esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 4 Results and Discussion
### Evaluation Methods
#### 4.1.2 Experimental Method  
In order to verify the practicability of the proposed algorithm, the adaptive noise cancellation system based is built on FPGA, which is shown in Figure 8. The sinusoidal signal with 10 kHz frequency is generated by the signal generator and is amplified by the amplifier. Then The signal is transmitted by the loop antenna, whose diameter is 20 cm. The transmitted magnetic signal is received by a three-channel tunneling magnetoresistance (TMR) sensor (TMR2305). The Y channel of TMR sensor is parallel to the axis of the loop antenna. The Y channel and X channel of TMR sensor are parallel to the horizontal plane. The Z channel of TMR sensor is vertical to the horizontal plane. The three channels are perpendicular to each other. After amplifiers, filters and 14-bit A/D converters, the received signals are processed by the FPGA. To generate the impulsive noise, a switched-mode power supply is used as the noise source. The comparison between original signal and filter output is made to show the noise cancellation performance of proposed algorithm.
###### Abstract  
This paper presents an experimental study of a novel automated manufacturing process that integrates cold embossing to add complex features, such as micro-Fresnel lens designs, onto a 3D-printed ABS polymer component. The research demonstrates that precise control over process parameters, including embossing time (E\({}_{\text{t}}\)) and velocity (E\({}_{\text{v}}\)), is critical for successful feature replication. Gloss analysis confirmed that surface softening as a crucial prerequisite for embossing was successfully achieved using a vapour smoothing (VS) chamber that was developed and optimised for the process. High-speed automation using a 6-axis KUKA robot allowed 48 embosses to be completed in just over one minute, highlighting its efficiency over conventional hot embossing (HE) methods. Results showed that an E\({}_{\text{t}}\) (0.01 s) prevented feature replication as there was insufficient time to allow for polymer flow, while an optimal E\({}_{\text{t}}\) (0.1 s) produced high-quality embosses across all test segments. Additionally, this study identified that while insufficient cycle times hinder polymer flow, extended durations can lead to surface hardening, prohibiting replication. These findings pave the way for integrating Diffractive Optical Elements into 3D-printed parts, potentially enhancing precision, functionality, and productivity beyond the capabilities of standard 3D-printing processes.  
additive manufacturing; automation; cold embossing; diffractive optical lens; vapour smoothing 2020
# Water Retention Curve of Biocemented Sands Using MIP Results
## 3 Materials and Methods
### Tests Performed
#### 3.4.5. Water Retention Curve  
The water retention curve was measured by using the water potentiometer equipment WP4-C [40; 41]. This equipment allows suction measurements between 0.5 and 84 MPa, which is not enough for sandy soils, as their air entry value is below 1 MPa, and a significant part of the curve may be under this low suction value. This equipment was used for bioemented soils by reference [13], but not for uniform grading-size sands.  
The vapor equilibrium was also used to confirm WP4 measurements; however, the applied suctions were above 0.5 MPa (0.5, 3, 15, and 39 MPa). The solutions used were prepared with NaCl, following the procedure described in Reference [42].  
Only the drying paths are analyzed in this paper; they are compared with the pore distribution curve of the intrusion part of the MIP test. Although there are many equations to be used in the literature (see Reference [43], for example), the equation proposed by [PERSON] [17] (Equation (12)) was used to adjust the MIP and the experimental data because this equation is the one most used in commercial programs:  
\[Sr=\left(1+\left(\frac{s}{p}\right)^{\frac{1}{1-y}}\right)^{-y} \tag{12}\]where \(s\) is suction, \(Sr\) is the degree of saturation, \(P\) is the air entry value (MPa), and \(y\) is a constant. Constants \(P\) and \(y\) are determined numerically.
## 2 Materials and Methods
### Surface Functionalization of MCM-41 and Lipase Immobilization  
To obtain a hydrophobic carrier for lipase immobilization, the surface of MCM-41 was treated through grafting modification with a silane coupling agent of C\({}_{8}\)[40]. Firstly, 0.5 g of MCM-41 was mixed with 5 mL of n-hexane and 231 \(\upmu\)L of n-octyltrichlorosilane at room temperature, which were stirred at the speed of 220 rpm for 40 min. Then, the well-mixed solutions were filtered using a Buchner funnel to collect the reacted solids, which were moved into a 60 \({}^{\circ}\)C drying oven (DHG-9030A, Shanghai Yiheng Scientific Instrument Co., Ltd., Shanghai, China) for 24 h. Finally, the dried solids, namely, MCM-41-C\({}_{8}\), were stored in the dryer at room temperature for long-term use.  
The immobilization of Candida antarctic lipase B (CALB) on MCM-41-C\({}_{8}\) was prepared based on the method reported by [PERSON] et al. [41]. To start, a certain amount of CALB was added into phosphate buffer (50 mM, pH 5-9) and stirred at 4 \({}^{\circ}\)C for 30 min to obtain the lipase solution. Then, 0.1 g of MCM-41-C\({}_{8}\) was then prewetted with 200 \(\upmu\)L of pure ethanol, followed by being immersed into 10 mL of prepared lipase solution and incubated in a shaking incubator (THZ-C, Suzhou Peiving Experimental Equipment Co.actorv, Suzhou, China) with the temperature of 30 \({}^{\circ}\)C and the shaking speed of 220 rpm for 40 min. Afterward, the whole contents were centrifuged using a refrigerated centrifuge (Allegra X-12, Beckman Coulter, Inc., Brea, CA, USA) at 8000 rpm for 10 min to obtain the supernatant, which was used to determine the amount of residual protein using a BCA protein assay. Finally, the precipitates of immobilized lipase CALB@MCM-41-C\({}_{8}\) were collected and washed using phosphate buffer at least 3 times and freeze-dried in a vacuum freeze-drying machine (YTLG-18A, Shanghai Yetuo Technology Co., Ltd., Shanghai, China) to obtain the targeted CALB@MCM-41-C\({}_{8}\).
## 4 Payload Development
### Vacuum Testing  
During flight, the payload was expected to withstand pressures ranging from 3 to 1000 hPa. The payload was tested in a vacuum chamber to assess its performance in a vacuum environment. Following the vacuum test, all components were tested to confirm proper functionality.
# Evaluation of the Level of Reliability in Hazardous Technological Processes
## 2 Definition of Safety-Critical Function
### ETA and FTA  
The analysis of dangerous situations and risks is one of the phases of the safety life cycle. The assessment of adverse events follows from the concept of the development of the EUC controlled device and its control system. The assessment of risks or adverse events influences the adoption of different measures based on the frequency of the event and the consequent severity of the event. Many analytical methods are used to analyze hazards  
Figure 1: The relationship between the residual risk and the tolerable risk [9].  
and risks. These methods include the techniques fault tree analysis (FTA) and event tree analysis (ETA) [10; 11].  
FTA is a fault tree analysis. The method is used to identify the probable occurrence of an event that may lead to an adverse event. Events are determined deductively, based on the peak event and looking for the causes that could have caused this event. A tree diagram is used to represent the causes, which shows the structure of the identified events about the fault events. The tree diagram can be used to identify potential causes and failures for qualitative analysis. Quantitative analysis allows you to calculate the probability of occurrence of a peak event based on the partial probabilities of the event [12; 13].  
ETA is an event tree analysis. The analytical technique used for general reliability assessment. The course of the process and the events that can lead to a possible accident are evaluated. Based on the analysis, equipment failures or errors are specified. The adverse event is the basic starting point. Subsequently, all system responses that may lead to a failure are taken into account. The aim is to evaluate measures, e.g., safety features that are effective in reducing side effects. The method can be used as a complementary FTA method to determine the consequences of an adverse event [13; 14].
## 3 Results and Discussion
### Drying Kinetics of the NR Sheets  
Figure 5 presents the NR sheet temperature change during the drying process at each type of coagulating material, airspeed, and temperature. Figure 5 shows the example data of the experimental values at various drying temperatures (50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C), where the airspeed was controlled at 0.3 m/s and 0.7 m/s. Initially, the NR sheet temperature samples were at approximately 30 \({}^{\circ}\)C. The NR sheet temperature increased rapidly during the first hour after the drying process started due to heat transfer from the hot air, and subsequently, the NR sheet sample temperature stabilized. The results show that increasing the air speed did not affect the NR sheet temperature at the same drying temperature and time period. This agreed with the results reported in [7; 38; 54; 55]. In addition, the results found that the type of coagulating material and air speed value did not affect the NR sheet temperature.  
Figure 4: Plasticity retention index change during the drying process.  
Figure 5: The NR sheet temperature change during the drying process (HA = hot air, PR = para rubber, Bb = bamboo, and CS = coconut shell).  
Figure 6 presents the NR sheet moisture change during the drying process for each type of coagulating material, airspeed, and temperature. When the drying process started, the NR sheet moisture rapidly reduced through convection heat transfer over the first 12 h. Afterward, the decrease in moisture of the samples was at a constant rate due to heat diffusion until drying finished at a final moisture of 3.0% d.b. [7, 56]. For this reason, as the drying temperature was increased, the drying time reduced.
# New Remote Care Models in Patients with Spinal Cord Injury: A Systematic Review of the Literature
## 3 Results
### Interventions  
All four examined RCTs [34; 35; 36; 37] compared home care for SCI patients supported by innovative technology with standard in-person care; technologies employed included two applications [36; 37]: a web-based platform [35] and a real-time service based on an encrypted software [34].  
Specifically, in the study by [PERSON] et al. [34], 56 SCI patients were randomized for two different treatment strategies for pressure injuries: in the experimental group, district nurses acted at patients' homes with the support of a real-time videoconference (CISCO Telepresence system) with a multidisciplinary team (formed by a wound care doctor, a wound care-trained nurse, and an occupational therapist). Patients in the control group continued their regular in-person care; both study arms were followed until the complete healing of the pressure injuries (or for a maximum of 52 weeks).  
In the study by [PERSON] et al. [35], 19 participants in the intervention group were followed using an application called iMHere to address major SCI complications (urinary tract infections, pressure injury, assessment of independence, mood, and quality of life). The remaining 19 patients received in-person care without any technological device. In this study, a multidisciplinary team was composed of a nurse, physiotherapist, physiotherap, and doctor. Outpatient follow-up, based on participants' health status and doctor decision, were conducted 9 months after the start of the study.  
In the study by [PERSON] et al. [36], 78 participants were randomized in two groups: in the experimental one, through a web-based platform, patients had direct communication with the multidisciplinary team (composed of nurses, doctors, and psychological consultants). The WeChat platform was used to perform one-on-one continuous care intervention; moreover, patients were facilitated to communicate their health status by uploading photos or videos, allowing the multidisciplinary team to quickly evaluate any red flags. Participants in the control group had a regular follow-up at 6 months.  
Finally, in the study by [PERSON] et al. [37], a population of 98 SCI patients was randomly assigned to experimental/control groups. At the beginning of the study, an application called Together, accessible both to the team and the patients, was designed. In the experimental group (49 SCI patients), the app was used to provide remote assistance through health-education, case sharing, and interaction with the patient, while the control group had a single routine telephone follow-up conducted 12 weeks after the start of the study. The multidisciplinary team was composed of a nurse, doctor, physiotherapist, and occupational therapist.
## 3 Results and Discussion
### Performance of Blob-Level Classification
#### 3.4.4 Discussion  
In the case of the small size polymers, the Fusion model clearly outperformed the VNIR and SWIR models when the detection rates were compared by 26% and 13%, respectively. The VNIR model performed well when the color of the samples was distinguishable from the chicken fillet but missed white materials which had a similar color to the fat or specular reflection. The SWIR model performed well to detect the samples that had different chemical compositions from the chicken fillet but missed red latex glove and blue facial mask pieces that were spectrally similar to the muscle tissue. However, the Fusion model successfully detected these small FM pieces missed by the VNIR and SWIR models. Hence, these results implied that the fusion of VNIR and SWIR HSI modalities was effective in the detection and classification of polymers.  
In the case of the small metal and wood pieces, the detection rates (metal: 81%, wood: 95%) of the Fusion model were much higher than the VNIR model by 37% for metal and 50% for wood, while they were similar in detecting wood (95%) or slightly higher in detecting metal (79%) compared with the SWIR model. The specular reflection on the metal surfaces and the light color of the wood samples were the main cause of the low detection rate of the VNIR and SWIR models. The results suggested that although the detection of an FM object was affected by its semi-transparency or specular reflection at the pixel-level, a portion of the object could be still detected as FM by the Fusion model as long as some part of the object was detected as a blob. In summary, the Fusion model improved the FM-blob detection rate by about 13% for polymer and 2% for metal detection compared with the SWIR model.  
Notice that the precisions on the test set were increased compared with the precisions on the training set mainly because the number of TP was increased for the test set, while the number of FP for the test set remained similar to the training set. This result suggested that more test data would increase the precision of the models. Although the precision (95%) of the fusion algorithm was slightly lower than the precisions of the VNIR (98%) and SWIR (96%) models, the recall (93%, sensitivity) of the Fusion model was much higher compared with the VNIR (62%) and SWIR (82%) models. Therefore, the F1 score of the Fusion model was higher than the VNIR (76%) and SWIR (89%) models, which indicated the Fusion model we developed worked efficiently in detecting small size FM samples. In addition, although more FPs were detected in the Fusion model, the JAC (88%) was much higher than the VNIR (62%) and SWIR (79%) models. Therefore, we concluded that the Fusion model outperformed the single modality models.
# The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand
###### Abstract  
While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDFs) to investigate the impact of built environment factors on ride-hailing travel demand, including their nonlinear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. Population density, land use mix, and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.  
ride-hailing; built environment; XGBoost; SHAP; nonlinearity
# Nondestructive Evaluation of Aluminium Foam Panels Subjected to Impact Loading
## 2 Materials and Methods  
### Materials  
Several AF specimens with various thicknesses, foam densities and skin materials were produced to be used in the LVI tests. The chemical composition of the AF, produced by the powder metallurgical (PM) route, was 10 wt.% Si and Al balanced, while 0.4 wt.% of titanium hydride (TiH\({}_{2}\)) powder was used as the foaming agent via heating to 620 \({}^{\circ}\)C [33]. The matrix material (AlSi10) was an aluminium alloy with good physical, thermal and mechanical characteristics (density 2659 kg/m3, electrical resistivity 4.91 Mu\(\cdot\)cm, thermal conductivity 110 W/m\({}^{\circ}\)C and tensile strength 379 MPa), together with excellent machinability properties. The density of the AF panels ranged from 400 to 640 kg/m\({}^{3}\). The in-plane dimensions of the specimens were 60 mm \(\times\) 60 mm. The specimens had two values of thickness, 20 mm and 34 mm. GFRP composite and aluminium alloy 6082 were used for the skins of AFS panels. The mechanical characteristics of GFRP and aluminium skins can be found in [13; 34]. The mechanical properties of the GFRP skins were the following: \(\rho\) = 1328 kg/m\({}^{3}\), E = 7500 MPa, \(\sigma_{\text{u}}\) = 80 MPa and \(\epsilon_{\text{max}}\) = 1.3%. The layup of GFRP skins, manufactured by hand layup, consisted of six layers according to the following stacking sequence: 2 MAT layers, 2 central layers oriented at 0\({}^{\circ}\) and 45\({}^{\circ}\) and 2 MAT layers. The central layers consisted of 0\({}^{\circ}\)/90\({}^{\circ}\) bidirectional fabrics. The total thickness of a GFRP skin was equal to 4.3 mm. The bidirectional fabrics had a specific weight of 400 g/m\({}^{2}\) and the MAT layer had a specific weight of 225 g/m\({}^{2}\). The fibre volume fraction was 0.55. The aluminium skin with total thickness of 2 mm was used in order to yield identical in-plane flexural stiffness with GFRP skin. The flexural stiffness D of both aluminium and GFRP was calculated in [13] by applying both Classical Lamination Theory and the Dietz approach [26]. Double-layer AFS panels were manufactured by applying epoxy adhesive provided by Teknica.  
Six different types of AF panels, shown in Figure 1, were investigated by means of LVI tests:  
* Type I: AF panels without external skins and with a 20 mm thickness;
* Type II: AF panels without external skins and with a 34 mm thickness;
* Type III: AFS panels with aluminium skins and with a 22 mm total thickness;
* Type IV: AFS panels with aluminium skins and with a 36 mm total thickness;
* Type V: AFS panels with GFRP skins and with a 38 mm total thickness;
* Type VI: double-layer AFS panels with aluminium skins and with a 44 mm total thickness.
## 3 Application Results and Discussion
### Sensitivity Analysis and Variable Importance  
Sensitivity analysis (SA) of variables is a technique used to determine how different values of predictor variables will affect an output variable. For each independent variable, the SA% is calculated as follows [7]:  
\[L_{i}=t_{max}(x_{i})-t_{min}(x_{i}) \tag{23}\]  
\[SA_{i}=\frac{L_{i}}{\sum_{j=1}^{M}L_{i}}\times 100 \tag{24}\]  
where \(t_{max}\) and \(t_{min}\) are the maximum and minimum of the estimated target over the \(i_{\text{th}}\) input domain, respectively, where other independent variable values are equal to their average values. The result of variable importance for the simulation of mechanical characteristics of RCCP is indicated in Figure 10 based on the RF model (best model). These figures show that the most effective variable in CS, TS, and FS estimation of RCCP is fine aggregate content.
## 2 Design and Fabrication
### Wide-Spectrum, Low-Reflectance Coating Design
Figure 1b illustrates the variations in reflectivity in a four-layer Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) film system when the thickness of each individual layer is varied by 1 nm. For the Nb\({}_{2}\)O\({}_{5}\) layers, a variation of 1 nm in the thickness of the first high-refractive-index layer results in a wavelength range, with reflectivity value below \(1\times 10^{-5}\), extending from 1570 nm to 1583 nm, and the reflective bandwidth is only 13 nm, as shown by the red line in Figure 1b. Similarly, for the third layer, the corresponding wavelength range, with reflectivity below \(1\times 10^{-5}\), shifts to 1536-1569 nm, as shown by the pink line in Figure 1b. These results indicate that for the Nb\({}_{2}\)O\({}_{5}\) layers, the reflective bandwidth changes significantly with thickness variations. In contrast, for the low-refractive-index SiO\({}_{2}\) layers, changing the thicknesses of the second layer and the fourth layer by 1 nm leads to reflection bandwidths, with reflectivity below \(1\times 10^{-5}\), of 50 nm to 75 nm, as shown by the blue and green lines in Figure 1b, respectively. These findings suggest that for the SiO\({}_{2}\) layers, a 1 nm variation limit for thickness ensures full coverage of the C-band (1530-1565 nm). As shown in Figure 1b, the thickness precision requirements for the Nb\({}_{2}\)O\({}_{5}\) layers are more stringent than those for the SiO\({}_{2}\) layers. The variation in the thickness of the Nb\({}_{2}\)O\({}_{5}\) layers has a significant impact on the reflection bandwidth as compared to the SiO\({}_{2}\) layers. However, the reflection bandwidth parameters are critical for determining whether narrow-linewidth lasers can achieve a wide tuning range. To achieve precise control over reflectivity and larger reflective bandwidth, the design precision is set at 0.01 nm, while the coating equipment control precision is maintained at 0.1 nm.