esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, the maximum displacement, relative velocity, and absolute acceleration response are calculated using the Newmark-\(\beta\) method to obtain the DRF values corresponding to different response spectrum types. Using 775 real vertical ground motions selected from the PEER database, each response spectrum was determined for damping ratios (\(\xi\)) of 1%, 3%, 5%, 10%, 15%, 20%, 30%, and 40%. Calculating the damping reduction values at damping ratios different from the 5% reference spectrum, namely the DRFa, DRFd, and DRFv, the effect of the earthquake magnitude (M\({}_{\rm w}\)), closest distance (R\({}_{\rm rup}\)), and site conditions was assessed. Based on these evaluations, new nonlinear prediction equations were developed for each response spectra using the damping ratio and the natural vibration period as variables. Consequently, the following conclusions could be drawn from the results:  
1. The effect of the moment magnitude M\({}_{\rm w}\) on the DRFa is more pronounced than that on the DRFd and DRFv. Moreover, the effect of damping on the DRFa decreases with an increasing earthquake magnitude.
2. Compared to the DRFd and DRFv, the DRFa is less sensitive to the damping ratio for R\({}_{\rm rup}>\) 30 km.
3. The amplitude of and trend in the DRFs are relatively similar between the site classes A and D and B and C, respectively. It was observed that soil classes have no apparent effect on the DRFa at low damping ratios (1% and 3%). On the other hand, at high damping ratios, the soil classes influence the DRFa, especially for periods longer than 1.0 s.
4. The DRFs obtained from different spectra highlighted that DRFs based on displacement spectra and absolute acceleration spectra have the same trend and they can be described in similar functional form.
5. It was proven that the proposed equations for the DRFa, DRFd, and DRFv are strongly compatible with real DRFs and showed strong robustness when compared with the existing DRF models.
6. Comparison of the code recommendations with the proposed model revealed that they agree on the DRFa when T \(\leq\) 0.5 s. On the other hand, these seismic codes underestimate the DRFs for systems experiencing periods longer than 1.0 s.  
Figure 17: Comparison of proposed DRFa values and seismic codes [12; 13; 14; 44; 45; 46] for different period values (T = 0.1; 0.25; 0.5; 1.0; and 2.0 s).  
**Author Contributions:** Conceptualization, B.B. and A.R.; methodology, A.R.; software, I.A.; validation, M.P., A.R. and I.A.; formal analysis, B.B.; investigation, A.R.; resources, A.R.; data curation, A.R.; writing--original draft preparation, A.R.; writing--review and editing, A.R., B.B., M.P. and I.A.; visualization, I.A.; supervision, B.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The authors offer their thanks to the Pacific Earthquake Engineering Research Center (PEER) ground motion database for providing the data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 1 Introduction
However, this study proposes how RS can be used by using DA to suggest potential matches for shippers and consignees. The case study in this research involves northern Thailand's largest bus company. The company offers courier services using the available space under routed buses. The service is in high demand due to the growth of e-commerce. The company saw an opportunity to expand its customer base after using a market development strategy based on the Ansoft Matrix [11] to develop new uses for its existing service. Based on existing customer databases, the Matching Consignees/Shippers Recommendation System (MCSRS) is one of the new market development strategies the company has chosen to use. The system explores new business opportunities for matching shippers with consignees by looking for patterns in receiving/sending and whether there is hidden supply and demand data. However, the company first needs to decide on the MCSRS business model's design before investment to identify demand, target consumers, product recommendations, and whether the business model will work [12]. Many companies use questionnaires to collect data to analyze customer behavior and needs. This can require long periods to compile feedback and may lead to communication problems and increased costs [13]. Conversely, this research aims to develop an MCSRS for courier services using existing data with minimal effort by applying DA. It will be of interest to note whether DA techniques can gain insight into customers' preferences and define customer demand in the new business. This approach could help managers decide on investment without the need to create a prototype and perform market testing. If the company implements the MCSRS business model, it could introduce suitable shippers to consignees, creating new business partnerships while increasing delivery volume. In addition, shippers and consignees were provided a suggestion of new products and new business partnerships which are most-likely and predictably matched. This study explores whether DA can be used to identify association, raising the following research questions:  
**RQ1**: Which products/items do consignees most often receive together? Which items are likely to be recommended? **RQ2**: Which shippers and consignees are the most/least valuable? What are their distinct characteristics? **RQ3**: Which shippers and consignees have a greater likelihood of becoming new business partners? **RQ4** Can the matching of consignees and shippers generate revenue? This paper is organized as follows: Section 2 presents relevant literature. Section 3 describes the research methodology. Section 4 describes the implementation through a case study. Finally, Section 5 presents conclusion and discussion along with the limitations of the study.
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Optical Coherence Tomography
In PS-OCT, lesions appear with higher contrast, so they are easily seen. In addition, high-intensity specular reflection off lesions that usually prevent them from being detected is reduced with the use of PS-OCT. OCT may also be used to monitor the remineralisation of existing lesions, as well as the detection of secondary caries after dental restorations [115]. In addition to its high resolution, another attractive aspect of OCT in dentistry is the ability to obtain precise thickness and volume measurements. [PERSON] et al. (2016) used the SD-OCT technique to produce a method to calculate the thickness and volume of remaining enamel regions during decay and demineralisation [116]. The study also successfully obtained high resolution images of ex vivo caries. OCT has also been applied in the detection of oral cancer. In 2004, [PERSON] et al. investigated the use of OCT for imaging and detecting epithelial changes and concluded that OCT has excellent potential for use as a diagnostic tool [117]. The same conclusion was reached in a 2005 study by [PERSON] et al., who investigated the use of OCT for diagnosis of oral cancer in multiple stages of  
Figure 11: OCT images of ceramic/enamel interface under (**a**) an 80% load with 21 cycles, (**b**) an 80% load with 205 cycles, (**c**) a 70% load with 4071 cycles and (**d**) a 60% load with 38,271 cycles. Adapted with permission from Ref. [113] 2022, Elsevier. progression [118]. The results confirmed OCT to be a good potential diagnostic tool of oral cancer. [PERSON] et al. also used the imaging the technique to scan various oral lesions and differentiate between them depending on their carcinogenesis stage [119]. After analysis of images, distinct features were identified that signified the stage of carcinogenesis. Mild and moderate dysplasia showed a significantly thicker epithelium compared to the control, but the boundary between the epithelium and the lamina propria was still visible. However, this boundary was no longer visible in the early-stage and well-developed squamous cell carcinoma samples. The significance and excellent potential of OCT as a diagnostic tool for oral cancer was successfully portrayed. Further development of OCT for oral cancer includes the use of surface plasmon gold nanoparticles to enhance contrast [120].
## 2 Nonlinear Switch Characterization
### Nonlinear Assessment of RA and PL
The EVM is a communications performance metric [43] that gained relevance in favor of traditional ones such as Bit-Error Ratio (BER) because of its independence on the receiver (no need to decode the received bit-stream), and its independence on the modulation scheme used [44] in either high- or low-distortion environments. The EVM is related to transmitter imperfections [45] such as modulator carrier leakage, IQ mismatch, nonlinearity, local oscillator phase noise and frequency error. An approach to calculate the EVM on nonlinear devices is presented in [46], assuming one system impairment is dominant whereas the remaining are modelled as part of the Gaussian noise. Figure 7: Port match in a test reconfigurable antenna. In general, the EVM is defined as  
\[EVM =\frac{\frac{1}{N_{S}}\sum_{n=1}^{N_{S}}\abs{S_{r}(n)-S_{t}(n)}^{2}}{ P_{0}} \tag{4}\] \[P_{0} =\frac{1}{N_{S}}\sum_{n=1}^{N_{S}}\abs{S_{t}(n)}^{2},\]  
where \(S_{t}(n)\) and \(S_{r}(n)\) are the nth transmitted and received symbols, respectively. Although it is valid only in \"Data-aided receivers\" [47], the Signal-to-Noise Ratio (SNR)-based approximation for EVM is proven to hold well for high SNR setups. It can also be used as an upper bound for the real EVM when the received symbols are estimated. Using a base-band signal model and reasoning similar to that in [46], the EVM can be expressed in terms of the SNR and the in-band nonlinear distortion (HD). Therefore, acknowledging that the major contribution to the in-band nonlinear distortion comes from the third order intermodulation products [45], the approximated EVM can be expressed as:  
\[EVM \approx\sqrt{\frac{1}{SNR}+HD} \tag{5}\] \[EVM \approx\sqrt{\frac{N_{0}}{P_{0}}+\left(\frac{P_{0}}{IP_{3}} \right)^{2}},\]  
which states that in low input power setups, the main driver of EVM is the system noise, while in high power setups the harmonic distortion is the main responsible for error. The approximated EVM is calculated using the results of the HB simulation of the PL-based RA under test, and assuming a moderately high noise floor of \(N=-60\) dBm. Results are shown in Figure 9. The observed results are in agreement with the trends and values observed in the measurements reported in [30], where the antenna operation mode \(M_{4}\) presents a higher EVM than the \(M_{1}\), and the EVM is below 8% for input powers up to \(P_{in}=30\) dBm, for all the antenna states of interest. This results also demonstrate that the PL-approach is a low-nonlinearity alternative to achieve complex antenna functions in accordance with the performance limits established for 5G. Figure 8: Harmonic components generated by the test reconfigurable antenna, with indication of \(IP_{3}\). (**left**) \(M_{1}\), (**right**) \(M_{4}\).
## 3 Finite Element Analysis
### Analytical Results
#### 3.2.1 Angles of Maximum Normal and Shear Stress Ranges  
In this section, the purpose was to predict the incipient angle of crack growth after a change in the applied load from the condition, which leads the crack to stable coplanar growth. [PERSON] and [PERSON] [13] suggested that the MTS range based on an elasto-plastic stress field could capture the effects of \(\Delta K_{\rm II}\)/\(\Delta K_{\rm I}\) and \(\delta\) on crack branching in rail steel. According to this criterion, the crack deviation angle lies along the material plane on which the tangential stress range (\(\Delta\sigma_{\Theta}\)) is maximum during the selected load cycle. In this study, the \(\Delta\sigma_{\Theta}\) level on every plane was investigated; when \(\sigma_{\Theta}\) was below zero, it was reset to zero in the calculation. The maximum \(\Delta\sigma_{\Theta}\) value and the plane on which it appeared were indicated, respectively, as \(\Delta\sigma_{\rm max}\) and \(\theta_{\rm\sigma max}\). The shear stress range (\(\Delta\tau_{\Theta}\)) on every plane was also investigated. This underlying assumption was adopted: if the cracks grow via a shear mode, the growth direction should be determined by the plane (\(\theta_{\rm\tau max}\)) on which \(\Delta\tau_{\Theta}\) became maximum (\(\Delta\tau_{\rm max}\)).  
As mentioned in Section 2, \(\Delta K_{\rm II}\)/\(\Delta K_{\rm I}\) and \(\delta\) could influence the crack growth direction. More specifically, when \(\Delta K_{\rm II}\)/\(\Delta K_{\rm I}\) increased from 1 to 1.5 with \(\delta\) = 120\({}^{\circ}\), the crack growth changed from coplanar into branch; a \(\delta\) increase from 90\({}^{\circ}\) to 120\({}^{\circ}\) while maintaining \(\Delta K_{\rm II}\)/\(\Delta K_{\rm I}\) = 1.5 changed the coplanar growth into branch growth; this was explicitly clarified by [PERSON] and [PERSON], namely, if this ratio was increased, the \(\Delta\sigma_{\Theta}\) direction switched from \(\theta\) = 0\({}^{\circ}\) to \(\theta\) = -70\({}^{\circ}\).Therefore, in this study, the \(\delta\) influence on the crack growth direction was clarified.  
The FEA results about the values of \(\Delta\sigma_{\rm max}\)/\(\Delta\tau_{\rm max}\), \(\theta_{\rm\sigma max}\), and \(\theta_{\rm\tau max}\), when changing \(\delta\) and maintaining \(\Delta K_{\rm II}\)/\(\Delta K_{\rm I}\) = 1.5, which is determined by comparing ARP1 and ARP2, are summarized in Table 6; when \(\delta\) increased, \(\Delta\sigma_{\rm max}\)/\(\Delta\tau_{\rm max}\) increased, whereas \(\theta_{\rm\sigma max}\) and \(\theta_{\rm\tau max}\) remained approximately on the branch and coplanar directions, respectively.
## 4 Verification of Evaluation Methods
### Comparison of Evaluation and Experimental Results  
The evaluation accuracy of the iterative procedure is verified by the corresponding test data. The test and evaluation results of the beam shear force (\(P_{b,u}\)), the beam deflection angle (\(R_{b,u}\)), and tendon strains (\(\varepsilon_{t,c}\) and \(\varepsilon_{t,t}\)) were compared in Figures 12 and 13. In the legend of Figure 14, (+) and (-) represent the values at the positive and negative loadings. To measure the tendon strains \(\varepsilon_{t,c}\) and \(\varepsilon_{t,t}\), strain gauges were attached to the tendons at the beam-column interface. The test data at both the positive and negative loadings are compared with the evaluation results. For the ultimate shear force \(P_{b,u}\), the evaluation errors of the iterative method are within 10%.  
It is noted that the test results of \(P_{b,u}\) for PCX01, PCJ05, and PCJ06 do not show much difference. The average \(P_{b,u}\) of the positive and negative loadings for the three specimens are 79.5 kN, 82.0 kN, and 80.2 kN, respectively. Although the evaluation results of \(P_{b,u}\) seem to have larger variation than the test results, the evaluation errors relative to the average \(P_{b,u}\) are within 5%. In addition, the test results of \(P_{b,u}\) increase in the order of PCX01, PCJ06, and PCJ05, and the same order could be found in the evaluation results. Therefore, the evaluation results exhibit good agreement with the test results.  
The evaluation results of \(R_{b,u}\) are found to be less accurate, and the errors were almost less than 20%. It is noted that the calculated deformation of the PC zone was dominant in all the specimens, and for the specimens PCX01, PCX02 and PCJ06, the deformation of the RC zone was less than 10% of the total beam deformation. Meanwhile, the RC zone deformation increased to 29% for PCJ05, since the bending moment and the length of the RC zone were relatively larger than the other specimens. With respect to the tendon strains \(\varepsilon_{t}\), the evaluation errors are within 15%. From Figures 12 and 13, the ultimate flexural points determined by the iterative method (intersections of the dashed lines representing the evaluated \(P_{b,u}\) and \(R_{b,u}\)) correspond well with the test results. Therefore, the iterative method has good accuracy, and the analytical model is able to faithfully reflect the seismic behavior of the PC beam-column connection subassemblages with debonded partial tendons.  
Figure 14: Comparison of iterative method results and test data: (**a**) beam shear forces; (**b**) beam deflection angles; (**c**) strain of tensile side tendons; (**d**) strain of compression side tendons.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 6 Reliability Prediction of the Communication Module Under the Mission Profile  
### Performance Degradation and Functional Failure Simulation Method  
To predict the reliability of the communication module, the performance degradation and functional failure of its components need to be simulated. Verilog-AMS is used for modeling, as it allows for the description of component behavior based on their voltage-current characteristic relationships. During the modeling process, the simulation time is introduced as a variable, enabling the dynamic adjustment of component characteristics to simulate performance degradation and functional failure. Table 4 summarizes the simulation methods for performance degradation and functional failure under the Verilog-AMS framework.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component Type** & **Failure Mode** & **Simulation Method** \\ \hline \multirow{4}{*}{Analog components} & \multirow{2}{*}{Parameter drift} & Import parameter degradation curves via lookup tables or expressions, modify corresponding parameters in the component model based on these curves. \\ \cline{2-3}  & & Use random numbers or specified failure times. Change the resistance values of series/parallel resistors once the failure time is reached. \\ \hline \multirow{4}{*}{Digital components} & \multirow{2}{*}{Output delay, amplitude, rise/fall time, and input threshold changes} & Import performance degradation curves via lookup tables or expressions, modify the corresponding parameters in the mixed-signal interface model based on these curves. \\ \cline{2-3}  & & Use random numbers or a given failure time. After the failure time is reached, control the device model to output logic ’1’. \\ \cline{2-3}  & & Use random numbers or a given failure time. After the failure time is reached, control the device model to output logic ’1’. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation methods for performance degradation and functional failure within the Verilog-AMS framework.  
Figure 20: Telemetry data simulation results showing transmit/receive timing strings.
## 1 Introduction
### LoRa  
\"LoRa Technology has revolutionized IoT by enabling data communication over a long range while using very little power. When connected to a non-cellular LoRaWAN(tm) network, LoRa devices accommodate a vast range of IoT applications by transmitting packets with important information. LoRaWAN fills the technology gap of cellular and Wi-Fi/BLE based networks that require either high bandwidth or high power, or have a limited range or inability to penetrate deep indoor environments. In fact, LoRa Technology is flexible for rural or indoor use cases in smart cities, smart homes and buildings, smart agriculture, smart metering, and smart supply chain and logistics\" [2] (see Figure 1).
## 3 Test Scheme and Result Analysis
### Uniaxial Dynamic Impact Compression Test
#### 3.2.5 Analysis of Failure Modes  
In order to study the effect of temperature on the macroscopic damage of concrete materials in dynamic impact compression tests, uniaxial dynamic impact compression tests of regular C40 plain concrete and S1 PP0.2 HyFRC in different temperature groups were conducted, and the macroscopic failure modes were comparatively investigated. The failure modes of C40 plain concrete and S1 PP0.2 HyFRC in six temperature groups (25 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 800 \({}^{\circ}\)C) at an impact air pressure of 0.3 MPa is shown in Figures 12 and 13.  
In general, both C40 plain concrete and S1 PP0.2 HyFRC in all six temperature groups become severely damaged with increasing temperature. At 25 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, the failure mode of C40 plain concrete is surface spalling; at 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C, the failure mode is mainly core failure; at 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, the failure mode develops into mass fragmentation and crushing. S1 PP0.2 HyFRC exhibits no significant failure at 25 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, with only small cracks observed; the failure mode at temperatures of 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C is surface spalling, exhibiting unpropagated cracks under the action of hybrid fibers; at 600 \({}^{\circ}\)C and  
Figure 11: Dynamic impact stress-strain curves for S1 PP0.2 HyFRC in different temperature groups.  
800 \({}^{\circ}\)C, the failure mode is dominated by the core failure, and the steel fibers passing through the body of the concrete as well as those pulled out during the core failure process can be clearly observed. Under the same impact air pressure and the same temperature condition, S1 PP0.2 HyFRC is less damaged and more resistant to impact compression than C40 plain concrete [27].  
Figure 12: Failure modes of C40 plain concrete at different temperatures.  
Figure 13: Failure modes of S1 PP0.2 HyFRC at different temperatures.
# Key Strata Identification of Overburden Based on Magnetotelluric Detection: A Case Study
## 2 MT Detection Method
### Technical Principle
It can be seen that when the resistivity is a constant, the propagation depth or detection depth of electromagnetic wave is inversely proportional to the frequency; i.e., high frequency corresponds to electrical property features in shallow strata, and vice versa. Therefore, the different frequency electromagnetic waves can be used to identify the strata depth. Moreover, the apparent resistivity (AR) and apparent phase can be calculated by observing the electric and magnetic field information in the broadband. Subsequently, the geoelectric characteristics and strata structure can be determined. Based on the characteristics of key strata, the relationship between the geological characteristics of overburden KS and its AR response are shown in Figure 3. Therefore, based on its basic principle, combined with inversion analysis and visualization technology, the KS of overburden can be identified. Figure 3: Relationship between KS characteristics and its apparent resistivity (AR) response.