esa-sceva/satcom-chunk-collection huggingface dataset:
# Salivary Test Assessment in an Oncohematological Pediatric Sample: A Case Control Study
## 1 Introduction  
Malignant tumors affecting the pediatric population account for 1-2% of all neoplasms and are one of the most frequent causes of death in this age group. In recent years, the incidence of neoplastic diseases in children has increased significantly. This is due to increased exposure to carcinogenic environmental novae and the diagnostic possibilities now available [1; 2; 3; 4]. In contrast to adult tumors, which are mainly carcinomas, tumors of embryonic and connective tissue origin predominate in children, namely leukemias, central nervous system tumors, lymphomas, neuroblastoma, bone and soft-tissue sarcomas. Acute lymphoblastic leukemia, among childhood leukemias, represents most cases, with the remaining 25% being various types of acute myeloblastic leukemia. The treatment of these diseases involves the use of cytotoxic drugs, such as alkylating agents, cytotoxic antibiotics, antimetabolites and agents that can be used in combination and inhibit microtubule formation [5; 6]. Immunodeppression, caused by antineoplastic treatments and local tissue changes resulting from possible radiotherapy, trigger undesirable effects in the oral cavity such as: mucositis, opportunistic infections, oral bleeding, xerostomia, dysgeusia, decay, dental malformations and pain. The oral cavity is damaged because its tissuesare affected by the cytotoxic impact of these drugs: antineoplastic agents, which are not selective, act on both tumor cells and healthy cells, thus inhibiting their replication [7].  
Mucosal cells in the oral cavity are particularly susceptible to the action of these agents, resulting in atrophy and loss of mucosal barrier integrity [8]. Mucositis is one of the most disabling and frequent complications. The earliest clinical sign is the appearance of an erythematous area, often in the non-keratinized mucosa. The clinical course is variable and in the most severe cases leads to the appearance of ulcerated and hemorrhagic lesions, responsible for intense pain, incompatible with the intake of solid food and liquids, making it necessary to resort to parenteral nutrition and pain-relieving therapies [9]. Another recurring complication occurring in the oral cavity is xerostomia associated with dysgeusia. Poor saliva production leads to a decreased buffering effect against the acidic pH, as well as to poor cleansing of the oral cavity and to a weakening of the mucous membranes. These factors, combined with changes in the oral microbial flora, are responsible for an increased risk of cavities [10]. The early detection of these symptoms and the prevention carried out to limit their severity are important aspects of the treatment process, enabling an improvement in quality of life during cancer therapy.  
We aim to assess, through salivary tests, the quality of saliva in pediatric patients affected by oncochematological diseases and treated with chemo/radiotherapy. A control group of healthy children was selected for comparison.
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 3 Survey of Signal Processing Units based on FBGs
### Optical Integrators  
FBG technology has served as a platform to implement another important all-optical signal processor, namely the optical integrator [64]. An optical integrator provides the cumulative time integral of the complex temporal envelope of an input optical signal. Its transfer function is  
\[y(t)\propto\int\limits_{\tau_{N}=-\infty}^{t},\cdots,\int\limits_{\tau_{1}}^{ \tau_{2}}x(\tau_{1})d\tau_{1}d\tau_{2}\cdots d\tau_{N}\to H_{\text{int}}(f) \propto\frac{1}{\left(j\cdot(f-f_{0})\right)^{N}} \tag{43}\]  
Among the applications of FBG-based optical integrators proposed in the literature, we can highlight the implementation of unit-step time-domain waveforms and the generation of flat-top pulse shapers, enabling re-configurability of their temporal width. Additionally, they are also key components for solving ODEs, with much better performance in terms of high-frequency noise than optical differentiators [5]. Generally speaking, an optical integrator can be interpreted as the light-wave equivalent of an electronic capacitor. The main drawback of any passive temporal integrator is that it necessarily operates over a limited time window, due to practical design and fabrication constraints [65].  
Figure 9: (**a**) Apodization profiles of the gratings required to implement Nth-order (up to N = 4) optical differentiators based on FBGs in transmission considering realistic spatial resolution (0.5 mm); (**b**) Intensity outputs from the obtained FBG devices.  
Significant efforts have been devoted to the implementation of broadband, high-resolution arbitrary order (Nth) optical integrators. Solutions based on passive and active configurations have been theoretically proposed based on FBGs in reflection and transmission, while some of them have been also experimentally demonstrated [64; 65; 66; 67; 68; 69; 70; 71]. The first approach was based on FBG analysis algorithms using a phase-shifted FBG in transmission as an optical temporal integrator [64] with operation bandwidths in the order of tens of GHz. Subsequently, from temporal specifications and based on the first-order Born approximation, an uniform FBG operating in reflection was proposed as a first-order temporal integrator over a limited time window [65]. This implementation was later generalized to high-order temporal integrators using a weak-coupling uniform FBG with a proper apodization function [67; 68; 69]. Finally, these FBG-based optical integrator designs operating in reflection were optimized by means of strong-coupling uniform FBGs (using FBG synthesis algorithms), increasing their energy efficiency and processing accuracy. Experimental results were presented in [70; 71].
# SmartX Multi-View Visibility Framework
## 4 SmartX MVF with Flow-Centric Visibility: Verification
### Visibility Overhead Results  
The visibility agent for network packets collection should be lightweight and nonintrusive. The aim of this first experiment is to ensure the requirement of non-intrusiveness of visibility agent. We monitored the performance of SmartX box in two situations: without execution of the visibility agent and with the execution of visibility agent. We recorded the average CPU load and memory utilization before and after the execution of visibility agent as shown in Figure 11. On the \(x\)-axis we have time and the \(y\)-axis shows resource usage. Visibility agent execution is started after 14:40:00. The CPU load almost remains the same at 0.7 as shown in Figure 11a. Memory utilization is initially increased by 217 MB and later stayed around 177 MB as shown in Figure 11b. These graphs indicate that visibility agent is lightweight and incurs low impact on SmartX box performance, even when processing network packets from multiple network interfaces.  
To verify the persistent measurement and collection of data via the proposed solution, as shown in Figure 12a, we reported the total number of data instances collected per day from playground distributed sites to the DataLake in one month. The highest number of data instances, around 47 million, were recorded on the twenty-ninth and the lowest number of data instances, just above 20 million, were recorded on the eighteenth. We effectually collected, validated, and stored almost 25 million data instances per day. In addition, we calculated the number of data instances that are collected from each layer of the visibilities for an hour (underlay resource layer = 54, physical resource layer = 420, virtual resource layer = 840, and flow layer = 52 million). In order to measure the execution time of implemented visibility integration component, we simulated it by increasing the number of network packets. In this experiment, we started the Spark analytics engine in standalone mode with a different number of CPU cores and memory allocation as shown in Figure 12b. We begin the tests using 25,726 network packets with a total size of 3.4 MB, where execution took a total of 3.7 s over 4 CPU cores and 8 GB of memory to aggregate network packets and tag them. Next, we increased the number of network packets (number = 450,000 and dataset size on disk = 61 MB). Now, execution took 5.8 s. Also, a similar trend was observed accordingly to the increased number of network packets (number = 5 million and dataset size on disk = 630.30 MB), where 7.4 s were taken by the execution. When we further increase the number of packets (number = 10 million and dataset size on disk = 1.3 GB), 13.5 s was needed for completing the execution. Based on these results, we can see execution efficiency improved with the increase in number of assigned resources to the analytics engine.  
Figure 11: Visibility data collection overhead for packet-precise network flows. (**a**) CPU load and (**b**) memory utilization.
## 5 Results and Discussion
### Analysis of Experimental Results
The determination coefficients R\({}^{2}\), RMSE, Mae, and MAPE of the three prediction models are shown in Figure 8. It can be found that the R\({}^{2}\) value of the AdaBoost SVM model is 0.972, which is significantly higher than 0.917 of the CNN model, 0.725 of the LSTM model, and 0.811 of the RF model, with the highest accuracy. At the same time, the RMSE, Mae, and MAPE of the CNN model are 2.184, 2.866, and 10.580%, respectively. Compared with the CNN model, the AdaBoost SVM model has a smaller deviation between the predicted results and the actual values and has the best robustness. Compared with the LSTM model and the RF model, the CNN model has higher robustness. These comparison results further prove that the AdaBoost SVM model has higher accuracy and stability and is effective and advanced in the task of coal and rock mass strength prediction. At the same time, the reasons for the differences are analyzed. Although the LSTM model has advantages in processing time series data, it is not accurate enough in fitting the complex nonlinear relationship of signals while drilling; the CNN model has its characteristics in feature processing and model complexity control, but it fails to give full play to its advantages under the specific data and scenarios of this study. The RF model has a high tolerance for outliers and missing values, but it has high requirements for super parameter setting and may still have an overfitting phenomenon. The AdaBoost model in this paper combines the processing ability of an SVM-based learning device for complex data, which is more suitable for the strength prediction task of the signal while drilling in the coal mine. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Base Learner** & \(\mathbf{R^{2}}\) & _MAE_ & _RMSE_ & _MAPE_ \\ \hline Tree & 0.806 & 2.801 & 5.146 & 12.270\% \\ SVM & 0.972 & 1.717 & 1.940 & 8.145\% \\ Linear & 0.451 & 9.107 & 16.512 & 55.744\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculation results of performance evaluation indicators for three models. Figure 8: Comparison of multi-model evaluation indicators.
## 3 Results and Discussion
### Relationship Between Spatial and Temporal Distribution of Sites and Environmental Factors
#### 3.2.2 Proximity to Water Sources  
From the Stone Age to the Warring States period, limited agricultural techniques and flood control measures made proximity to water a critical factor in human settlement and burial site selection. Using hydrological data in ArcMap, buffer zones of 1 km and 3 km around water bodies were created and overlaid with site locations from different periods to calculate their distances to water sources. The results are summarized in Table 3. During the Stone Age, 51% of the sites were located within 1 km of a water source, while 32% were situated between 1 and 3 km away. The frequent flooding of rivers in the Chengdu Plain during this period may explain why some settlements were positioned more than 3 km from water sources. The Shang to Spring and Autumn periods exhibited the most pronounced preference for proximity to water, with 70% of the sites located within 1 km of a water source and only 6% situated beyond 3 km. During the Warring States period, 49% of the sites were within 1 km of water, while 38% were between 1 and 3 km away. Climate fluctuations during this period, including both droughts and floods, likely influenced site selection strategies. Overall, the proportion of sites located within the 1 km buffer zone remained consistently high across all periods, peaking at 70% during the Shang to Spring and Autumn periods and remaining around 50% during the Stone Age and Warring States periods. This pattern underscores the strong preference of ancient populations for settling near water sources to support daily life and agricultural activities, highlighting a distinct tendency toward water proximity.
# Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
## 2 The Backpack Metaphor  
Recent research has highlighted the important role that student effort plays in academic success [19]. High school readiness [20], economic disadvantage [20], classroom climate [21] and curriculum design [22] had previously been identified as factors that explain dropout. However, self-efficacy also plays a key role in academic success.  
Self-efficacy is defined as a person's belief in their ability to succeed in a specific situation [23] and has recently been linked to the effort that students are willing to make [19]. In a qualitative case study, [PERSON] and [PERSON] [24] found that loss of confidence due to poor performance contributed to engineering attrition. Those students who believe that intelligence is fixed and cannot be developed tend to be less tolerant of failure [19]. On the contrary, those who believe that it can be developed, when faced with an adverse situation, try harder, develop active learning strategies and, even when their curricular progress is not optimal, persist [19].  
Based on those previous works about the relevance of self-efficacy, this paper defines the backpack metaphor. A metaphor is \"a mechanism of analogy in which we conceive a concept that belongs to a certain conceptual domain in terms of another conceptual domain, and in which correspondences between the attributes of both domains are established\" [25]. Metaphors are commonly used to communicate ideas in technical disciplines, facilitating interdisciplinary work. The backpack metaphor is defined as follows: The list of failed courses that a student must retake can be represented as stones that the student puts in a backpack. Each time a student fails a course, a new stone is placed in their backpack, which remains there until the course is passed. Carrying many stones in the backpack could awaken in the student the need to empty it as soon as possible, making risky decisions from the curricular point of view [26]. On the other hand, never being able to empty the backpack (even if the failed courses changed), could affect self-efficacy, have serious consequences in the medium term, affect the students' goals or even result in program dropout.  
Traditionally, the analysis of curricular trajectories has been based on the progress of each student in the curriculum [8; 27]. Even though this strategy has the advantage that the model is obtained directly from the data and is easy to understand, it does not represent the psychological burden students perceive when they have failed courses that later on they must retake. Understanding how students manage their course backpack could help to better understand their decisions regarding course enrollment, persistence and dropout.
# Modeling and Control Simulation of Power Converters in Automotive Applications
## 4 Software-in-the-Loop Simulation Analysis
### Dual Active Bridge Simulation Analysis  
As the input voltage \(V_{1}\) supplied by the AC/DC converter varies within the operational range of interest \(V_{1}\in[200;500]\), Figure 23 shows the analysis of the output voltage transient of the DC/DC converter for a control voltage of \(V_{ref}=250\) V (battery voltage connected to the second stage of the DAB conversion). Figures 24 and 25 also show a completely similar analysis. The step response gravitates toward an (almost) linear first-order transient for lower voltage levels and displays second-order behavior for higher \(V_{1}\) voltage levels under all operating circumstances that have been investigated and demonstrated. The more DAB output voltages used, the more obvious this discovery becomes. A thorough examination of the robustness of the phase-shifting control method is provided in the previously stated Figure 26. This study takes into account possible fluctuations in the values of circuit components, with a particular emphasis on the variations in leakage inductance and output capacitor. The analysis is given for the intermediate working state, with the goal of reaching a steady battery voltage of 350 V, to guarantee clarity and comprehensiveness. It is clear that adjustments to the values of both circuit components affect the transient behavior more than the steady-state value, with the residual voltage ripple being partially impacted.  
It was found that, in particular, a greater value for the inductance tends to enhance the voltage step response's overshoot, whereas a value that is too low makes the dynamic response resemble a first-order linear system, which also affects the steady-state error. It is evident that the output capacitance influences the maximum overshoot and the settling time, but it appears to have little effect on the steady-state value (at least within the examined range of variation). This allows the desired voltage step response to be achieved with almost no steady-state error.  
Figure 23: Impact of the \(V_{in}\) fluctuation on the DAB converter’s output voltage control, when \(V_{ref}=250\) V.  
Figure 24: Impact of the \(V_{in}\) fluctuation on the DAB converter’s output voltage control, when \(V_{ref}=350\) V.
# Natural Frequencies and Modes of Electrostatically Actuated Curved Bell-Shaped Microplates
## 3 Results and Discussion
### Frequency Tuning  
#### 3.3.1 Mechanical Tuning  
The equilibrium curves of a mechanically loaded curved plate with \(h_{0}/d=3.75\) and a gap of \(5\,\upmu\)m obtained using the seven DOF ROM and the FEM are presented in Figure 7. The fundamental modes of vibrations around differing equilibrium states along the equilibrium path, under various magnitudes of the mechanical loading, are presented in the insets. Good agreement between the ROM and FEM predictions, for both the equilibrium path and the tuned fundamental modes, is observed. This agreement is further demonstrated in Figure 8, where the ROM and the FEM predictions for the mechanically tuned real parts of the eigenvalues associated with the first five axisymmetric modes of the plate are presented. As expected, the fundamental mode frequency becomes zero at the snap-through (ST) and release (R) limit points. Non-zero imaginary parts of the eigenvalues (not shown) obtained within the interval between the ST and the R points (vertical black dashed lines) indicate that plate equilibria are unstable in this interval of the deflections.  
Figure 6: Convergence of the ROM in terms of the fundamental mode non-dimensional frequency. Results of one (blue dash-dot line), three (red diamonds marker), five (solid green line), and ten (dashed yellow lines) DOF models are shown. The reference values (purple \(\times\) markers) are the non-dimensional frequencies obtained by the FE model. The inset shows the errors (with respect to the reference FE results) of the ROM with three (red diamonds), five (green circles), and ten (yellow plus signs) DOF.
## 3 Results and Discussion
### Confirmatory Analysis Results
#### 3.2.4 Measurement Uncertainty  
The evaluation of the uncertainty of analytical results is compulsory for laboratories accredited according to ISO/IEC 17025 [18], and several methods for the determination of this parameter have been proposed [28; 29; 30]. In this work, we used the \"bottom-up\" method on validation data obtained from each step of the analytical procedure [30].  
A relative expanded measurement uncertainty was calculated by using a coverage factor (k) of 2, corresponding to a 95% confidence level [30]. This parameter was calculated for three fortification levels: 100, 250, and 500 ug kg\({}^{-1}\). Values in the range of 6-16.5% were obtained, assuring a good level of accuracy of the final result.  
These results were also compared to the uncertainty function (U\({}_{I}\)), as reported in Regulation (EC) No. 401/2006 [16], useful for evaluating the \"fitness-for-purpose\" of an analytical method. This function, which gives the acceptable maximum uncertainty level of the method, was calculated by using the following equation:  
\[U_{f}=\sqrt{\left[\left(\frac{LOD}{2}\right)^{2}+\left(\alpha C\right)^{2} \right]}\]  
where \(C\) is the target concentration and \(\alpha\) is a constant depending on \(C\) value. The evaluated measurement uncertainties were considerably lower than corresponding \(U_{f}\) values, namely  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**VALID** & \multicolumn{2}{c}{**ELISA**} & \multicolumn{2}{c}{**UPLC/FLD**} \\
**PARAMETER** & **T-2** & **HT-2** & **T-2** & **HT-2** \\ \hline SPECIFICITY (\%)/SELECTIVITY & \(\sim\)85 \({}^{*}\) & 100 \({}^{*}\) & No interfering compounds & No interfering compounds \\ MEAN RECOVERY (\%) (\(n=12\)) & 102 & 102 & 106 & 100 \\ PRECISION (CV \%) & 6.5 & 6.5 & 13 & 11 \\ (\(n=12\)) & & & & \\ LIMIT OF & & & & \\ DETECTION & 3.0 & 3.0 & 9.0 & 5.7 \\ (instrumental, \(\mu\)g kg\({}^{-1}\)) & & & & \\ MEASUREMENT RANGE & 75–900 & 75–900 & 29–100 & 19–100 \\ (\(\mu\)g kg\({}^{-1}\) in matrix) & & about 1 h & & about 4 h \\ \hline \hline \end{tabular} \({}^{*}\) Sum of T-2 and HT-2. CV: coefficient of variation.  
\end{table}
Table 4: Comparison of method performances of the ELISA and UPLC for the determination of the T-2 and HT-2 toxins in cereals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Toxin** & **Analytical Session** & **Contamination Level (\(\mu\)g kg\({}^{-1}\))** & **RSD\({}_{\text{r}}\) (\%)** & **RSD\({}_{\text{R}}\) (\%)** \\ \hline  & 1 & 100 & 13 & 18 \\  & 2 & 1 & 13 & 18 \\  & 1 & & 11 & \\ T-2 & 2 & 250 & 8 & 13 \\  & 1 & 500 & 11 & \\  & 2 & 500 & 5 & 8 \\  & 1 & 100 & 5 & 7 \\  & 2 & 100 & 8 & 7 \\ HT-2 & 1 & 250 & 10 & 19 \\  & 2 & 7 & 9 \\ \hline \hline \end{tabular} \({}^{*}\)Sum of T-2 and HT-2. CV: coefficient of variation.  
\end{table}
Table 3: UPLC/FLD intra-day and inter-day precision studies on durum wheat samples spiked with the T-2 and HT-2 toxins.  
18%, for each concentration level (100, 250, and 500 ug kg\({}^{-1}\)). These results confirmed that the method is \"fit-for-purpose.\"