esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 1 Introduction
### Methodology of Operational Deflection Shape (ODS) and Mode Shape  
The basic equation of motion for a vibration structure which was excited by a force vector can be expressed as  
\[\textbf{M}\textbf{x}^{{}^{\prime}}(t)+\textbf{C}\textbf{x}^{{}^{\prime}}(t)+ \textbf{K}\textbf{x}(t)\ =\ \textbf{f}(t) \tag{1}\]where vector \(\mathbf{x}(t)\) consists of the spatial coordinates describing the motion of the structure. The \(\mathbf{M},\mathbf{K},\mathbf{C}\) represent the global mass, stiffness, and damping matrices of the structure, respectively. The vector \(\mathbf{f}(t)\) is the excitation force. For the harmonic excitation force, the force function can be written as  
\[\mathbf{f}(t)\ =\ \mathbf{F}^{\mu\alpha t} \tag{2}\]  
Thus, the corresponding harmonic response of the system is expressed as  
\[\mathbf{x}(t)\ =\ \mathbf{X}^{\mu\alpha t} \tag{3}\]  
Substituting Equations (2) and (3) into Equation (1), the force response solution can be directly written as  
\[\mathbf{X}\ =\ (\mathbf{K}-\omega\mathbf{M}+i\omega\mathbf{C})^{-1}\mathbf{F}\ =\ \mathbf{H}(\omega)\mathbf{F} \tag{4}\]  
The frequency response matrix \(\mathbf{H}(\omega)\) can be derived from the modal parameters and a more explicit form of the force response solution may be expressed as follows:  
\[\mathbf{X}\ =\ \sum_{r\ =\ 1}^{N}\frac{\mathbf{\varphi}_{r}^{T}\mathbf{F}\mathbf{\varphi}_{r}}{ (\omega_{r}^{2}-\omega^{2}+i2\xi_{r}\omega_{r}\omega)} \tag{5}\]  
where \(\mathbf{\varphi}_{r},\omega_{r},\xi_{r}\) represent the mode shape, natural frequency, and modal damping ratio of the \(r^{th}\) mode, respectively. Equation (5) permits the calculation of one or more individual responses to an excitation of several simultaneous harmonic forces. The modal response \(\mathbf{X}\) is sometimes referred to as an operating deflection shape (ODS). It can be seen that the ODS is a function of both modal properties and the excitation forces and can be written as the combination of real and imaginary parts:  
\[\mathbf{X}\ =\ \mathbf{X}_{R}+i\mathbf{X}_{I} \tag{6}\]  
where \(\mathbf{X}_{R},\mathbf{X}_{I}\) are the real and imaginary parts of ODS. When a sinusoidal excitation frequency is equal to the \(r^{th}\) modal frequency of the system, the \(r^{th}\) mode shape is almost dominated the ODS. Therefore, in this case, the real part of ODS \(\mathbf{X}_{R}\) can be approximately treated as the \(r^{th}\) mode shape.
## 4 Experimental Results and Analysis
### Traffic Parameter Extraction Results
#### 4.1.4 OD Matrix Analysis  
The OD matrix comprehensively reflects travel demand distribution within the road network, serving as fundamental data for traffic planning and management. The two types of OD matrices generated in this study provide information at different levels:  
The basic OD matrix (19 \(\times\) 20 dimensions) focuses on flow exchanges between intersections. As shown in Figure 9, OD flow distribution is highly uneven, with several dominant OD pairs. These high-flow OD pairs typically correspond to important commuting corridors or connections between commercial and residential areas. The basic OD matrix (19\(\times\)20 dimensions) clearly demonstrates that OD flow distribution is highly uneven, with several dominant OD pairs constituting the primary traffic flows within the network. These high-flow corridors typically represent important commuting routes, with intersections 1, 9, and 17 serving as major traffic generation and attraction points that maintain strong connections with multiple other intersections. Identifying these key OD pairs holds significant importance for arterial coordination control design: green wave coordination should be prioritized for high-flow OD pairs.  
The detailed OD matrix incorporates OD point information, providing finer-grained origin-destination distributions. As partially shown in Figure 10, OD points at the network periphery handle substantial collection and distribution functions. This finding validates the necessity of adding OD points while also suggesting the need to consider special requirements of traffic entering and exiting the network in signal control design. The detailed OD matrix incorporates peripheral OD point information, revealing that boundary OD points handle substantial traffic collection and distribution functions. This finer-grained analysis provides essential input for designing signal coordination strategies that accommodate both internal circulation patterns and external access requirements at network entry/exit points.  
Figure 9: Basic OD matrix.  
Notably, the OD matrix contains a certain amount of same-point OD flows (identical origins and destinations). As mentioned earlier, the average flow from intersection 1 to intersection 1 reaches 13.1 vehicles. This phenomenon may have multiple explanations: vehicles conducting short-distance activities near the node before returning, data recording start and end times coincidentally falling within the node's range, or taxi cruising behavior in the area. Such special travel patterns require particular consideration in signal control optimization.
## 5 Simulation Results and Discussion
### Simulation Results  
The key simulation parameters for the SCN in ns-3 are summarized in Table 2.  
The network is designed and configured, and events are scheduled per the requirements referring to Tables 1 and 2. The network is simulated under three cases for effectively analysing the resiliency of the synchrophasor communication network, which will be discussed subsequently. Nevertheless, the network animation remains the same for all cases.
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 3 Design of Data Naming Mechanism
### ICN Forward Process  
LEO satellite nodes primarily enable access to terrestrial mobile terminals. The communication in LEO mega constellation is initiated by the consumer, i.e., the user terminal (UT), which exchanges two types of packets: interest and data [50]. The UT sends an interest with a content identifier to request the corresponding data. Two types of packets have a name for identifying the content. When data are requested, the UT puts the name of the required data into the interest and sends it to the network [51]. The satellite nodes use this name to forward the interest to the data producer, i.e., the content source server [51]. Once the interest reaches the satellite nodes that have the requested data in its cache, the satellite nodes return the data. The packet structure is shown in Figure 9[52].  
For performing the interest and data forwarding, each satellite node needs to maintain three functional components: forwarding information base (FIB), pending interest table  
Figure 9: Packet architecture.  
(PIT), and content store (CS) [53]. Moreover, the interest and data forwarding steps in a satellite node are described in Figure 10.  
Setting the ground station1 (GS\({}_{1}\)) as the interest requester and the ground station2 (GS\({}_{2}\)) as the data responder, the communication of GS\({}_{1}\) and GS\({}_{2}\) in LEO mega constellation operates according to the following steps.  
Step1: The GS\({}_{1}\) requests content through sending an interest with the name of the content.  
Step2: A satellite node receives an interest and checks whether a match data already exists in its local CS: if the responding data are found, then data sends back GS\({}_{1}\). Otherwise, the satellite node investigates if an interest with the same name in the PIT already exists; if so, there is no forwarding of the new interest. If a similar interest is not found in the PIT, the new interest is forwarded according to the FIB and cached in the PIT.  
Step3: When the GS\({}_{2}\) receives the interest, a data containing the requested content is sent back. By following the reverse path of interest, the data follows the traces left in the PIT of each satellite node.  
Step4: When the data arrives at a satellite node, the data are forwarded through ISL. Afterwards, the satellite node eliminates the entry from the PIT, and the data are cached in the satellite node.
# Sintering Behavior of a Six-Oxide Silicate Bioactive Glass for Scaffold Manufacturing
###### Abstract  
The intrinsic brittleness of bioactive glasses (BGs) is one of the main barriers to the widespread use of three-dimensional porous BG-derived bone grafts (scaffolds) in clinical practice. Among all the available strategies for improving the mechanical properties of BG-based scaffolds, strut densification upon sintering treatments at high temperatures represents a relatively easy approach, but its implementation might lead to undesired and poorly predictable decrease in porosity, mass transport properties and bioactivity resulting from densification and devitrification phenomena occurring in the material upon heating. The aim of the present work was to investigate the sinter-crystallization of a highly bioactive SiO\({}_{2}\)-P\({}_{2}\)O\({}_{5}\)-CaO-MgO-Na\({}_{2}\)O-K\({}_{2}\)O glass (47.5B composition) in reference to its suitability for the fabrication of bonelike foams. The thermal behavior of 47.5B glass particles was investigated upon sintering at different temperatures in the range of 600-850 \({}^{\circ}\)C by means of combined thermal analyses (differential thermal analysis (DTA) and hot-stage microscopy (HSM)). Then, XRD measurements were carried out to identify crystalline phases developed upon sintering. Finally, porous scaffolds were produced by a foam replica method in order to evaluate the effect of the sintering temperature on the mechanical properties under compression loading conditions. Assessing a relationship between mechanical properties and sintering temperature, or in other words between scaffold performance and fabrication process, is a key step towards the rationale design of optimized scaffolds for tissue repair.  
bioactive glass; sintering; scaffold; bone tissue engineering; mechanical properties; bioactivity +
Footnote †: journal: Applied Physics Letters
## 3 Results and Analysis
### Weld Bead Shape and Porosity  
The weld beads are shown in Figure 6a. It is found that the weld beads are perfectly formed in each thermal frequency without spatter, undercut, and collapsing. The typical ripples are formed on the front surface of weld bead. The distance between the adjacent ripples becomes wider, when the thermal frequency is lower, therefore, the shape of the weld bead becomes successively smoother in the longitudinal section as the pulse frequency is increased from 3 Hz to 5 Hz.  
Figure 5: Transient input energy and transient resistance waveforms of DP-GMAW: (**a**) 3 Hz; (**b**) 4 Hz; and, (**c**) 5 Hz.  
Figure 6: Weld bead shape and porosity: (**a**) weld bead shape; and, (**b**) distribution of porosity on longitudinal section.  
In the solidification process of welding pool, the gas dissolving in liquid metal escapes from the molten pool through three stages of nucleation, growth, and floating. If the solidification rate of the molten pool is faster than the gas rate of nucleation, growth, and rising, then the weld bead will have residual pores. Distribution of porosity on longitudinal section weld bead processed at different thermal frequencies are shown in Figure 6b. Macrostructure details and porosity of weld beads are presented in Table 3. It is observed that the welding parameters for different thermal frequencies could gain access to a full penetration of 6061-T6 welds with no crack. There will be more porosity being contained in the weld bead when the thermal frequency becomes lower. It reveals that the higher thermal frequency can improve the fluidity of the molten pool, which is beneficial to the gas escaping. Further analysis shows that the thermal frequency of 5 Hz has a result in a higher W/D ratio than the thermal frequency of 3 Hz and 4 Hz due to the strong stirring effect. A higher W/D ratio means a better escapement of gas, which is in agreement with the report by [PERSON] (2018).
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Conventional Semi-Bridgeless Boost Converter with Soft Switching  
Soft switching is performed by adding a snubber circuit composed of a switch \(Q_{3}\), an inductor \(L_{3}\), and a capacitor \(C_{1}\) as shown in Figure 8. Basically, the snubber circuit allows for reducing switching losses from SBBC [92]. In addition, resonant circuits can be coupled for SBBC topologies as suggested in [93; 94].
## 3 Results and Discussion
### In Vivo Experiment
#### 3.3.3 Effect of Photosensitization on Maize Colour
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & **L* & **a* & **b* & **C** & \(\Delta\)E \\ \hline P\({}^{\text{O}}\)\({}^{\text{o}}\) white & 81.9 \(\pm\) 0.5 \({}^{\text{a}}\) & \(-\)0.7 \(\pm\) 0.3 \({}^{\text{a}}\) & 29.4 \(\pm\) 0.9 \({}^{\text{b}}\) & 29.4 \(\pm\) 0.9 \({}^{\text{b}}\) & - \\ P\({}^{+}\)L\({}^{\text{o}}\) white & 81.7 \(\pm\) 0.8 \({}^{\text{a}}\) & \(-\)6.5 \(\pm\) 0.4 \({}^{\text{b}}\) & 50.6 \(\pm\) 1.8 \({}^{\text{a}}\) & 51.0 \(\pm\) 1.7 \({}^{\text{a}}\) & 21.9 \({}^{\text{a}}\) \\ P\({}^{+}\)L\({}^{\text{+}}\) white & 79.8 \(\pm\) 0.5 \({}^{\text{a}}\) & 0.1 \(\pm\) 0.4 \({}^{\text{a}}\) & 26.8 \(\pm\) 0.7 \({}^{\text{b}}\) & 26.8 \(\pm\) 0.7 \({}^{\text{b}}\) & 3.5 \({}^{\text{b}}\) \\ \hline P\({}^{\text{O}}\)\({}^{\text{o}}\) yellow & 74.8 \(\pm\) 0.5 \({}^{\text{a}}\) & 6.8 \(\pm\) 0.7 \({}^{\text{ab}}\) & 39.8 \(\pm\) 2.5 \({}^{\text{b}}\) & 40.3 \(\pm\) 2.5 \({}^{\text{b}}\) & - \\ P\({}^{+}\)L\({}^{\text{o}}\) yellow & 77.6 \(\pm\) 1.3 \({}^{\text{a}}\) & 4.4 \(\pm\) 0.9 \({}^{\text{b}}\) & 54.1 \(\pm\) 1.9 \({}^{\text{a}}\) & 54.2 \(\pm\) 1.9 \({}^{\text{a}}\) & 14.7 \({}^{\text{a}}\) \\ P\({}^{+}\)L\({}^{\text{+}}\) yellow & 77.2 \(\pm\) 0.5 \({}^{\text{a}}\) & 7.5 \(\pm\) 0.6 \({}^{\text{a}}\) & 37.6 \(\pm\) 2.4 \({}^{\text{b}}\) & 38.3 \(\pm\) 2.3 \({}^{\text{b}}\) & 3.3 \({}^{\text{b}}\) \\ \hline P\({}^{\text{O}}\)\({}^{\text{O}}\) popcorn & 74.1 \(\pm\) 0.6 \({}^{\text{a}}\) & 9.8 \(\pm\) 0.9 \({}^{\text{a}}\) & 28.5 \(\pm\) 1.3 \({}^{\text{a}}\) & 30.2 \(\pm\) 1.4 \({}^{\text{b}}\) & - \\ P\({}^{+}\)L\({}^{\text{o}}\) popcorn & 77.4 \(\pm\) 0.9 \({}^{\text{a}}\) & 5.8 \(\pm\) 1.0 \({}^{\text{b}}\) & 34.9 \(\pm\) 1.8 \({}^{\text{a}}\) & 35.3 \(\pm\) 1.7 \({}^{\text{a}}\) & 8.7 \({}^{\text{a}}\) \\ P\({}^{+}\)L\({}^{\text{+}}\) popcorn & 76.0 \(\pm\) 0.7 \({}^{\text{a}}\) & 8.7 \(\pm\) 0.7 \({}^{\text{a}}\) & 25.4 \(\pm\) 1.9 \({}^{\text{a}}\) & 26.9 \(\pm\) 1.8 \({}^{\text{b}}\) & 3.8 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}  
\({}^{\text{P}}\)\({}^{\text{O}}\)\({}^{\text{o}}\) = control; P\({}^{\text{O}}\)\({}^{\text{L}}\)\({}^{\text{o}}\) = curcumin treatment; P\({}^{\text{O}}\)\({}^{\text{L}}\)\({}^{\text{o}}\) = photosensitization; C = chroma; data are means \(\pm\) SE (\(n=3\)); data with different letters in the same column and maize variety are significantly different (\(p<0.05\)). The comparison of means was performed within the same variety for different treatments, as the aim was to assess if different treatments result (or not) in colour changes on the surface of the maize kernel.  
\end{table} 
Table 1: Colour parameters in maize kernels submitted to different treatments.
## 3 Experiments and Analysis
### Experimental Evaluation Indicators
#### 3.2.2 Fruits Tracking Experiment  
Fruit ID switch rate (\(IDSR\)), multiple object tracking accuracy (\(MOTA\)), and multiple object tracking precision (\(MOTP\)) are used to evaluate the tracking algorithm performance.  
Figure 4: Introduction of drawing lines counting strategy.  
Fruit ID switching rate refers to the ratio of the fruits whose IDs have changed to all counted fruits in the video, and the smaller value means better tracking performance. Multi-target tracking accuracy indicates the performance of the tracking algorithm in maintaining the tracking trajectory and the larger value means the better tracking performance, as shown in Equation (9). Multi-target tracking accuracy refers to the success rate of matching all tracked targets; the higher the accuracy, the better the performance of the algorithm, as shown in Equation (10).  
\[MOTA=\
icefrac{{M}}{{S}} \tag{9}\]  
\[MOTP=\
icefrac{{M}}{{T}} \tag{10}\]  
where \(S\) denotes the total number of fruits tracked in the video, i.e., the number of algorithm counts; M denotes the number of fruits tracked and matched correctly in the video; and T denotes the total number of fruits tracked and matched in the video, i.e., the number of detections.
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## 2 Materials and Methods
### GNSS RTK Positioning System and Differential GPS  
To obtain the precise positioning coordinates, this system decodes and operates the satellite data then transmits the data to the server via Wi-Fi. This system is a real-time dynamic satellite positioning system. It uses two GNSS receivers capable of receiving GPS, Global Navigation Satellite System (GLONASS), and Beidou satellite data. One is used as a reference station and the other is used as a monitoring station. The satellite data of the two stations are sent back to the server. After the device is processed by the differential GPS technology, it can instantly obtain the positioning coordinates of the monitoring station accurately to the centimeter level. The three main architectures of this system include a fixed reference station, a movable monitoring station, and a server program for calculating the positioning coordinates. The satellite data can be stored in an SD card inside the black box.  
\[\rho=D+c\cdot(dt-dT)+d_{\text{ion}}+d_{\text{trop}}+\
u_{\rho} \tag{1}\]  
\[\Delta P=\rho+c\cdot\Delta dT+\Delta d_{\text{ion}}+\Delta d_{\text{trop}}+\varepsilon \tag{2}\]  
\[\
abla P=\
abla\rho+c\cdot\
abla dt+\
abla d_{\text{ion}}+\
abla d_{\text{trop }}+\varepsilon \tag{3}\]  
\[\begin{array}{rl}\
abla\Delta P&=(\Delta P)_{\text{satellite}_{2}}-(\Delta P )_{\text{satellite}_{1}}+\varepsilon\\ &=\
abla\Delta\rho+\
abla\Delta d_{\text{ion}}+\
abla\Delta d_{\text{trop}}+ \varepsilon\end{array} \tag{4}\]  
The difference result of the GNSS RTK positioning system receiver can be calculated by the above Equations (1)-(4). Equation (2) expresses the single difference between receivers. Equation (3) expresses the single difference between satellites. At time \(t\), the error from satellite clock \(dt\) and receiver clock errors \(dT\) could be canceled as in (4). Because the distance between base and robot is short (less than 100 km), the localized atmospheric errors can be reduced, and more accurate location can be obtained. The positioning system transmits location and direction from the receiver to the computer via a USB connection. In Equations (1)-(4), \(\rho\) is the virtual distance between a satellite and receiver, \(D\) is real distance between a satellite and receiver, \(\
abla\Delta\) is a double difference (receiver-satellite), \(d_{\text{ion}}\) is the ionosphere correction(the delay error caused by ionosphere), \(d_{\text{trop}}\) is the tropospheric  
Figure 1: (a) The omnidirectional WMR with webcam, laser sensor, ultrasonic sensors, GNSS RTK positioning system, StarGazer positioning system, and notebook; (b) the GNSS antenna and the GNSS receiver [8].  
correction (the delay error caused by the troposphere), \(\varepsilon\) is the measurement noise, \(c\) is the speed of light, \(dt\) is the delay of GPS satellite clock relative to GPS time, \(dT\) is the delay of receiver's clock relative to GPS time, and \(\
u_{\rho}\) is the random error between receiver and satellite.
# An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
## 3 The Proposed 5G Procedure
### Data Delivery Procedure
#### 3.2.1 Mobile Host to Internet  
In the case of data delivery between the mobile host and the Internet, as shown in Figure 4, the U.E. sends packets to eNB, eNB encapsulates the packets and forwards them to M-GW through a GTP tunnel. When M-GW receives packets, it checks the destination IP address against the routing table and makes an optimal routing decision to the Internet. This simplified procedure eliminates exchanging control messages between S-GW and P-GW. Furthermore, Our proposal simplifies S5/S8 tunnel establishment. Hence, it improves signaling and total time transmission.
## 2 Methodology  
The proposed methodology presents a structured framework for designing thinned wideband antenna arrays based on Eisenstein fractal geometry that utilizes the self-similarity properties of the geometry in combination with an optimization process guided by evolutionary optimization techniques. The primary objective is to minimize the number of active elements while preserving high performance characterized, by low sidelobe levels and stable directivity across a wide frequency range.
## 2 FRCCs-Based Electrical Impedance Acquisition System and Crack Localization Algorithm
### Instantaneous Multi-Channel Electrical Impedance Acquisition System  
Figure 1 shows the working principle of the instantaneous multi-channel electrical impedance acquisition system. The developed system is composed of the control computer, an arbitrary waveform generator (AWG), digitizer, multiplexer and self-sensing circuit. The developed system is programed and controlled by LabVIEW installed in the control computer. The working procedure is as follows. First, control computer sends the trigger signals to AWG and digitizer. Second, AWG generates an AC input with a tailored waveform and the corresponding electrical current propagates along the manufactured FRCCs. Third, the propagated current is measured by the digitizer through the self-sensing circuit and send to control computer. Finally, the process from the first step to the third step is repeated with respect to the multi-sensing regions using the multiplexer. Since the FRCCs specimen is acting as a semi-conductive component due to combined fibers, the driving voltage is required above a certain level. Here, the self-sensing circuit which acts as a charge amplifier is needed to simultaneously excite and sense the small voltage in FRCCs [45]. The self-sensing circuit based on voltage divider can measure the voltage of FRCCs using Equation (1):  
\[v_{c}(t)=\ -\frac{C_{r}}{C_{c}}v_{0}(t)-v_{i}(t), \tag{1}\]  
where \(v_{c}(t)\) is generated from the electrical response over time (\(t\)). \(C_{r}\) and \(C_{c}\) represent the reference capacitor of the self-sensing circuit and the capacitance values of FRCCs respectively. \(v_{0}\) and \(v_{i}\) are the output and input voltages as shown in Figure 1.  
Figure 1: Schematic of the instantaneous multi-channel electrical impedance acquisition system: AWG is an arbitrary waveform generator. \(v_{o}\), \(v_{i}\) and \(v_{c}\) are the voltages of output, input and FRCCs, respectively. \(C_{c}\) and \(C_{r}\) are the capacitances of FRCCs and the reference capacitor.