esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Performance Evaluation  
The performance evaluation results, which are summarized in Table 1, show that the required sensitivity was reached, with LOQs of approximately 9 pg/mL. This method, along with the one proposed by [PERSON] et al. [17], presents the lowest LOQs and solvent volumes that can be encountered in the literature for the analytes, without the need for further concentration steps on the extract. The calculated precision was comprehended between 18.7% and 21.6%, while the accuracy was between 102% and 111%. An example of the obtained experimental chromatograms is reported in Figure 3. The obtained values for matrix effect and recovery are 93% and 77% for iphosphamide and 88% and 79% for cyclophosphamideamide, respectively.  
As can be seen in Figure 4, the tests on uSPEed showed that, in the developed conditions, a single cartridge can be reused up to five times, with a relative standard deviation lower than 5% for both analytes, before observing a reduction in their performances.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Compound** & **Slope** & **Intercept** & **R2** & **LOD** & **LOQ** & **PR (\%)** & **ACC (\%)** & **RE (\%)** & **ME (\%)** \\ \hline Iphosphamide & 0.019 & 0.055 & 0.996 & 2.87 & 8.6 & 18.7 & 102 & 77 & 93 \\ Cyclophosphamide & 0.020 & 0.094 & 0.995 & 3.12 & 9.4 & 21.6 & 111 & 79 & 88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance evaluation results, expressed as the limit of detection (LOD), the limit of quantification (LOQ), precision (PR) and accuracy (ACC) of the method, extraction recovery (RE), and matrix effect (ME).
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 2 Portable Sensor System for Registration, Processing and Analysis of PPG Signals
### Preprocessing and Mathematical Methods for PRV Analysis
#### 2.3.3 Module for Mathematical Analysis of PRV
Poincare plot [52; 53] is a geometric method for representing the time series, such as PP intervals in a Cartesian plane [54]. This method allows for physicians to view the PP time intervals of the entire PPG signal at a glance and quickly detect cardiovascular disorders, if any [55]. Physiological signals, such as PPG and ECG, have been proven to have fractal behavior and have the following properties: self-similarity and fractal dimension [56]. The degree of self-similarity can be determined with the Hurst exponent. The R/S method is one of the most popular fractal analysis methods. The fractal analysis is intended to assess the degree of harmonization of the biorhythms of different organs and systems of the body, including the cardiovascular system, in order to identify the functional and pathological changes in the human body, as well as to predict changes in the health status of the patient. The value of the Hurst exponent is in the range of 0.5 to 1.0 if the signal is fractal [57]. The DFA method is suitable for the study of dynamic changes in PPG signals, which takes into account, not only the stationary, but also the non-stationary components of the temporal structure of the heart rhythm [53; 58]. DFA quantifies the fractal, correlation properties of PP intervals. The correlations are short-term and long-term, being characterized by the parameters \(\alpha_{1}\) and \(\alpha_{2}\), respectively [59]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline TP & Total Power (0–0.4 Hz) \\ VLF & Very Low Frequency Power (0.003–0.04 Hz) \\ LF & Low Frequency Power (0.04–0.15 Hz) \\ HF & High Frequency Power (0.15–0.4 Hz) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Commonly used Frequency-Domain Parameters. (Five minutes of the studied series). Fractal signals are two types: monofractal and multifractal [60]. The monofractal signal is homogeneous, because it has the same scale properties, both locally and globally. This signal can only be characterized by a one Hurst exponent value [60]. The multifractal signal consists of homogeneous, fractal subsets, whose singular properties can be characterized by a spectrum of Hurst exponent values [60]. The Generalized Hurst exponent is an MFDFA tool that reflects the structural inhomogeneity of the PPG signal. The Approximate Entropy [61] and Sample Entropy [62; 63] are quantitative measures of chaos in a dynamic system, such as the PP time series. Larger values of entropy indicate the irregularity of the signal, and smaller values indicate that the signal is largely regular [1].
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 2 Experimental Setup and Monitoring Systems  
The laboratory test was conducted on a reinforced concrete beam under gradually increasing loading in the four-point bending configuration, as shown in Figure 1. The reinforced concrete beam is 4 m long and was designed to have a ductile behavior to be able to follow a progressive and gradual propagation of cracks. The cross section of the beam is 150 mm wide and 300 mm high. The longitudinal reinforcements are composed of two bars of 14 mm in diameter at the bottom and two bars of 18 mm in diameter at the top of the section, with a concrete cover of 30 mm. Stirrups of 8 mm in diameter as shear reinforcement have been installed with 70 mm spacing at the bearings, while in the remaining beam shear reinforcements are spaced every 140 mm. According to the Italian ministerial decree, Updating of the Technical standards for construction (Gazzetta Ufficiale, D.M. 17 Gennaio 2018), C20/25 and B450C have been selected for the concrete and steel materials. The maximum aggregate size has been fixed to 10 mm with a water-cement weight ratio of about 0.5 and 300 kg/m\({}^{3}\) of cement.  
The load frame and the front side of the beam specimen are shown in Figure 2 (top), with a servo hydraulic actuator fixed at the center of the frame. A stepwise increasing load was applied downwards in displacement control by means of steel roller bearings in a four-point bending configuration. The load was measured with a 1000 kN load cell at the bottom of the actuator, while the mid span vertical displacement was measured through a linear variable differential transformer (LVDT) device, compensated with two LVDTs measurement points at the beam supports. Figure 2 (bottom) shows the beam specimen at the back side with the speckle pattern applied to a central surface of about 0.8 m length.  
Figure 1: Specim geometry and acoustic emission (AE) sensor locations. The eight AE sensors are numbered from 0 to 7. Dimensions are in cm.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 5 Radio Modulation Recognition Model Based on CNN
### Influence of CNN Network Hyperparameters on Modulation Recognition Rate
#### 5.2.2 Influence of the Number of Convolution Kernels on Recognition Results
Precision refers to the proportion of the number of samples correctly predicted as a class to the total number of samples predicted as a class. This is for the forecast results. For the example we gave earlier. The precision is the proportion of the number of correctly predicted QPSK _TP + FP_ as shown in (2) below. The precision of WBFM is the same. \[Precision=\frac{TP}{TP+FP} \tag{2}\]  
Recall refers to the proportion of the number of samples correctly predicted as a certain category to the actual total number of such samples. This is for the original actual sample. For the example we gave earlier, the recall is the proportion of the quantity _TP_ correctly \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{3}{c}{**Predicted**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-4}  & & **1** & **0** & \\ \hline \multirow{2}{*}{Actual} & 1 & TP & FN & TP + FN: Actual Positive \\ \cline{2-4}  & 0 & FP & TN & FP + TN: Actual Negative \\ \hline \multirow{2}{*}{Total} & \multirow{2}{*}{TP + FP: Predicted Positive} & FN + TN: Predicted Negative & TP + TN + FP + FN: The total number of samples \\ \cline{2-4}  & & & & \\ \end{tabular}
\end{table} 
Table 13: Confusion matrix. predicted as QPSK to the total quantity _TP + FN_ of the original QPSK as shown in (3) below. The recall of WBFM is the same. \[Recall=\frac{TP}{TP+FN} \tag{3}\]  
_F1-score_ is based on the harmonic average of recall and precision, which is a comprehensive evaluation of recall and precision, as expressed below  
\[F1\text{-}score=\frac{2\times recall\times precision}{recall+precision} \tag{4}\]
# Dispersion and Pressure Sensitivity of Carbon Nanofiber-Reinforced Polyurethane Cement
## 1 Introduction
The hardening specimen section morphology method [20] involves observation of the morphology of a specimen's cross section using a scanning electron microscope and the exploration of carbon fiber dispersion; however, this method cannot reflect the dispersion of carbon fiber macroscopically owing to the limitation of sampling location and quantity and the high cost of testing. The hardening specimen resistance method [19; 21] indirectly infers the dispersion of fibers in the carbon fiber cement matrix composites by calculating the coefficient of variation of resistance in each samples set. Although the dispersion status of carbon fibers in different hardened specimens can be qualitatively estimated using this method, it is impossible to predict the dispersion of carbon fiber in a single sample. Therefore, more samples are needed in each group, which makes the process more expensive. [PERSON] [22] reported that the uniform dispersion of fibers in steel fiber reinforced concrete specimens could be evaluated using the alternating current (AC) impedance method and the coefficient of variation of the ratio of conductivity to the matrix conductivity of composites could be calculated, which may characterize the dispersion of conductive fibers in concrete columns. Nevertheless, there must be a double arcing phenomenon in the AC impedance spectrum, which presents a significant limitation. [PERSON] et al. [23] suggested that the dispersion effect of fiber can be quantitatively evaluated using computed tomography (CT), albeit with high cost. There is currently an urgent need to establish a more essential and quantitative relationship between the microstructure and properties of materials, to further understand the conductivity of materials and guide the design of new materials. In this study, we attempted to improve the hardening specimen resistance method and combined it with the fresh mix paste method and the section morphology method. Samples having the same quantity were obtained from different parts of mixed CNFPUC and subsequently developed into hexahedron specimens; then the coefficient of variation of the hexahedron resistance was used to judge the dispersion of CNF. Further, we observed the distribution of CNF in the PU matrix by using the SEM method and discriminated the dispersion degree of CNF in PU in the entire CNFPUC mixture. By comparing the piezoresistive characteristics of the CNFPUC, we explored the relationship between the coefficient of variation of hexahedron resistance and the sensitivity of the CNFPUC in the elastic loading process under different ratios of polymer to silicon-ash and silica fume content. In case of differences in the CNF dosage, the critical value of the sampling coefficient variation could be used to judge the sensitivity of the CNF by fitting the conic curve, which provides a simple and convenient method for evaluating the sensitivity of the CNFPUC mixtures.
## 4 Results  
This section presents the performance of each DL architecture on individual datasets. The results are visualized in three subsections using Box plots, Bland-Altman plots, and Correlation plots. All results are based on the GDC metric. For NMI and NRMSE metrics, please refer to Appendices A and B, respectively. It is important to note that the y-axis scale varies across the figures because model performance differs between datasets.
## 3 Results and Discussion
### SEM Analysis  
Five SEM micrographs of NiSb\({}_{2}\)O\({}_{6}\) powders calcined at 900 \({}^{\circ}\)C are shown in Figure 6. These images show that the material's surface was made up of polyhedron-shaped particles and irregular particles of different sizes (Figure 6a-e). Figure 6a,b show a large agglomeration of these particles, which were distributed over all the analyzed areas. Additionally, it is clear to observe that the polyhedra were composed of smaller, agglomerated particles, which gave rise to the microstructures shown in Figure 6c-e.  
The morphologies shown in Figure 6 are attributed to the fact that the microstructural features are strongly related to the synthesis method [24]. Reference [36] reported that the preparation route, the calcination temperature, and the application of microwave radiation favor obtaining specific morphologies, such as octahedral structures, spheres, wires, and  
Figure 6: SEM images of NiSb\({}_{2}\)O\({}_{6}\) powders calcined at 900 \({}^{\circ}\)C at the following magnifications: (**a**) 7.06 kx, (**b**) 13.7 kx, (**c**) 33.0 kx, (**d**) 39.9 kx, (**e**) 43.3 kx.  
hexagonal nanostructures, among others. On the other hand, other studies informed that chemical methods are desirable in the formation of specific microstructures adequate for gas detection because the nucleation and growth processes that take place with them favor the control of the particles' nanometric size [37; 38]. In our case, the use of a wet-chemistry process assisted with microwave radiation in the presence of ethylenediamine favored the growth of morphologies such as those presented in Figure 6a-e. The results followed the nucleation and growth principles proposed in reference [18].  
From Figure 6, the particle size was estimated considering areas of the material's surface where the particles were clearly identifiable. A size in the range of 40-150 nm was estimated, with an average of ~92.30 nm and a standard deviation of ~\(\pm\) 24.64 nm (see Figure 7a). The size of the octahedral polyhedra was estimated in the range of 80-240 nm, with an average of ~154.78 nm and a standard deviation of ~\(\pm\)32.55 nm (Figure 7b).
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 7 Experimental Results and Analysis
### Time Analysis of Federated Learning Workflow  
In our experimental FL setup, the majority of the time is spent on local training performed by each client device. After completing local training for a number of epochs, clients send their model updates to the central server, which performs aggregation and returns the updated global model. Under stable Wi-Fi conditions, these communication steps are relatively fast, whereas local training is significantly more time-consuming.  
For instance, in the non-IID setup (Table 6), training Model 3 on a dataset of 1000 images takes approximately 84 s per epoch, or about 840 s for 10 epochs. Since FL rounds are synchronous, the server must wait for all the clients to complete their local training before aggregation can proceed, meaning the total round duration depends on the slowest client. In this case, two FL rounds were needed to reach the best model, resulting in a total training time of around 2 \(\times\) 850 s--roughly 30 min. Communication delays, such as sending updates to the server (uplink), server-side aggregation, and receiving the global model (downlink), were comparatively minimal. In this scenario, the uplink took around 30 milliseconds, aggregation about 20 milliseconds, and downlink another 30 milliseconds. These steps contribute negligibly to the overall round time. Across all the evaluated cases, local training remained the dominant factor in total FL runtime.
# User QoS-Based Optimized Handover Algorithm for Wireless Networks
## 4 Experimental Results  
The simulation experiments were performed using Mininet-WiFi [21; 22], which is an open-source platform for simulating wireless scenarios, allowing high-fidelity experiments that replicate real network environments. Mininet-WiFi is a wireless network emulator extended from Mininet. It has native WiFi support, but can also emulate other wireless network technologies in experiments using it. Users can virtualize stations and access points, as well as use existing Mininet nodes, such as hosts and switches. The hardware used in the simulation experiment was a notebook, which was a Windows 10 laptop with a 15.60-inch display that had a resolution of 1366 \(\times\) 768 pixels. It was powered by a Core i5-8250U processor and came with 4 GB of DDR4 RAM. The notebook had 1 TB of hard disk storage. The graphics were powered by NVIDIA GeForce MX150.  
The experimental scenarios and parameters will be described in Section 4.1. In Section 4.2, the performance and QoS comparisons between the proposed method and the other two hand-off methods will be shown, to demonstrate the excellent performance of the proposed method.
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 4 Results
### Atlantic vs. Pacific  
To further examine the overall differences between the TC-winds and HWRF, we separated the storms in this study by ocean basin (Atlantic or Pacific) and compared the differences. This separation resulted in 11 storms in the Atlantic and seven storms in the Pacific (one storm was located in the Southern Ocean and was not included in either of these plots) between 2017 and 2020. Similar to Figure 13, Figure 14 shows the wind biases and standard deviations between each of the sensors and HWRF separated by basin. It is evident that the satellites and HWRF were in better agreement in the Atlantic Ocean than the Pacific. In the Atlantic, the bias between the sensors and HWRF was near zero for winds of up to 35-40 m/s. In contrast, the bias in the Pacific quickly became negative as wind speed increased, staying consistently between \(-2\) and \(-5\) m/s from approximately 25 to 45 m/s before switching signs at higher wind speeds, while maintaining roughly the same magnitude as the lower wind speed bias. The standard deviation between satellites and HWRF winds at higher winds (above 30-35 m/s) also was significantly lower in the Atlantic. We note that although there were fewer storms in the Pacific than the Atlantic, and thus also fewer matchups with the satellites, the bin populations in each case were sufficiently large to make a meaningful comparison.  
Since the algorithms for the each of sensors did not change from basin to basin, it could be concluded that the differences seen between the basins were most likely due to differences in the HWRF model. As noted in [25], the HWRF analysis times assimilate observational data such as conventional observations, satellite observations, and Doppler radar radial velocities whenever available. Most of these assimilated data are gathered in situ, and are often much easier to collect in the Atlantic Basin due to many tropical cyclones' proximity to land. This is in contrast to the Pacific Basin, where most storms are too remote to be observed using any method other than satellites. Therefore, the initial HWRF 0-h analysis vortices in the Atlantic will often be constrained by more assimilated in situ data than those in the Pacific, and are likely to more closely resemble the storm's actual structure and intensity at a given time. Another reason for the differences in standard deviations between Atlantic and Pacific basins could simply be the fact that, on average, the Pacific TCs reach higher wind speeds than those in the Atlantic. At higher model or satellite wind speeds, the likelihood of a wind-speed mismatch increases (due to the model, satellite, or both), and thus the resulting standard deviations also increase.
## 4 Results
### Implementation of a Software Prototype  
For answering RQ2, a software prototype was implemented using Mathworks Matlab. Containing all calculation methods and the rule base described in Section 4.1, the software prototype was used for support evaluation parallel to development (Section 4.3), tracking and documentation of requirement changes within the three case studies as well as application and success evaluation (Section 5).  
The following main functions were implemented:  
* Upload of requirement lists either with or without assigned main features and influence factors (Microsoft Excel data format.lskx).
* Upload, editing and export of a rule base for requirement interrelations (Microsoft Excel or Mathworks Matlab proprietary format).
* Upload, editing and export of influence factors and their prioritization (Microsoft Excel or Mathworks Matlab proprietary format).
* Assignment of main features and influence factors to requirements.
* Adaptation of classification boundaries for priority score.
* Calculate requirement change risk for the requirement list.
* Illustration using the requirement change risk portfolio.
* Detailed requirement view to identify actively influencing requirements and passively influenced requirements related to a requirement \(\mathrm{r_{i}}\).
* Export of requirement lists enriched by change risk (Microsoft Excel data format.lskx).
* Distinction of users (for traceability of editing).
* Change history with comment section (requirement changes).
* Editing history with comment section (adaptions of the rule base of prioritization).
* Determination and selection of an individual dataset (rule base and influence factors: Type of project, branch and customer).  
The software prototype is illustrated in Figure 10 below.  
Figure 9: Risk of requirement change portfolio.
## 1 Introduction
The maximum projection time information method is used to achieve target detection, and then the goniometric data of a single optical platform are converted into the spatial coordinates of the target by a Gaussian minimum mean square error difference correction algorithm through a space-based multi-optical platform to achieve target localization. Subsequently, the multi-frame image spatial target position information is fused to complete the solution of orbital equations, followed by space debris orbit determination for accurate safety prediction of spacecraft in-orbit operation. By applying the performance advantages of star trackers to space target detection, the advantages of star trackers in terms of high accuracy and high update rate are thus effectively exploited in the field of space detection. Different from the tracking and observation mode of specialized monitoring satellites, the space debris monitoring network based on multiple star trackers proposed in this paper relies on the natural intersection between the field of view of star trackers and the target to capture the target. Although each continuous observation is short, the number of star trackers is large, which can realize the intermittent short arc observation of space targets under different observation geometry. This kind of network does not require enhancing the detection ability of the system but increases the detection opportunities of the system so that more targets can be detected by the system at the same time. In fact, it improves the visible turn state between the target and the platform and provides ideas and engineering practice for the expansion and application of star trackers. This paper is structured as follows: Section 2 presents the comprehensive analysis of space debris. Section 3 presents the star tracker model. Section 4 focuses on the target identification method between star trackers. Section 5 presents the localization and initial orbiting of space targets, presenting both typical and advanced operations required for the specific task of this work. Section 6 reports the results obtained from the feasibility and characterization experiments, the results of the simulation validation, and a discussion of the related computational effort. Finally, concluding remarks are given in Section 7.