esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Soft Error Number  
Data (read error count) from the host software was extracted, and the data points were plotted as a curve of soft error number with laser energy, as shown in Figure 6. When the pulsed laser energy is 814 pJ (corresponding to the LET value [6; 7; 8] of 37 MeV\(\cdot\)cm\({}^{2}\)/mg), soft errors due to SEE start to be generated, so the SEU threshold for this SRAM device is 814 pJ (\(\pm\)81 pJ). With increasing laser energy, the number of errors increases. When the pulsed laser energy reaches 1651 pJ (corresponding to the LET value of 75 MeV\(\cdot\)cm\({}^{2}\)/mg), the number of errors is 11125 bits.  
The variation curve between the number of soft errors and the electrostatic discharge voltage is shown in Figure 7. With the threshold of 12.5 kV, discharge voltages below 12.5 kV do not cause soft errors in the device. In the range of 12.5 kV to 20 kV, the number of soft errors increases with the increase of discharge voltage. In particular, the slope of the error number growth curve increases significantly between 17 kV and 20 kV.  
According to Figure 7, when the discharge voltage is less than or equal to 12 kV, the number of errors in the detection system is not accumulated, but the character segment recording of the error address is recorded. The SEE experiment did not experience a  
Figure 6: Error number curve with pulsed laser energy.  
Figure 7: Error number curve with ESD voltage.  
similar phenomenon. To investigate the issue, the read-only mode of operation of this SRAM device only recorded errors when a read error was detected (3 reads, \(\geq\) 2 mistakes), while the SEU error count accumulated. The set access time for the read/write circuit was approximately 30 nanoseconds, and the pulse width of the discharge pulse was also 30 nanoseconds. Thus, if the discharge pulse caused a functional failure of the read/write circuit, it would only last for one read-only data period. Only if a real error occurred in the SRAM storage data, would it trigger SEU data accumulation according to the three-check judgment scheme [9,10]. Therefore, it could be concluded that the read/write circuit of this SRAM device is more sensitive to discharge pulses than the memory circuit.
## 3 Results
### Acoustic Streaming and Cupula Displacement  
This section examines the acoustic streaming caused by energy dissipation at each frequency and compares the accompanying displacement of the cupula. Figure 7 visualizes the acoustic streaming in the endolymph of the SCC induced by focused ultrasound, showing the differences across frequencies. The three subplots represent the flow patterns at 400 kHz, 600 kHz, and 800 kHz, with each graph displaying the scale factor indicating the strength of the flow.  
Figure 6: Acoustic energy dissipation density at **(a)** 400 kHz, **(b)** 600 kHz, and **(c)** 800 kHz. The area of concentrated dissipation decreases as the frequency increases, similar to the intensity.  
Figure 7: Visualization of acoustic streaming patterns in the endolymph of the SCC induced by focused ultrasound at three different frequencies: **(a)** 400 kHz, **(b)** 600 kHz, and **(c)** 800 kHz. The results are shown on the xz-plane (y = 0), which represents a cross-sectional slice of the SCC structure perpendicular to the y-axis. The scale factors for each frequency are set at 500,000, 50,000, and 10,000, respectively.  
At 400 kHz (Figure 7a), the flow vectors appear weak, with the magnitude and direction sparsely distributed. The scale factor is set to 500,000, reflecting the weak flow toward the cupula within the endolymph. As the frequency increases to 600 kHz (Figure 7b), the size and density of the flow vectors noticeably increase, with a scale factor of 50,000, indicating a stronger flow than at 400 kHz. At 800 kHz (Figure 7c), the flow vectors are the most pronounced, with a scale factor of 10,000, showing that the strength of acoustic streaming in the endolymph increases significantly with higher frequencies. In particular, at 800 kHz, the flow pattern is the clearest, indicating stronger acoustic streaming. These results are consistent with previous acoustic dissipation results.  
The analyzed acoustic streaming seems to circulate within the endolymph but actually pushes the cupula upwards (positive z direction). Based on this flow, cupula displacement can be calculated through FSI analysis. Figure 8 shows the 3D visualization of cupula displacement caused by streaming velocity in the endolymph. Each graph illustrates the displacement distribution at the FSI interface for different frequencies. Therefore, the z = 0 plane of the graph corresponds to this interface. Furthermore, 400 kHz (Figure 8a), the maximum displacement is 0.084 \(\upmu\)m, with generally low and flat displacement. At 600 kHz (Figure 8b), the maximum displacement increases to 0.68 \(\upmu\)m, with a more distinct shape and larger displacement overall. At 800 kHz (Figure 8c), the maximum displacement reaches 1.46 \(\upmu\)m, showing the most significant increase with higher frequencies. These results demonstrate that cupula displacement increases as frequency rises. This shows that fluid movement caused by acoustic streaming leads to larger deformation at higher frequencies, with the greatest cupula displacement observed at 800 kHz.
# Erythrocytes and Nanoparticles: New Therapeutic Systems
## 3 NPs and RBC
### RBC-Mimicking Particles  
Another strategy regarding NP-delivery systems involves the development of mimicking particles [71; 72]. In particular, for erythrocyte-related delivery systems, these are synthetic particles that try to emulate the beneficial properties of erythrocytes (e.g., their morphological properties, membrane composition and surface) and hence their physiological behavior (e.g., resistance to phagocytosis and systemic clearance) [40].  
Several materials have been studied in attempts to mimic the erythrocyte properties considered useful for drug delivery. Modifications from polystyrene or PLGA spheres led to RBC-shaped templates and cationic and anionic polymers deposited by means of the layer-by-layer technique [70]. Materials, such as silica, have been coated with lipids to emulate the erythrocyte outer leaflet, demonstrating an improvement of their hemocompatibility [73]. Other RBC-like particles based on cellulose derivatives have been synthetized by electro-spraying. The inclusion of magnetite NPs and fluorescent dye allowed the researchers to obtain a dual-imaging probe with a flexibility similar to RBCs [74].  
Some of the most investigated materials in this field are hydrogel microparticles of different form and flexibility [75; 76; 77]. Particles--including PLGA based particles --mimicking flexibility and the ability to bind and carry oxygen, drugs and contrast agents have been obtained [70]. Discoidal polymeric nano-constructs (DNPs)--composed by a homogeneous mixture of poly(D,L-lactide-co-glycolide)-acid carboxylic terminated (PLGA-COOH) and poly(ethylene glycol) diacrylate (PEG-DA), conjugated to tissue plasminogen activator (tPA)--have demonstrated their lytic activity with an adequate deformability and blood circulating times that confirm their potential for thrombol therapies [78].  
Particles based on 2-hydroxyethyl acrylate have been also designed to mimic murine erythrocytes, demonstrating that their biocompatibility, circulation times and biodistribution are conditioned by the particle elasticity [75].  
Stimuli-responsive systems based on poly (methacrylic acid) (PMAA) particles with morphology and flexibility similar to erythrocytes have been developed in order to integrate advantages of these erythrocyte mimicking particles with the potential of drug delivery triggered by existing differences between pathological and healthy regions [77].
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
## 4 Experimental Setup
### Datasets
#### 4.1.2 N-CMAPSS  
This new dataset contains two sub-datasets, namely DS01 and DS02 [37]. The two sub-datasets have been defined in such a way that DS01 should be tested with model-based approaches and DS02 should be tested with data-driven approaches. Hence, here, we only use the sub-dataset DS02 that consists of the run-to-failure degradation trajectories of nine turbofan engines with different initial conditions. Compared to the previous CMAPSS dataset, in this case, the CMAPSS dynamic model was again used to generate synthetic trajectories, but a fidelity gap between simulation and reality was mitigated by reflecting real flight conditions recorded on board a commercial jet. Furthermore, the relation between the degradation and its operation history is considered to extend the degradation modeling [37].  
Table 2 describes the DS02 sub-dataset. A unit in the table indicates each engine. Among the nine units, we use six units (\(u_{2}\), \(u_{5}\), \(u_{10}\), \(u_{16}\), \(u_{18}\) and \(u_{20}\)) for the training set \(D_{train}\), and the remaining three units (\(u_{11}\), \(u_{14}\) and \(u_{15}\)) for the test set \(D_{test}\). In particular, the \(u_{14}\) and \(u_{15}\) relate to shorter and lower altitude flights compared to those of the training units, so that the evaluation results on the \(D_{test}\) can implicitly reflect the generalization capability of the RUL prediction model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **FD001** & **FD002** & **FD003** & **FD004** \\ \hline Number of engines in training set & 100 & 260 & 100 & 249 \\ Number of engines in test set & 100 & 259 & 100 & 248 \\ Max/min cycles in training set & 362/128 & 378/128 & 525/145 & 543/128 \\ Max/min cycles in test set & 303/31 & 367/21 & 475/38 & 486/19 \\ Operating conditions & 1 & 6 & 1 & 6 \\ Fault modes & 1 & 1 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CMAPSS dataset overview.  
In the given dataset, the total number of samples (i.e., timestamps) is 5.26 M in \(D_{train}\) and 1.25 M in \(D_{test}\), with a sampling rate of 1 Hz. Considering the large number of samples, we assume a lower sampling rate of 0.01 Hz by taking one sample every 100. The end-of-life time \(t_{EOL}\) points out the counted flight cycles at the end of the engine's lifespan, i.e., \(t_{EOL}\) is the same as the initial value of the labeled RUL. There are two distinctive failure modes in the dataset: the abnormal high-pressure turbine (HPT) and the low-pressure turbine (LPT). The combination of the two failure modes for a unit means that the unit is subject to a more complex failure mode than a single-failure mode. The dataset provides 20 condition monitoring signals that are related to the useful life of the flight engine. The multivariate time series from the 20 signals is used as an input for our SN P system.
## 5 Evaluation Approach
### Context Description and Feature Models
This configuration replaces the _DEFAULT_SECURITY_ configuration, used in previous versions of the Linux kernel to specify the security module to be used by default. In order to simplify the model and make it easier to read, the restrictions have not been used in the model to represent the dependency relationships between configurations. Instead, optional relationships have been used so that choosing a configuration gives access to the configurations that depend on it (if any). For instance, the configuration of the feature _HARDENED_USERCOPY_FALLBACK_ and _HARDENED_USERCOPY_PAGESPAN_ depends on the configuration of _HARDENED_USERCOPY_ feature which also depends on the configuration of the _HAVE_HARDENED_USERCOPY_ALLOCATOR_ feature. Figure 10: SSL/TLS Feature Model. Figure 12 is related to the security settings provided by the API level of the Android SDK (Android Developer Guide: [[https://developer.android.com/reference/android/provider/Settings](https://developer.android.com/reference/android/provider/Settings)]([https://developer.android.com/reference/android/provider/Settings](https://developer.android.com/reference/android/provider/Settings)). Secure). This feature model represents the security-related system preferences specified in Android SDK API level 29. These preferences must be modified by the user explicitly through the system's user interface or specialised APIs. It should be borne in mind that this model excludes all preferences that, although related to system security, are obsolete at the API 29 (or earlier) level of the Android SDK. Through this model, the security parametres that can be configured by users can be consulted through the apps in version 29 of the Android SDK API. For instance, the list of input methods that are currently enabled by the user is defined in the _ENABLED_INPUT_METHODS_ configuration while the default input method is recorded into the _DEFAULT_INPUT_METHOD_ configuration. Regarding location, most of the available configurations in previous versions of the API level of the Android SDK have been removed from the security settings but in the current version only two of them have remained: (1) _LOCATION_MODE_OFF_ which specifies that the location mode is off, and; (2) _ALLOWED_GEOLOCATION_ORIGINS_ which defines the origins for which browsers should allow geolocation by default. Regarding the feature model structure, the preferences have been grouped in four groups to facilitate the readability of the model and the definition of configurations: (1) _ACCESSIBILITY_ encompasses preferences related to system accessibility; (2) _INPUT_METHODS_ encompasses preferences related to system input methods; (3) _LOCATION_ encompasses preferences related to device location; (4) _TTS_ groups preferences related to the Text-To-Speech system, and; (5) _MISCELLANEOUS_ encompasses preferences not related to any of the groups described above.
# Signal Enhancement of Helicopter Rotor Aerodynamic Noise Based on Cyclic Wiener Filtering
## 4 Experiment
### Helicopter Long-Distance Detection  
The rotor aerodynamic noise will exhibit the harmonic characteristics of the blade passing frequency and its higher-order harmonics in the frequency domain when the helicopter is in operation. Therefore, the detection algorithm was constructed following the envelope spectrum and combined with the harmonic characteristics of the rotor aerodynamic noise to display the detection results intuitively, so as to avoid looking at the envelope spectrum whenever viewing the filtering effect. Next, the spectral peaks were discovered by constructing the spectral peak search function, and whether the helicopter signal was detected was judged according to the constructed, complete detection function. The flow chart of the detection algorithm is provided in Figure 14.  
The part of the Robinson R44 helicopter flying close from a long distance was intercepted, and the detection results were obtained based on the distance, as illustrated in Figure 15. Specifically, one indicates that a helicopter signal was detected, and zero indicates that no helicopter signal was detected. Moreover, the detection rate was calculated every 100 s, allowing it to more clearly judge whether the helicopter signal was effectively detected.  
Before filtering, in Figure 15, the detection rate prior to \(t=1947\) s (corresponding to a distance of 4.114 km) was relatively low, indicating that the helicopter signal cannot be effectively detected. However, the detection rate after filtering was considerable, reflecting that the helicopter signal can be effectively detected. This further confirmed that the cyclic Wiener filter exerted a good enhancement effect on the helicopter rotor aerodynamic noise signal.  
During the field flight experiment, the background noise signal was collected deliberately, and the correctness of the detection code was verified by the background noise signal. The detection rate of the background noise signal is also called the false alarm rate. The false alarm rate of the background noise signal is presented in Figure 16.  
Figure 13: (**a**) Envelope spectrum of the Robinson R44 helicopter before filtering at 17.75 km; (**b**) Envelope spectrum of the Robinson R44 helicopter after filtering at 17.75 km.  
Figure 14: Flow chart of detection algorithm of helicopter.  
Figure 15: Helicopter long-distance detection results.  
As demonstrated in Figure 16, the false alarm rate with the background noise as the input signal for helicopter detection was extremely low, suggesting that the detection algorithm did not judge the background noise signal as the helicopter signal, consistent with the established fact. Figures 15 and 16 revealed that the filtered detection rate for the helicopter noise signal varied from low to high. Meanwhile, the false alarm rate before and after filtering for background noise signals was relatively low. This implied that the detection algorithm effectively distinguished the helicopter signal from the background noise signal, fully verifying the correctness of the detection algorithm.
## 3 Results
### Heterogeneity of Included Studies  
As evidenced by the different sizes of the confidence and prediction regions SROC curves, the heterogeneity of the included studies was high, mostly for moderate OSA (Figure 4). The extensive surface of the prediction regions SROC curves was probably explained by the small number of studies and the large OSA prevalence range in the study populations (ranging between 42.6% and 83% in mild OSA, between 37.4% and 54.8% in moderate OSA, and between 21.3% and 53.8% in severe OSA).  
Figure 4: SROC curves of studies based on ODI3 as oximetry parameter: 95% confidence region (full line area), 95% prediction region (dashed line area), studies (circles), summary point (black dot) and summary curve (full line).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Number of** & **Number of** & **Sensitivity (95\%)** & **Specificity (95\%)** & **Positive** & **Negative** \\  & **Studies** & **Participants** & **Confidence** & **Confidence** & **Predictive Value** & **Predictive Value** \\  & **Studies** & **Interval** & **Interval** & **(95\% Confidence** & **(95\% Confidence** \\  & & **Interval)** & **Interval)** \\ \hline Mild OSA & 4 & 1118 & 61\% (58–65\%) & 84\% (79–88\%) & 91\% (88–93\%) & 46\% (44–49\%) \\ \hline Moderate OSA & 5 & 1237 & 69\% (65–73\%) & 85\% (82–87\%) & 77\% (73–80\%) & 79\% (77–81\%) \\ \hline Severe OSA & 4 & 924 & 79\% (73–84\%) & 88\% (85–90\%) & 76\% (72–80\%) & 89\% (87–91\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of summary estimates for ODI3.  
Figure 3: Forest plot of studies adopting ODI3 as parameter to define abnormal oximetry recordings. CI, confidence interval; FN, false negative; FP, false positive; TN, true negative; TP, true positive [24; 26; 30; 33; 34; 39].
## 1 Introduction
The research on CFST truss has been widely concerned by scholars in recent years. Experimental and analytical research was carried out by [PERSON] et al. [56] on flexural behavior of curved concrete filled steel tubular (CCFST) trusses with curved CFST chords and hollow braces. Test results indicated that the stiffness and load-carrying capacity of CCFST trusses were larger than those of CFST trusses. [PERSON] et al. [57] investigated the flexural behavior of 4 circular concrete-filled stainless steel tubular truss girders. The influence of the location of concrete filling on the flexural behavior of CHS stainless steel tubular truss girder was analyzed. [PERSON] et al. [58] present analytical and experimental findings pertaining to the design and behavior of composite truss members with standoff screws as shear connectors. Composite beams constituted by a concrete-encased steel truss welded to a continuous steel plate were analyzed using a nonlinear finite element formulation based on [PERSON]'s classical model by [PERSON] et al. [59]. [PERSON] et al. [60] tested the truss girders with different web arrangements and investigated their behaviors, drawing the conclusion that the geometry of the CHS girder joints was such that only chord face failure and punching shear failure could occur. Although many researchers have studied the behavior of uniplanar CFST truss and CFST members, there are few investigations being carried out on the performance of Warren CFST trusses. In addition, the reinforcing method of adding a half outer sleeve on each joint was used for Warren CHS tubular trusses and contrasted with the behavior of Warren CFST trusses in this study. Subsequently, an experimental investigation was conducted in this paper on four types of Warren CHS tubular trusses under static loading. The failure modes, load carrying capacity, overall deflection, and strain intensity of all test specimens were discussed. Finite element analysis (FEA) software was used for numerical modelling of Warren CHS tubular trusses and the results obtained by numerical analysis were compared with those of the experimental results.
## 3 Results
### The FEM Model Results
#### 3.2.2 The Strain Distribution  
The principal object of this part was to find the regularities of strain distribution on the cylindrical wall. The strain in two directions was calculated and compared to the monitoring results. At the maximum inner pressure, the strain distribution in two directions is shown in Figure 11.  
The vertical strain on concrete surface is \(\varepsilon_{22}\) (Figure 11a). \(\varepsilon_{22}\) varied from 82 \(\upmu\varepsilon\) to 127 \(\upmu\varepsilon\) with elevation from +4 m to +40 m. The junction of the dome and cylinder which was near the ring girder had the maximum value. As the ring girder was the place where the vertical prestress tendons and dome tendons were anchored, this place might bear stress concentration. Besides, the minimum principal direction strain is the same as circumferential strain (see Figure 11b). The circumferential direction strain showed small difference from elevation 0 m to +45.8 m with magnitude from 211 \(\upmu\varepsilon\) to 297 \(\upmu\varepsilon\). The maximum strain, about 382 \(\upmu\varepsilon\), also occurred at the junction of the dome and cylinder  
Figure 11: The strain distribution contour in two directions at the maximum inner pressure. (Deformation scale factor is 500.) (a) Vertical direction strain contour; (b) Circumferential direction strain contour.  
As a consequence, the strain in two directions was relatively high at the junction of the dome and cylinder. According to the strain distribution regularities, more FBG sensors should be installed at this place.