esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Nanohybrid Characterization  
Figure 1 shows the SEM images of MWCNT-COOH, p-MWCNT-Pd, np-MWCNT-Pd and Pd/C. The MWCNTs had diameter in the range of 20-40 nm and the length was about 10-30 um. There was no detectable change in tube morphology after acid treatment or after hybrid formation, implying that there was minimal visible damage to the tube structure. It is quite evident from the SEM images that the CNTs were coated with the metal nanoparticles. The EDX data shows large amounts of metal particles on the surface of the CNTs which are around 41.02% and 36.78% for p-MWCNT-Pd and np-MWCNT-Pd, respectively. The size of Pd particles ranged from 1 to 10 nm on p-MWCNT-Pd and from 5 to 50 nm on np-MWCNT-Pd. This implied that the catalyst was nanostructured and this was expected to contribute towards enhanced activity [19].  
The FTIR spectra shown in Figure 2 confirmed the presence of functional groups in MWCNT-COOH, MWCNT-ODA, p-MWCNT-Pd, and np-MWCNT-Pd hybrids. The carbonyl stretching frequency in MWCNT-COOH was seen at 1716 cm\({}^{-1}\) (COOH). The 3440 cm\({}^{-1}\) band (O-H) present in MWCNT-COOH spectrum was attributed to the hydroxyl vibration of the carboxylic acid group introduced through functionalization. In Figure 2b, the peaks around 1636 cm\({}^{-1}\) and 1715 cm\({}^{-1}\) were attributed to stretching vibrations of the amide C=O group, the sharp peaks at 2921 cm\({}^{-1}\) and 2847 cm\({}^{-1}\) were attributed to the C-H stretching vibration of the alkyl chain from ODA, the peak at 3460 cm\({}^{-1}\) was attributed to the N-H stretching vibration from ODA, and the peak at 1250 cm\({}^{-1}\) was attributed to C-N stretching vibration. In all the samples, the peak around 1580 cm\({}^{-1}\) was assigned to the C=C stretching of the carbon skeleton. The appearance of new bands at 993 cm\({}^{-1}\) and 996 cm\({}^{-1}\) in the spectra for the hybrids confirmed the presence of Pd [37; 38; 39]. The broad peak centered around 3440 cm\({}^{-1}\) in MWCNT-COOH was not prominent in the IR spectrum of p-MWCNT-Pd, and the shift in the carbonyl stretching frequency from 1716 cm\({}^{-1}\) to 1721 cm\({}^{-1}\) in the spectra for the hybrids indicated interaction of Pd with the COOH group.  
Figure 1: SEM images of (**a**) MWCNT-COOH, (**b**) p-MWCNT-Pd, (**c**) np-MWCNT-Pd, and (**d**) Pd/C.  
Figure 2: FTIR data for (**a**) MWCNT-COOH, (**b**) MWCNT-ODA, (**c**) p-MWCNT-Pd, and (**d**) np-MWCNT-Pd.
## 2 Project Overview, Problems, and Solutions
### Engineering Background  
The Liantang Tunnel is a crucial control project for the eastern transit expressway in Shenzhen. It starts within Xianhu Botanical Garden in Luohu District, Shenzhen, China, and ends at Liantang Port, which borders Hong Kong, China. The tunnel has a total length of 2.77 km and a design speed of 60 km/h. Furthermore, the tunnel is adjacent to Shenzhen Reservoir and Liantang residential area, with a nearest straight-line distance of 120 m between the construction workforce and Shenzhen Reservoir. The Liantang tunnel is 2.65 km long. The tunnel's left-lane bifurcation-out section is a typical bifurcation-out tunnel with a super-large cross-section that uses the \"3-lanes and 2-lanes\" interchange arrangement. All lanes converge into five lanes and then gradually convert to four lanes. It is China's first subterranean highway interchange tunnel. Figure 1 shows the plan of the bifurcation-out section.  
The bifurcated tunnel with a super-large section is formed by the convergence of a three-lane tunnel and a two-lane tunnel, resulting in a super-large-section five-lane tunnel (Figure 2a). The maximum section area is 428.5 m\({}^{2}\), located in the section from K1+880 to K1+901, and it is the world's largest highway tunnel constructed by the drilling and blasting method. The maximum span and crown height are 30 m and 18.4 m, respectively, with a height-to-span ratio of 0.61. The minimum thickness of the interlayered rock in the small-clearance section is 0.5 m, making it the second smallest clearance tunnel in the world. The section areas of the two-lane and three-lane tunnels are 100.4 m\({}^{2}\) and 137.5 m\({}^{2}\), respectively. The two lanes lead to the municipal road and the center of Liantang Port. The plan view and the sectional structure at the junction interface of the bifurcated tunnel are shown in Figure 2b.  
Figure 1: Geographical position of Liantang Tunnel.  
According to the engineering geological exploration report of Liantang tunnel, the topography along the bifurcation tunnel is characterized by low hills and undulating terrain, with relatively intact landforms. The tunnel passes through mainly Carboniferous layered metamorphic sandstone and Jurassic rhyolite formations. The tunnel section is located in the foothills of low mountains with elevations below 100 m, and the slope of the mountains is gentle. The maximum depth of cover is approximately 90 m. The tunnel alignment is not within a high-stress zone, and the surrounding rock in the area consists mostly of moderately weathered sandstone, classified as a Class III rock mass. The rock mass exhibits well-developed joint fissures, and many of the fissure surfaces are stained with iron and manganese oxides. The groundwater is expected to be moist, dripping, or flowing during tunnel excavation in a linear fine water flow. The surrounding rock has a low level of stability. Medium and minor landslides may occur locally when the arch is unsupported, although the side wall is often secure. The longitudinal section of the main tunnel segment is shown in Figure 3.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Mission Preprocess for All Levels of Granularity
#### 2.1.3 Cost Calculation  
Mission cost consists of three parts: time cost, memory cost, and power cost. The time cost \(c_{t_{i}}\) is defined as:  
\[c_{t}=\left\{\begin{array}{cc}10\cdot\log_{10}\frac{T_{i}^{5}}{100}&T_{i}^{ 5}>100\\ 0&T_{i}^{5}\leq 100\end{array}\right., \tag{8}\]  
where \(T_{i}^{5}\) is the start time of time window of target \(i\).  
The mission duration \(t_{d_{i,j}}\) corresponds to the \(i\)th target and \(j\)th stage of the mission, expressed as:  
\[t_{d_{i,j}}=\left\{\begin{array}{cc}2&j\leq 2\\ T_{i}^{E}-T_{i}^{5}&j>2\end{array}\right.. \tag{9}\]  
Memory cost per second is considered as a constant corresponding to the camera, written as MEM. The memory cost \(c_{m}\) changing with mission duration can be expressed as  
\[c_{m}=MEM\times t_{d_{i,j}}. \tag{10}\]  
Similar to memory cost, average satellite power is also considered as a constant: POW. The power cost \(c_{p}\) also changes with mission duration, expressed as  
\[c_{p}=POW\times t_{d_{i,j}}. \tag{11}\]  
The total cost is the sum of these three costs, i.e., the cost for \(i\)th target during its \(j\)th stage, is \(c_{i,j}=c_{t}+c_{m}+c_{p}\).
## 5 Experimentation and Result Analysis
### Comparative Analysis of Proposed Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{4}{c}{**NSL-KDD+**} & \multicolumn{4}{c}{**NSL-KDD+20**} \\ \cline{2-9}  & **Precision** & **Recall** & **FPR** & **F1-Score** & **Precision** & **Recall** & **FPR** & **F1-Score** \\ \hline LSSVM-IDS & 52.74\% & 52.89\% & 8.78\% & 53.17\% & 55.78\% & 55.91\% & 7.14\% & 55.88\% \\ AE-CGAN-RF & 64.94\% & 65.06\% & 5.17\% & 64.62\% & 67.23\% & 67.98\% & 4.97\% & 67.57\% \\ RNN-IDS & 82.14\% & 82.77\% & 3.47\% & 81.76\% & 85.04\% & 85.87\% & 3.01\% & 84.97\% \\ SSAE-LSTM & 84.17\% & 86.16\% & 2.74\% & 84.87\% & 88.54\% & 89.03\% & 2.14\% & 88.67\% \\ Proposed model & 91.94\% & 92.05\% & 1.85\% & 91.88\% & 95.42\% & 96.07\% & 1.06\% & 95.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance comparison. Figure 7: The performance of the proposed model with other compared models on NSL-KDD+ and NSL-KDD+20 datasets. by outperforming other models in terms of accuracy. Furthermore, the suggested model may be integrated into a real-time intrusion detection system to increase detection speed and accuracy.
## 2 Materials and Methods  
A literature search was performed including all the studies published until June 2022 regarding application of 3D printing in orthopedics. Initially, all the studies describing application of 3D-printed models in the pre-operative planning of different orthopedic fields were included. Studies were screened and more relevant studies were briefly described.  
However, to better understand the role of 3D-printed models in Knee replacement surgeries, two cases were reported. In both the cases a 3D printed model, based on a CT scan, was produced by Medics(r) (Turin, Italy). The 3D model was printed with FDM additive technology and was provided with a further document with all measurements performed. Furthermore, the model can be printed separable in two parts, especially in revision cases: the bone (after implant removal) and the implant. The surgeon can decide to make bone, implant and cement in different colors in the document, as well as in the 3D-printed model. Finally, the manufacturer can provide the surgeon with a bone thickness map, which turns out useful in planning more delicate zones.
# Correlations between SSQ Scores and ECG Data during Virtual Reality Walking by Display Type
###### Abstract  
To encourage the application of virtual reality (VR) in physical rehabilitation, this study analyzed the occurrence of motion sickness when walking on a treadmill in virtual straight paths presented on two types of displays (screen and head-mounted displays (HMDs)) at a constant speed of 3.6 km/h. The simulator sickness questionnaire (SSQ) scores, which indicate motion sickness, were collected from the participants. In addition, the heart rate (HR) and heart rate variability (HRV; RMSSD and LF/HF ratio) were measured from electrocardiogram data. The correlations between the SSQ scores and HR and HRV were examined to identify a reliable variable for evaluating motion sickness. The SSQ scores were used to classify the data into the motion-sickness and no-motion-sickness groups. The data were classified into the motion-sickness group if a minimum difference of 15 points existed between the walking and baseline phases when using the screen and HMD; otherwise, the data were classified into the no-motion-sickness group. The HR and LF/HF ratio were higher, whereas the RMSSD was lower in the motion-sickness group. Moreover, within the motion-sickness group, the reduction in RMSSD and increase in HR and LF/HF ratio were greater with the HMD than with the screen. Regression analysis was performed on the HR, HRV, and SSQ scores to differentiate between the motion-sickness and no-motion-sickness groups. The regression analysis results showed a high negative correlation between the SSQ score and RMSSD. The results of this study can assist in controlling the occurrence of motion sickness in VR-based applications.  
virtual reality walking; display motion sickness; screen and HMD; SSQ score; heart rate; heart rate variability; cybersickness +
Footnote †: journal: Applied sciences
# Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite
Therefore, the range of adjacent sub-images is relatively large, and the resolution of the wind speed inversion is low (\(25\times 25\) km). With the ongoing development of satellite technology, networked satellites will become a practical satellite working mode. Multiple spaceborne SARs are used to observe the sea surface and obtain SAR data of the same sea area under different incident angles, which can ensure the consistency of the sea surface wind field between different SAR data. Therefore, when applying the wind speed inversion method of multi-incident angle to the data of a networked SAR satellite, the resolution of the SAR wind speed inversion is not limited by the differences in the incident angles. In view of the current problems in wind speed inversion, this paper proposes a working mode of satellite networking. In this mode of operation, three satellite-borne SARs observe the same sea surface at different incidence angles. Since this working mode of networking satellites has not been implemented, the actual networking SAR data cannot be obtained. Therefore, this paper obtains the networked X-band SAR data through simulation. Aiming at the data of three SARs observing the same sea surface at different incident angles, this paper uses XMOD to establish a cost function. By minimizing the cost function, high-resolution wind speed information is extracted without inputting wind direction information. Under the condition of less noise of SAR data, inputting wind direction information can further improve the accuracy of wind speed information.
## 3 The Impeller Optimization Design Method  
### Theoretical Analysis  
Figure 8 illustrates the forces acting on a fluid particle within the rotating centrifugal impeller, where the effects of gravity and fluid viscosity are neglected, and it is assumed that the velocity component perpendicular to the plane of the figure is zero. Assuming the fluid particle inside the impeller has a mass \(m\), it experiences centrifugal force \(F_{R}=m\omega^{2}R\), Coriolis force \(F_{C}\), and centrifugal force \(F_{S}\) due to the curvature of the streamlines. The forces are decomposed In the direction perpendicular to the streamline as follows [34]:  
\[F_{S2}=m\omega^{2}R\cos\beta \tag{3}\]  
\[F_{S}=m\frac{W^{2}}{R_{S1}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Inlet Domain** & **Outlet Domain** & **Volute Domain** & **Impeller Domain** & **Total** \\ \hline
1,308,411 & 1,207,765 & 3,019,409 & 4,529,117 & 10,064,702 \\ \hline \end{tabular}
\end{table}
Table 2: The mesh numbers for each domain.  
Figure 6: Grid independence verification.  
Figure 7: Wall y+ distribution.  
\[F_{c}=2 mW\times\omega \tag{5}\]  
where \(\omega\) is the angular velocity of the impeller, \(W\) represents the local relative velocity, and \(\beta\) denotes the blade setting angle.  
In general, the generation of secondary flows within the impeller is due to the forces acting perpendicular to the direction of the streamlines. Therefore, the introduction of the \(R_{OB}\) number is used to characterize the relative magnitude of the forces acting perpendicular to the streamlines on the fluid inside the impeller:  
\[R_{OB}=\frac{F_{S2}+F_{S}}{F_{c}} \tag{6}\]  
The value of \(R_{OB}\) determines the direction of motion of the fluid particles relative to the main flow. When \(R_{OB}\) is approximately equal to 1, the forces acting perpendicular to the streamlines are balanced, and there is no significant secondary flow within the impeller. When \(R_{OB}\) is less than 1, the Coriolis force dominates, causing the fluid to shift towards the pressure surface. When \(R_{OB}\) is greater than 1, secondary flows drive the fluid to deviate towards the suction surface of the blade.  
By combining the above expressions and setting \(R_{OB}\), Equation (6) can be rewritten as:  
\[(\frac{1}{2}\omega^{2}R\sin(2\beta)\sin\beta R_{s1})A^{2}-(2\omega Q\sin\beta R _{s1})A+Q^{2}=0 \tag{7}\]  
Define \(x_{1}=Rsin(2\beta)sin\beta R_{s1}\) and \(x_{2}=sin\beta R_{s1}\); by calculating the linear correlation coefficients between variables \(x_{1}\) and \(x_{2}\) with the radius R, it is found that their values are as high as 0.99. This indicates that variables \(x_{1}\) and \(x_{2}\) can be approximated as linear functions of R:  
\[x=mR+n \tag{8}\]  
where \(m\) and \(n\) are constant coefficients, whose values can be calculated based on the original impeller geometric parameters. By substituting these values into Equation (7), the optimal flow area distribution at different radii within the impeller passage can be determined.
## 4 Results and Discussion
### Rain Cell Retrieval by Network of ESLs and HMLs
Additionally, this illustrates the ability of the joint network of ESLs and HMLs to retrieve rain cells with high accuracy.
# Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
## 2 Materials and Methods
### Magnetic Positioning System (MPS)
#### 2.1.2 Transmitting Nodes
A connected client can set a new PWM frequency by writing a new value in the specific characteristic. The output frequency of the PWM peripheral is obtained through a fractional division of the internal main clock. To easily set the right divider ratio through the BLE connection, a lookup table is implemented in the CYBLE-222014 memory. The value sent from the BLE client represents the index of this table. The index range goes from 0 to 122, mapping the available frequency interval (149.5 to 210.5 kHz, 500 Hz-steps). When a new index is received by the transmitting node, the on-board firmware checks its validity, updates the clock divider ratio and the PWM output frequency changes. The fractional hardware clock divider permits a good degree of flexibility but does not allow an arbitrary choice of the output frequency; the maximum deviation between the real and the desired frequency is, however, always less than 50.39 Hz and known a priori for each selected frequency. Hence, under such conditions, the receiving section works properly. To improve the usability of the overall system, the last received index is also saved in the ROM memory and restored at each power-on cycle. Five transmitting nodes have been realized and are placed on each finger of the hand. Since every transmitting node must be addressed univocally, every board has been programmed with a unique Bluetooth name to facilitate its individuation in the client discovery process. Each PCB is supplied by a 3.7 V, 85 mAh Li-Po battery. The current absorbed when the resonant coil is disconnected is 13 mA; when the coil is connected, the current is 27 mA. Battery duration in working conditions can thus be estimated as \(\approx\)3 h. The size of the battery is 15 \(\times\) 15 \(\times\) 5 mm, and it has been chosen in order to not exceed the size of the PCB. The battery can thus be attached directly on the PCB, obtaining a compact, light and easy-to-handle device. Such a small device can be comfortably mounted on any body part, or on other objects, in different positioning scenarios.
###### Abstract  
Emerging cyber-physical systems, such as robot swarms, crowds of augmented people, and smart cities, require well-crafted self-organizing behavior to properly deal with dynamic environments and pervasive disturbances. However, the infrastructures providing networking and computing services to support these systems are becoming increasingly complex, layered and heterogeneous--consider the case of the edge-fog-cloud interplay. This typically hinders the application of self-organizing mechanisms and patterns, which are often designed to work on flat networks. To promote reuse of behavior and flexibility in infrastructure exploitation, we argue that self-organizing logic should be largely _independent_ of the specific application deployment. We show that this separation of concerns can be achieved through a proposed _\"pulverization approach\"_: the global system behavior of application services gets broken into smaller computational pieces that are continuously executed across the available hosts. This model can then be instantiated in the aggregate computing framework, whereby self-organizing behavior is specified compositionally. We showcase how the proposed approach enables expressing the application logic of a self-organizing cyber-physical system in a deployment-independent fashion, and simulate its deployment on multiple heterogeneous infrastructures that include cloud, edge, and LoRaWAN network elements.  
Keywords:self-organization; decentralized control; deployment independence; declarative programming; aggregate computing +
Footnote †: journal: Future Internet
## 1 Introduction
This study collects an adequate bio-optical dataset that covers the measurements of inherent optical properties, such as particulate backscattering (\(b_{\text{bp}}\)), scattering (\(b_{\text{p}}\)), and beam attenuation (\(c_{\text{p}}\)) coefficients, remote sensing reflectance (\(R_{\text{rs}}(\lambda)\)) measurement, and total suspended matter (TSM) measurement. The investigated water areas of this study were the Bohai Sea and Yellow Sea. The objective of the current study was to develop a method for remote sensing estimation of \(n_{\text{p}}\) by using satellite ocean color data. This study proposes a multiple-step hybrid model that firstly depends on the TSM and \(b_{\text{bp}}\) retrievals, then the established close relationships between \(b_{\text{p}}\) and \(c_{\text{p}}\) with TSM, and ultimately upon the [PERSON] et al. (2001) [2] model derived from Mie theory calculations. By using independent datasets including in situ data and satellite retrievals, the proposed method is assessed, and then applied to Geostationary Ocean Color Imager (GOCI) data. The spatiotemporal distribution patterns of \(n_{\text{p}}\) are documented. At last, some necessary discussions are appended.
## 3 Parameter Design Method of TBM Tunnel ASRBs
### Evaluation of Rockburst Grade in Deeply Buried Hard Rock TBM Tunnels  
In rock engineering with rockburst risks, the evaluation of rockburst grade is crucial for designing support structures and risk prevention and control measures [46]. In the pre-construction phase of the project, assessing the rockburst grade can provide a reference for the resolution of the excavation method and hazard prevention measures [47]. Therefore,  
Figure 4: Flow chart of TBM tunnel ASRB parameter design method.  
before conducting the ASRB design scheme for TBM tunnels, rockburst grading along the tunnel should be determined based on the engineering geological conditions, geostress field conditions, and relevant construction design parameters of the project area, such as tunnel burial depth, excavation diameter, etc. With increasing research on the rockburst mechanism, many rockburst classification methods have been proposed [48]. According to the number of factors used in the theoretical and empirical criteria, the assessment methods of rockburst grade can be divided into single-factor and composite-factor criteria. Among these, single-factor theoretical criteria include the strength criterion ([PERSON] criterion [49], the Turchaninov criterion [50], the Russenes criterion [51], etc.) and the energy criterion (strain energy storage index [52], the stiffness criterion (brittleness coefficient [53], etc.). Composite-factor criteria include the empirical rockburst vulnerability index (RVI) [54] and excavation vulnerability potential (EVP) [55]. Table 5 lists the theoretical and empirical criteria that are commonly used to assess the rockburst grade.  
Based on the assessment methodology listed above, an assessment of the rock burst grade along the TBM tunnels can be carried out to determine the rockburst grade of the unexcavated tunnel sections. It is essential to carefully evaluate the implementation of ASRBs specifically for tunnel sections at high risk of intense or extremely intense rockbursts. The rationale behind this consideration stems from the potentially severe consequences associated with rockburst incidents in these tunnel sections. The occurrence of intense or extremely intense rockbursts in these sections has the potential to endanger the safety of TBM equipment and construction operations, and even destroy the TBM equipment, leading to significant casualties. This unfortunate scenario previously unfolded during the construction of the TBM-constructed drainage tunnel at the Jinping II Hydroelectric Power Station, where the TBM became buried and its main beam fractured, resulting in the loss of seven lives [56; 57].