esa-sceva/satcom-chunk-collection huggingface dataset:
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 5 Validation of the Theoretical Model
### Model Development and Numerical Simulation
This is because the work performed by external forces is entirely stored as elastic strain energy during the elastic phase and converted into both elastic strain energy and dissipative energy during yielding. Equation (25) in Figure 10a employs the simulated stress-strain curve values for theoretical calculations. The dissipated energy of the rock under various confining pressures aligns with the theoretical predictions, thereby validating that the theoretical elastoplastic constitutive model accurately represents its energy dissipation behavior. Similarly, Figures 9 and 10b indicate that during the elastic phase (\(\gamma_{p}=0\)), \(\varepsilon_{V}^{c}=0\), and \(\varepsilon_{V}\) overlaps with \(\varepsilon_{V}^{e}\). During yielding, \(\varepsilon_{V}^{c}=\varepsilon_{V}-\varepsilon_{V}^{e}\). This is because volumetric strain transforms entirely into elastic volumetric strain during the elastic phase and into both elastic and crack volumetric strains during yielding. The initial values of \(\varepsilon_{V}\) and \(\varepsilon_{V}^{e}\) in Figure 9 are set to zero by subtracting the elastic volumetric strain during the confining pressure loading phase. In Figure 10b, the overlap of \(\varepsilon_{V}^{c}\) at various confining pressures occurs because Section 3.2 theoretically derives that \(\varepsilon_{V}^{c}\) can be indirectly quantified using \(\psi\), which is developed based on \(\psi(\gamma_{p})\) in Equation (46). Thus, at the same \(\gamma_{p}\), the simulated values of \(\psi\) and \(\varepsilon_{V}^{c}\) remain unchanged with varying confining pressures, further validating the theoretical rationale. Equation (29) in Figure 10b also utilizes simulated stress-strain curve values for theoretical calculations. The volume strain of the cracks in the rock under various confining pressures is consistent with the theoretical predictions, thereby validating that the theoretical elastoplastic constitutive model effectively characterizes crack evolution. Figure 8: Simulated relationship of stress–strain curves. Figure 9: Simulated relationships of energy evolution and crack development under different confining pressures: (**a**) \(\sigma_{3}=1\) MPa; (**b**) \(\sigma_{3}=2\) MPa; (**c**) \(\sigma_{3}=3\) MPa; (**d**) \(\sigma_{3}=4\) MPa; (**e**) \(\sigma_{3}=5\) MPa.
## 4 Implementation Results and Comparisons
### Comparison to State of the Art  
There are certain limitations when providing a realistic comparison to the state of the art. For example, the comparison to multipliers, published in [14; 15; 16], is not possible because these architectures are implemented in a bit-serial fashion, while we employed a digit-parallel implementation style. Additionally, the implementations of these designs are provided on different ASIC technologies, while we reported results on a 28 nm FPGA technology (Virtex-7). Similarly, the comparison to [25] could be more challenging as the complexity of multiplier design is offered in mathematical formulations rather than providing the actual FPGA implementation. The comparison to some digit-parallel multipliers, reported in [28; 29], is also not possible as these are used for point multiplication computation in ECC without providing the area and timing details of the utilized multipliers. It is important to note that we have presented our comparison with only the most relevant bit-parallel state-of-the-art multiplication architectures in Table 2. Moreover, we have also synthesized our multiplication circuits for the FPGAs that are utilized in state-of-the-art implementations. Therefore, column one in Table 2 provides the reference design. The name of the corresponding multiplier is given in column two. The utilized binary field is shown in column three. Column four provides the implementation device. The area utilizations (slices) are provided in column five. Columns six to eight present the timing information in terms of clock cycles (CCs), clock frequency and latency. Column nine presents the power consumption of the multiplier architectures. Finally, we have used a symbol (-) in Table 2, where the relevant information is not provided.
## 2 Data and Methods
### RBSP Satellites  
The dual-spacecraft Radiation Belt Storm Probes (RBSPs) move along highly elliptical orbits (extending from 1.2 to 5.8 \(R_{E}\)) at an inclination of 10\({}^{\circ}\), thus offering a non-ionospheric point of view due to direct penetration of the radiation belts.  
The Relativistic Electron-Proton Telescope (REPT) on board the satellites of the RBSP class measures electrons in differential bins in the energy range \(\sim\)1-20 MeV with high detection efficiency above 5 MeV. A background partly due to galactic cosmic rays primarily afflicts the REPT measurements in the highest electron channels [38].
# Effect of Nylon Fiber Addition on the Performance of Recycled Aggregate Concrete
## 2 Experimental Section  
### Materials  
In this study, ordinary Portland cement conforming to ASTM C150 was used in preparing the concrete specimens. The cement had been produced by a local cement plant in South Korea. The density and specific surface area of the cement used were 3.15 g/cm\({}^{3}\) and 328 m\({}^{2}\)/kg, respectively. The mineralogical compounds of the cement were 54.9, 16.6, 10.3, and 9.1% for C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A and C\({}_{4}\)AF, respectively.  
Crushed stone aggregate (CA) and recycled coarse aggregate (RA) were used as coarse aggregates for the concrete production. RA was produced by crushing the waste concrete with a jaw crusher and an impact crusher. To enhance the purity of RA, impurities such as wood, bricks and glass were manually removed. A water jet with high-pressure was also used to remove mud and debris from RA. Both CA and RA have continuous grains from 5 to 25 mm. The main properties of coarse aggregates are presented in Table 1. The size grading of CA and RA is shown in Figure 1. Natural river sand was used as a fine aggregate with a fineness modulus of 2.80, a water absorption of 0.98% and a density of 2.65 g/cm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Crushed Stone Aggregate (CA)** & **Recycled Coarse Aggregate (RA)** \\ \hline Density (g/cm\({}^{3}\)) & 2.64 & 2.37 \\ F.M. & 7.17 & 7.43 \\ Absorption (\%) & 0.66 & 4.31 \\ Abrasion rate (\%) & 21.7 & 47.5 \\ Adhered mortar (\%) \({}^{1}\) & 5.56 \\ \hline \hline \end{tabular} \({}^{1}\) Acid-soluble content.  
\end{table}
Table 1: Physical properties of the coarse aggregates.  
The nylon fibers (NF) used in the present study, a picture of which is shown in Figure 2, had been supplied by a local fiber company in South Korea, and they are now commercially available in the domestic market. The properties of NF used in the present study are shown in Table 2. In addition, a polycarbonate-based superplasticizer (SP) was used to improve the initially low workability of the fresh concrete. The basic properties of SP were provided by a chemical manufacturer in South Korea.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 3 Mathematical Analysis
### Estimation for a Single Building
#### 3.1.2 When Both SBSs and CGC Stations Are Collocated  
Let \(M_{\text{SPS}}\) denote the number of RBs in the satellite spectrum. Assume that each SBS can serve the maximum of \(U_{\text{F,max}}\) UEs so that \(u\in\left\{1,2,\ldots,U_{\text{F,max}}\right\}\). Hence, by employing the ABS based eICIC technique to address co-channel interference between a SBS and a CGC station, the aggregate capacity of the small cell UEs for any SBS (i.e., \(\sigma_{\text{s}}:s\in\left\{1,2,\ldots,S_{\text{F}}\right\}\), in \(\text{$n_{\text{non-ABS}}$}\in\mathbf{T}\backslash\mathbf{T_{\text{ABS}}}\) is given by  
\[\sigma_{\text{s,sb}}=\sum_{t=t_{\text{non-ABS}}\in\mathbf{T}\backslash\mathbf{T_{ \text{ABS}}}}\ \sum\
olimits_{i=1}^{M_{\text{SPS}}}\sum\
olimits_{u=1}^{U_{\text{F,max}}} \sigma_{t,i,u}\Big{(}\ \rho_{t,i,u}\Big{)} \tag{4}\]  
where \(\sigma\) and \(\rho\) are responses over \(M_{\text{SPS}}\) RBs of \(u\in\left\{1,2,\ldots,U_{\text{F,max}}\right\}\) UEs for any SBS. If each SBS serves the maximum of one UE (i.e., \(U_{\text{FU,max}}=1\)), then Equation (4) is given by  
\[\sigma_{\text{s,sb,1}}=\sum_{t=t_{\text{non-ABS}}\in\mathbf{T}\backslash\mathbf{T_{ \text{ABS}}}}\ \sum\
olimits_{i=1}^{M_{\text{SPS}}}\sigma_{t,i}\Big{(}\rho_{t,i}\Big{)}. \tag{5}\]  
If each SBS has exactly one small cell UE, and all SBSs are serving simultaneously in, \(t_{\text{non-ABS}}\in\mathbf{T}\backslash\mathbf{T_{\text{ABS}}}\) the aggregate capacity per 3D building is then given by  
\[\sigma_{\text{cl,sc}}^{\text{SBS,CGC}}=\sum\
olimits_{s=1}^{S_{\text{F}}} \sigma_{\text{s,sb,1}} \tag{6}\]
## 4 Experimentation and Results
### Experiment System  
#### 4.2.1 Platform  
The YOLOv5 framework integrated in Python, a high-performance workstation with an NVIDIA GPU, and the DeepSORT library for tracking were among the hardware and software utilized in the experiment. This configuration guarantees real-time product and consumer tracking, detection, and the effective processing of high-resolution images in retail settings.
## 3 Case Studies and Analysis
### Test Results and Analysis for PFG at SCP3
#### 3.4.2 Test Results and Analysis in Three-Dimensional Spaces  
According to Equation (7) and Figure 8, the spatial surfaces of the PFG at SCP3 in spaces have vastly different shapes than the other SCPs. The surfaces by Reg.1 and Reg.2 modes all present non-uniform dome shapes humping upwards, and the surface areas by the two regulation modes are similar, and are all larger than those at SCP1 and SCP2, indicating the PFG to CIV1 at SCP3 have nearly the same regulation efficiencies which are larger than SCP1 and SCP2.  
According to Equations (8) and (5) and Figure 9, the spatial surfaces of PFG to CIV2 at SCP3 are similar to those at SCP2, and the primary difference lies in that the spatial surfaces at SCP3 by Reg.2 have larger curvatures and areas than those at SCP2, indicating the OSIV of UPFC at SCP2 has enough large efficiencies to regulate the power flows at other SCP3. Another difference is that the spatial surfaces at SCP3 in the severe operation condition display non-closed loops of the projections on the P-Q planes.  
Figure 8: Spatial surfaces of PFG to CIV1 at SCP3 in spaces: (**a–d**) Spatial surfaces of PFG to CIV1 by Reg.1 mode of OSIV in four operation conditions; (**e–h**) Spatial surfaces of PFG to CIV1 by Reg.2 mode of OSIV in four operation conditions.  
Figure 9: Spatial surfaces of PFG to CIV2 at SCP3 in spaces: (**a–d**) Spatial surfaces of PFG to CIV2 by Reg.1 of OSIV in four operation conditions; (**e–h**) Spatial surfaces of PFG to CIV2 by Reg.2 of OSIV in four operation conditions.  
The spatial surfaces of PFG to CIV3 at SCP3 in four regulation scenarios are shown in Figure 10. The prominent features are that the spatial surfaces have two funnel peaks, one humps upwards and the other one humps downwards, indicating that the PFG to CIV3 at SCP3 can have inverse varying tendencies within different regulation regions, this feature is more beneficial to occupy larger surface areas and adaptive to different regulation requirements.
# A Miniature Flapping Mechanism Using an Origami-Based Spherical Six-Bar Pattern
## 2 Design
### Flapping Mechanism  
One important point is that only linear motion should be transmitted to generate the flapping motion of the wings. To make this possible, one end of the spherical six-bar linkage is fixed at the body frame as shown in Figure 2g. This constraint filters the rotating motion of the motor and transmits the linearly reciprocating motion only, which is shown in Video S1. By using this working principle, a successful flapping motion can be achieved without mechanical parts that cause friction problems.  
To generate the thrust in the end, a wing rotating motion is necessarily required. To achieve the wing rotating motion, passive hinges are employed as shown in Figure 2c. The passive joints are positioned between the output of the transmission and the wings. The passive hinges are parallel to the spanwise direction and have a joint-stop to prevent excessive rotation. Overall, the system has three degrees of freedom--flapping actuation and two passive hinges for wing rotation.  
Flapping motion is achieved by the constrained spherical six-bar linkage shown in Figure 3, which is the key enabling technology of the proposed mechanism. Generally, the spherical six-bar linkage has complex structures due to many joints, links, and axes. The spherical six-bar linkage shown in Figure 2 is designed using an origami-based pattern and fabricated through a layer-based method: smart composite microstructures (SCM). This process provides lightweight structures by replacing pin joints with flexures and finally enables us to achieve spherical six-bar linkage by folding a paper just three times.  
Figure 3 shows the pattern for the origami-based spherical six-bar linkage. All axes of the flexures pass through a single point and rotate around the point. The spherical six-bar array is installed between the wing and the crank as shown in Figure 2. The spherical linkage acts as a mechanical filter, which transmits only linear motion to the wings. This  
Figure 2: Snapshots of the moving process of the transmission. From (**a**–**f**), the mechanism shows upstroke. From (**g**–**l**), downstroke is shown.  
is possible since one end of the linkage is constrained. The motor and the cranks make the rotating motion. When this rotating motion passes through the spherical linkage, the horizontal motion is absorbed by the spherical linkage but the vertical motion is transmitted to the wing.  
Depending on how we choose to constrain the direction of the axis, the direction of the output motion can be differed. In our case, we need a flapping motion vertical to the motor's axis. Therefore, the axis constrained has been determined as a direction perpendicular to the span wise direction, which is equal to the direction of the motor's axis. Figure 2 shows how the constrained spherical six-bar works.
## 5 Description of the Study Area  
The mathematical formulation and optimization algorithms are applied to a real study area in Petaling Jaya (PJ), Kuala Lumpur, Malaysia. PJ is a major Malaysian city originally developed as a satellite township in Kuala Lumpur. The objective was to minimize the total cost of the feeder bus network in the area. The case study region, shown in Figure 3, is an area of 5.5 km by 6.5 km in the south of PJ in Malaysia and includes the Kelana Jaya Line of the Kuala Lumpur LRT. There are four stations in the study region. The existing bus stops in each traffic zone are considered feeder bus stops covered by a feeder line. A certain amount of demand corresponding to the traffic zone is assigned to each of the bus stops in that particular zone.  
Figure 3: The case study regions for the Petaling Jaya (PJ) (extracted from Google Maps, 2014).
## 2 Theory Background and Methodology
### Compressed ESM Based on the Equivalent Redundant Dictionary  
#### 2.2.1 The Equivalent Dictionary Under the Sparse Assumption  
When the actual sound sources are distributed sparsely, the source strengths \(q\) often have a sparse representation in some basis. In this condition, the equivalent source strengths can be expressed in the following form, as seen in Equation (6) [21]:  
\[\mathbf{q}=\mathbf{I}\mathbf{\alpha} \tag{6}\]  
where \(\mathbf{I}\) is the identity matrix, which can be regarded as the sparse basis of equivalent source strengths. \(\mathbf{\alpha}\) is the coefficient vector of the basis.  
Then, the \(l_{2}\) norm in the penalty term of Equation (4) can be replaced by a \(l_{1}\) norm, i.e., see Equation (7):  
\[\underset{\mathbf{\alpha}}{\mathrm{argmin}}\mathbf{p}-\mathbf{A}\mathbf{I}\mathbf{\alpha}_{2}^{2} +\lambda\mathbf{\alpha}_{1} \tag{7}\]  
Once the coefficient \(\mathbf{\alpha}\) is solved, the sound pressure on the reconstruction plane can be obtained by the following formula of Equation (8):  
\[\mathbf{P}_{R}=\mathbf{G}\mathbf{q}=\mathbf{G}\mathbf{I}\mathbf{\alpha}=\mathbf{\phi}\mathbf{\alpha} \tag{8}\]  
where \(\mathbf{\phi}=\mathbf{G}\mathbf{I}\) denotes an equivalent dictionary.
## 3 Results and Discussion
### Comparison and Evaluation of Prototype DPIs
#### 3.2.1 Comparison of Maximum Injection Pressure Results  
The maximum injection pressure with the representative Types 1, 2, and 3, is shown in Figure 14. The Type 3 DPI showed the highest injection pressure at 180 MPa. It was found that the hydraulic application required to build up needle movement is an essential element to obtain an injection performance at high pressures, over 130 MPa. As a result, it can be said that the Type 3 DPI prototype has a similar injection performance to the conventional type DPI, and it has a better flexibility for designing the internal structure of a direct needle-driven piezo-injector.
## Materials and Methods
### Instrumental Setup  
The SPME fibers selected for the collection of BVOCs were divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS-50/30 \(\upmu\)m layer, Supelco, Sigma-Aldrich Co., Darmstadt, Germany). Each SPME fiber was held in the middle of a 17 cm long aluminum cylinder, perpendicularly connected to a smaller vertical cylinder 7 cm long acting as a support. In Figure 6, a blueprint (I) and pictures (II, III, IV) of the dynamic sampling system for BVOC collection are represented. Observing panel I on the top, the DBSS is constituted by a vertical tube (A), a metal net (B) connected to the main cylinder (C) both in aluminum, and a small fan (D). The diameter of the cylinder was chosen to allow a proper fiber exposition. One face of the cylinder (external diameter 5 cm and internal diameter 4 cm) is closed by a metal net (B) to protect the fiber from debris and to create a swirling flow, while the other face is closed by a small fan (Jamicon(r), Kaimei Electronic Corp., New Taipei City, Taiwan, 12 V, 6200 rpm, 13 m\({}^{3}\)/h, 40 \(\times\) 40 \(\times\) 20 mm, panel IV) powered by a lead-acid battery (Join(r), Alpha Elettronica S.r.l., Collecchio, Italy, 12 V, 4.5 AH). The instrument aluminum body is designed to allow an easier sampling and movement in the forest and is, therefore, light and resistant; in addition, to connect the single parts of the instrument, 4 long screws were used instead of glue to allow the utilization under high-temperature conditions and avoid any interferences during the sampling.  
Figure 6: In **panel I**, the schematic drawing of the dynamic sampling system, adapted and modified by the BVOC system developed by [PERSON] et al. [36], is reported. The SPME fiber was firmly positioned in the appropriate vertical tube (**A**) by four small magnets. A metal net (**B**) was installed at one end of the cylinder (**C**) to protect the fiber, while at the opposite end, a fan (**D**) was positioned to create a swirling flow. All parts are linked together by screws. In **panel II**, a picture of the whole device is reported, while in **panel III** and **IV**, the details of the net located in the front of the device and the fan set on the back are shown.  
After sampling, the fibers were placed in a special tray, within a hermetic case, with dedicated Teflon pressure supports to seal the needle until the fiber was transported to the laboratory, and all the fibers were subsequently desorbed within the same day, avoiding any degradation of compounds adsorbed into the SPMEs.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Resistance Capability of ECSSP-SC against Attacks
#### 6.2.7. Resistance to De-Synchronization Attacks  
It has been mentioned that in ECSSP-SC, the password \(PWD\) of the \(USR\) is updated synchronously with the service provider and the smart card, and there is no other synchronous updating of any other information. Thus, ECSSP-SC also ensures resistance to de-synchronization attacks.  
Figure 8. Resistance to server impersonation in ECSSP-SC.
## 2 Experimental Descriptions
### Cryostat Design
#### 2.1.1 Temperature Distribution and Heat Leakage Calculations  
To minimize heat leakage at 4 K, we employed COMSOL Multiphysics(r) (COMSOL Co., Ltd., Beijing, China) to simulate the temperature distribution and heat leakage of the cryostat. We conducted a temperature simulation of the two thermal shields. Although the surfaces of the thermal shields were polished, they had oxidized at the time of installation, so we set the emissivity of the surfaces without MLI to 0.2, and that of the surface with MLI to 0.005. The RRR of Cu used in the simulation is 100. The variation in thermal conductivity and cooling capacity with temperature was taken into account in the simulation. Figure 2 presents the simulation results of the temperature distribution of the two thermal shields. The outer and inner thermal shields operated at approximately 50 K and 7 K, respectively, with temperature differences of less than 6 K and 1 K across each shield. The radiant heat leakage of the two thermal shields was 42.407 W and 0.393 W. Since the top plate of the outer shield was not covered with MLI, the radiant heat leakage contributed about 70% of the total radiant heat leakage. Table 1 summarizes the heat leakage in each temperature zone of the cryostat. The total heat leakage at 4 K was estimated to be less than 0.02 W.  
Figure 1: Cryostat model drawing and installation process.  
Figure 2: Temperature distribution at steady state for two thermal shields obtained by simulation: (**a**) outer shield and (**b**) inner shield.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 3 Sensor Operating Modes and Energy Consumption Measurements
### Consumption Profiles in Active and Idle Mode
#### 3.3.3 ECG Sampling Event  
The ECG sampling is performed in equidistant ECG sampling events. The ECG sensor uses a 32.768 kHz RTC (real-time clock) quartz oscillator as a high-precision reference clock to minimize the jitter. The internal timer is set to regular interrupt intervals in accordance with the ECG sampling frequency. At each interrupt, the MCU is awakened from sleep mode and analog-to-digital (A/D) conversion is started. When the A/D conversion is finished, the ADC triggers an interrupt routine, where a fresh A/D sample is added to the existing ECG packet buffer, and then the MCU returns to sleep if the buffer is not full. If the buffer is full, then MCU transfers the ECG packet buffer to the radio for transmission  
Figure 4: Current consumption profiles of BLE connection events for different TX power settings.  
Figure 5: Current consumption profile of a BLE message-processing event.  
before returning to sleep. An example of an ECG sampling event is presented in Figure 6. The ECG sampling event begins with the wake-up triggered by the ECG sampling timer, followed by the ECG sampling procedure, and ends with the processing of the ECG sample and the transition back to sleep mode.  
The energy consumption of an ECG sampling event \(e_{sample}\) is calculated as a sum of current consumption profile samples \(i_{t}\) multiplied by a sampling interval \(\Delta t\) and battery voltage \(V_{batt}\) (Equation (1)). The measured mean energy consumption per ECG sampling event is 2.78 \(\upmu\)J with 0.03 \(\upmu\)J standard deviation.  
\[e_{sample}=V_{batt}\sum_{t=0}^{T}i_{t}\times\Delta t \tag{1}\]