metabolism) & - & 0.04–0.08 & & \\ \hline
**CVDs drugs** & & & & \\ \hline Aliskiren, prasugrel and rivaroxaban & - & 0.5–5.0 pg\(\cdot\)mL\({}^{-1}\) & MEPS/LC-MS/MS & [77] \\ \hline Pravastatin and pravastatin lactone & 1.5 nM & 5 nM & SPME/LC-MS & [78] \\ \hline Stimulants and \(\beta\)-blockers & 0.1–1.2 & - & SPME/LC-MS & [79] \\ \hline Acebutolol and metoprolol & - & 1.0 & MEPS/LC-MS/MS & [80] \\ \hline Propranolol & 4–7 & 13–20 & SPME/CEC & [81] \\ \hline Verapamil, propranolol and metoprolol & - & - & MEPS/\(\mu\)PESI-MS/MS & [82] \\ \hline Verapamil, gallopamil, norverapamil & 52–63, 5–8 & - & SPME/LC-UV (LC-MS) & [83] \\ \hline Lidocaine & 1.0 & 5.0 & MEPS/LC-MS/MS & [36, 84] \\ \hline
**ODs** & & & & \\ \hline Hexanal and heptanal (lung cancer) & 0.10–0.11 & 0.21–0.23 & & [85] \\ \hline Breast cancer VOCs & - & - & & [41] \\ \hline Prostate cancer VOCs & 0.10 & 0.16 & & [86] \\ \hline Sarcosine and N-ethylglycine & - & 0.03–0.06 & & [87] \\ \hline Neuroendocrine tumor markers & & & & \\ (HVA, VMA, 5-HIAA) & 0.046–24.3 & 0.063–49.6 & SPME/GC-QqQ-MS & [88] \\ \hline Unrelated cancer forms & - & - & SPME/GC-MS & [89] \\ \hline
5-HMUra and 8-oxodG & & & & \\ (oxidatively damaged DNA) & & 0.05–4.0 & 0.23–130.0 & MEPS/LC-PDA & [90] \\ \hline
8-hydroxy-2’-deoxyguanosine & 2.04 nM & 7.12 nM & SPME/LC-UV & [91] \\ (oxidatively damaged DNA) & 2.61 nM & 8.63 nM & SPME/CE-ECD & [92] \\ \hline
**ODs** & & & & \\ \hline
17\(\beta\)-estradiol and 2-methoxyestradiol & & & & \\ (potential angiogenesis modulators) & - & - & SPME/GC–MS & [93] \\ \hline Roscovitine (potential anticancer drug) & 0.5 & 1.0 & MEPS/LC-MS/MS & [94] \\ \hline Olomoucine (potential anticancer drug) & 0.5 & 1.0 & MEPS/LC-MS/MS & [95] \\ \hline Acrolein (lipid peroxidation by-product & & & & \\ and metabolite of cyclophosphamide and & - & - & SPME/GC–MS & [96] \\ ifosfamide (anticancer drugs)) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Representative reports using MEPS and SPME methodologies in the characterization of target urinary metabolites.
## 5 Case Study: COTS-based Embedded Real-Time Control of a Time Mash Delivery Machine
### The System under Control  
The gearbox system driven by the motor is the actual mechanism that rotates the single-shaft agitation system. The shaft's rotation generates a relative velocity that regulates the lime flow. The main control parameter that regulates the quantity of the lime slurry flow is the reference angular speed (\(\omega\)) of the single gear shaft agitation system (paddles shaft), which forces the lime volume to flow out of the silo. The outlet volumetric flow rate \(Q_{v}\) of the lime slurry is proportional to the angular (rotational) speed \(\dot{p}_{\omega}\) of the gear shaft according to the following simplified equation:  
\[Q_{v}=\dot{K}\dot{p}_{\omega} \tag{1}\]  
where K is the lime constant, which depends on the viscosity and velocity of the lime.  
A schematic diagram of the lime tank controller is shown in Figure 6.  
The flow meter monitors the flow of lime slurry and this data is used by the controller to automatically adjust the speed of the single gear shaft agitation system to maintain the accurate flow rate. The custom-design of the machine's controller was based upon 80C188 EB Intel microprocessor (25 MHz, 20 bits address bus, 1M address space, 8 bits data bus, 16 I/O pins, 3 timers/counters, 5V). The single-board controller basically contains four 62,256 (\(32\)K \(\times\) 8) RAMS that compose a \(128\)K \(\times\) 8 bit memory, two 27C512 (\(64\)K \(\times\) 8) EPROMs that compose a \(128\)K \(\times\) 8-bit memory, two RS232 (PC) communication ports and a programmable peripheral interface circuit that directly connects to the actuators' control equipment (electro-valves and motor relays). The control software was written in assembly language (\(80\)\(\times\)\(86\) family MASM Assembler). A simplified block diagram of the 80C188 EB microprocessor-based custom-built controller is shown in Figure 7.  
Figure 5: Lime mash/slurry storage and delivery machine under control.  
Figure 6: Schematic diagram of the lime tank controller.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 5 Discussion
### Challenges in Single-Delay Resampling  
Most resampling algorithms use a symmetrical time-limited window from the input signal to calculate the reconstruction. As a result, the output is only available after a certain group delay, which may be acceptable in the target application. With this approach, a high SNR can be achieved with a low OSR (Table 2). A jitter within the limits of theorem (3) and noise with RMSA = \(-\)60 dB had minimal impact in the group-delay scenario. When every 11 th sample was missing, the necessary OSR typically increased by 10%, which can be attributed to the higher average sampling interval.  
Only a few methods can reconstruct the signal without waiting for future samples or recalculate filter coefficients, if the filter is truncated. Methods based on complex optimization were also excluded for the processing of live sensor data. [PERSON], Margolis, and Lagrange calculate the weighting factors based on the available set of input samples and can thus adapt to an unsymmetrical, truncated set.  
The restriction to the single-delay scenario required an increased OSR by a factor between 1.6 and 2.2 (Table 2). Furthermore, all methods, except Akima, became noise sensitive. Only moderate noise amplification was observed up to a window half width of HW = 18 in the group delay scenario, whereas the noise for [PERSON] and Lagrange sharply increased even for small HW values in the single-delay scenario (Figure 8).  
The high noise sensitivity in the single-delay scenario can also be explained by Figure 10. Input signals above \(f_{L}\)=0.3 Hz result in an RMSE > 0, i.e., the distortion is larger than the input signal. High spectral components of noise signals lead to amplified distortions in a similar way.  
The combination of missing samples and noise was the most difficult case. Only Kriging and Akima achieved at SNR = 40 dB, with the latter one requiring a high OSR = 6.58. At least one future sample must be available to avoid exceedingly high prediction errors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Case**} & \multirow{2}{*}{**SNR**} & \multirow{2}{*}{**Noise**} & \multicolumn{2}{c}{**Necessary OSR/by Method**} \\ \cline{3-5}  & & & **Group-Delay** & **Single-Delay** \\ \hline Jitter & 40 dB & - & 1.15 & 1.80 \\ Jitter & 40 dB & \(-\)60 dB & 1.15 & 1.95/Margolis \\ Jitter & 60 dB & - & 1.34 & 2.40 \\ Missing samples & 40 dB & - & 1.29 & 2.14/Margolis \\ Missing samples & 40 dB & \(-\)60 dB & 1.29 & 2.89 \\ Missing samples & 60 dB & - & 1.42 & 3.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Necessary OSR for different test cases. All OSR values achieved by Kriging if not stated otherwise.
## 3 The Hybrid-ISL Metrology Setup  
To compute the position of spacecraft \(T\) with respect to spacecraft \(C\), the ISL system should provide radio frequency (RF)-based measurements: the range between the spacecraft, \(R\), and the LoS angles on the L-shaped phase array, \(\alpha\) and \(\beta\). Refs. [22; 23] give a suitable solution, where the ISL performs RF-based measurements by using 3 radio links  
Figure 1: (**a**) The traditional model for 3D localization L-shaped array; (**b**) the 3D localization model proposed for standardization.  
between the two spacecrafts. There are three antennas mounted on spacecraft \(C\) and one antenna mounted on spacecraft \(T\), as depicted in Figure 2a. The main antenna, \(A_{0}\), on both the \(C\) and \(T\) spacecrafts is used for range measurements, based on the round-trip time. The antennas mounted on spacecraft \(C\) constitute a reference plane. The location vector noted \(L\), is projected onto the reference plane, furthermore the projection has two components along \(x\)- and \(y\)-axis \(L_{x}\) and \(L_{y}\) (see Figures 2b and 2c, respectively). Note that the length of the segments are the coordinates of spacecraft \(T\) along the \(x\)- and \(y\)-axis. To compute the length of these two segments, two additional antennas, \(A_{x}\) and \(A_{y}\), provide two phase-difference measurements, \(\Delta\varphi_{x}\) and \(\Delta\varphi_{y}\). The ISL's module first computes the lengths \(L_{x}\) and \(L_{y}\) and then the LoS angles, \(\alpha\) and \(\beta\).  
In Appendix B, the calculus for the projected lengths with respect to available measurements (range and phase difference) is given, yielding \(L_{x}\) to be the following:  
\[L_{x}=\frac{R\lambda\Delta\varphi_{x}}{d}-\frac{\lambda^{2}\Delta\varphi_{x}^{ 2}}{2d}+\frac{d}{2}, \tag{3}\]  
where \(\lambda\) is the wavelength of the RF signal emitted by satellite \(C\), \(d\) is the baseline distance (distance between the main antenna \(A_{0}\) and the \(x\)-axis antenna \(A_{x}\)), \(R\) is the distance between the main \(A_{0}\) antennas, and \(\Delta\varphi_{x}\) is the phase difference measurement between \(A_{0}\) and \(A_{x}\) on the carrier. From (3) the LoS angle along \(x\)-axis results, the following is derived:  
\[L_{x}=\frac{R\lambda\Delta\varphi_{x}}{d}-\frac{\lambda^{2}\Delta\varphi_{x}^{ 2}}{2d}+\frac{d}{2}, \tag{4}\]  
Similarly, the y-axis projection of the location vector \(L\) is as follows:  
\[\alpha=\arccos\left(\frac{L_{x}}{R}\right), \tag{5}\]  
and, respectively, the counterpart LoS angle is as follows:  
\[\beta=\arccos\left(\frac{L_{y}}{R}\right). \tag{6}\]  
Figure 2: LoS measurement; (a) the projection of the LoS vector on the reference place; (b) the computation of the X-axis projection of the LoS; (c) the computation of the Y-axis projection of the LoS.
## 5 A Plate with Elastic Boundaries and its Dynamic Responses  
In this section, the conclusion of the simplified 3-stage model is verified via the multi-DOF finite element model. Figure 11 shows the finite element model of the plate-isolator with the elastic boundary. The modeling methods of the flywheel, 6-DOF vibration isolator, and structural plate are the same as mentioned in Section 2. In the finite element model, the \(X\backslash Y\backslash Z\) translation freedoms of the four edges of the structural plate are released. The four edges of the structural plate are connected to the ground with 60 vertical spring-dashpot elements and 64 transverse spring-dashpot elements, respectively. Moreover, in practice, the rotational freedoms of the edges are released due to the challenges of applying rotational constraints on the edges of elastically supported structural plates.  
In the finite element model, each vertical boundary spring stiffness is set to \(K=\frac{1}{60}340,000\) N/m, and each vertical damping coefficient is set to \(C=\frac{1}{60}2000\) Ns/m. In the transverse direction, the spring stiffness and the damping coefficient are \(K=\frac{1}{32}600,000\) N/m and \(C=\frac{1}{32}2000\) Ns/m at each supporting point. Harmonic excitations are applied on the mass center of the flywheel. The predicted force transmissibility of the plate-isolator system with elastic boundaries is shown in Figure 12. In Figure 12a, FEA results of the plate-isolator system with elastic boundaries are compared with the 3-stage simplified model. The results of the lumped parameter theoretical model are qualitatively similar to the FEA results. In the vertical direction (y), the results of the multi-DOF finite element model and the lumped parameter theoretical model are a little different after 50 Hz; the main reason is that the lumped parameter theoretical model is not precise enough to illustrate the complicated mode shape of the structural plate. Nevertheless, in this study, the 3-stage simplified lumped parameter model is applied as a guide to choosing the appropriate stiffness and damping for the plate boundary. In Figure 12b, the force transmissibilities of the plate-isolator system with and without elastic boundary are compared. The predictions show that the vibration isolation effect of the plate-isolator system is obviously improved by the elastic plate boundary. In Figure 12b, it is demonstrated that the 3-stage simplified model is an effective tool to determine the boundary stiffness and damping for the plate-isolator system.  
Figure 11: The finite element model of the plate-isolator system with elastic boundary.
## 4 Results and Discussion
### Evaluation Measures  
The proposed Bi-GRU model's effectiveness was validated by using six evaluation measures, namely NRMSE, ND, RMSSE, MAE, R2, and MSE. The evaluation measure NRMSE is one of the crucial measures in analyzing the proposed Bi-GRU model for sales forecasting; particularly, it is preferred while performing outliers' removal. The spread is considered as the difference between the minimum, \(y_{min}\), and the maximum, \(y_{max}\), values in the training dataset in NRMSE. The error measure, ND, accounts for the scale difference between the actual and predicted values. The evaluation measures NRMSE and ND are mathematically formulated in Equations (16) and (17).  
\[NRMSE\ (y,\ \hat{y})=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\hat{y}_{i}-y_{i} \right)^{2}}}{y_{max}-y_{min}} \tag{16}\]  
\[ND\ (y,\ \hat{y})=\frac{\sum_{i=1}^{N}\left|\hat{y}_{i}-y_{i}\right|}{\sum_{i=1}^ {N}\left|y_{i}\right|} \tag{17}\]  
where \(N\) represents the number of data points, and \(y\) and \(\hat{y}\) are denoted as the actual and predicted values. The error measures, i.e., MSE, MAE, and RMSSE, determine the average square difference, mean of absolution difference, and average square scale error difference between the actual and the predicted values. The error measures, MSE, MAE, and RMSSE, are mathematically specified in Equations (18)-(20), where \(q\) is denoted as period. The \(R^{2}\) is the number that ranging between zero and one; it measures how well the proposed Bi-GRU model predicts the outcomes and is numerically specified in Equation (21).  
\[MSE=\frac{1}{N}\sqrt{\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{18}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-\hat{y}_{i}\right| \tag{19}\]  
\[RMSE=\sqrt{\frac{1}{N}\frac{\sum_{i=q+1}^{q+N}\left(y_{i}-\hat{y}_{i}\right)^ {2}}{\frac{1}{q-1}\sum_{i=2}^{q}\left(y_{i}-y_{i+1}\right)^{2}}} \tag{20}\]  
\[R^{2}=1-\frac{\sum\left(\hat{y}_{i}-y_{i}\right)^{2}}{\sum\left(\overline{y}_ {i}-y_{i}\right)^{2}} \tag{21}\]
## 5 Results and Discussion
### Real Attributes  
For the undirected graph (Health Study), we show results for two vertex attributes: Age and Floor (the building and floor number where the person works; people who work on the same floor were highly likely to exercise together). For the directed graph (Enron), there is one vertex attribute, the person's job role.  
Vertex Strength (Figure 5): The graphs generated from Agwan have vertex strength distributions which map very closely to the input graphs. The graphs generated from MAG-R1 are better than random (ER), but the vertex strength distribution is compressed into the middle part of the range, with too few high- and low-strength vertices. This indicates that vertex strength depends on both the label distribution and the edge weight distribution; Agwan models both of these effectively.  
Spectral Properties (Figure 6): The spectral properties of the Agwan graphs map very closely to the input graphs. The singular values follow the same curve as the input graphs. This is significant as it indicates that graphs generated with Agwan have similar connectivity to the input graph: the primary singular value has been shown to be the most significant measure of graph connectivity [2]. In contrast,  
Figure 12: Triad Participation—Synthetic Attributes. (**a**) Undirected; (**b**) Directed (Cycles); (**c**) Directed (Middlemen); (**d**) Directed (Ins); (**e**) Directed (Outs).  
MAG-R1 does not preserve the singular value curve, showing a linear relationship instead. MAG-R1 cannot model the singular vector components at all without taking the edge weights into account. These results show that the spectral properties of the graph are dependent on both the vertex label distribution and the edge weight distribution.  
Clustering Coefficients (Figure 7): The accuracy of Agwan and MAG-R1 is similar. For the undirected graph, performance is little better than random. For the directed graph, Agwan gives reasonable performance for the low-degree vertices but drops away for the high-degree vertices. In all cases, the clustering is independent of the edge weight distribution. The accuracy of the results depends on which vertex attribute was chosen, implying that some attributes can predict community formation better than others. We hypothesise that cluster formation may in fact be (conditionally) independent of the vertex label values and may be better explained by triadic closure [5]: links are likely to form between two people who share a mutual friend, independently from their vertex attributes. The apparent dependency on vertex labels may be an artefact of the connection to the mutual friend, rather than the true explanation of why clustering assies. This aspect of network formation requires further investigation.  
Triad Participation (Figure 8): Similar to clustering, triad participation appears to be dependent to some extent on vertex label values but independent of the edge weight distribution. We hypothesise that like clustering, the apparent dependency between vertex label and triad participation values may be due to triadic closure, which is not currently modelled by either MAG or Agwan.