esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Validation of FLAC Modeling on Wave Transmission across Rock Joints
### Obliquely Incident P Wave Transmission through a Single Joint  
In this slope model, the inclined angle is 30\({}^{\circ}\). For an oblique slope surface, obliquely incident wave transmission across joints occurs after being reflected from the oblique surface. In order to verify the dependence of wave transmission on the incident angle and the two-dimensional effect in FLAC, an obliquely incident P wave through a horizontal joint is given, and the wave transformation, wave reflection as well as wave transmission are shown in Figure 5. As the wave transmission is two-dimensional, in order to eliminate the effects of wave superposition of the reflected wave from the upper and side boundaries on the transmission coefficient, the point (X, Z) = (250 m, 251 m) used to monitor the transmitted wave is near the joint. Through this method, the transmitted wave across the joint and the reflected wave from the upper and side boundaries can easily be separated because of the time difference in the arrival of the P wave and the Sv wave.  
Figure 4: Relations between transmission coefficient \(T_{N}\) and dimensionless fracture spacing \(\xi\) for normally incident P wave transmission across a joint set.  
Figure 5: Obliquely incident wave transmission, reflection, and transmission on a joint.  
The FLAC results and analytical solutions are compared in Figure 6. The horizontal coordinate is the incident angle \(\theta\). The vertical coordinate is the magnitude of transmission coefficient across a single joint for P wave oblique incidence \(T_{1P}\), which is defined as Equation (3). From Figure 3, it can be found that \(T_{1P}\) from the FLAC results agrees well with the analytical solutions. Therefore, FLAC is applicable to study the dependence of wave transmission on the incident angle and the two-dimensional effect.
# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 2 Materials and Methods
### Biomimetic Facial Recognition System  
The primary function of a biometric facial recognition system is to verify and identify individuals using facial photos. These systems operate in two modes: verification (Figure 3) and identification (Figure 4). Both involve comparing biometric features in the examined image with a reference image. However, the critical difference lies in how the reference image is selected. In verification mode, the user chooses the reference image. The system's sole task is to compare the examined photo with this reference photo and determine their degree of similarity. The program operates based on a comparator principle.  
Figure 4: General operation diagram of the facial recognition system in identification mode [15].  
Figure 3: General diagram of how the facial recognition system works in verification mode.  
The most common task of verification mode is to authorize access. This mode is becoming increasingly popular as a system for unlocking Android mobile phones, authorizing banking and ATM transactions, and, in companies, accessing rooms with internal company information [12; 13].  
In the identification mode, the system compares the examined photo with a database of reference photos. Unlike the verification mode, which checks for similarity, identification provides a specific result--a particular reference photo. The identification process leverages the verification mode by comparing each database photo with the examined image. Based on a similarity measure, the program calculates the result. However, a crucial condition for obtaining a result is that the degree of similarity must exceed a specified threshold.  
In identification mode, a facial recognition program performs significantly more calculations than verification mode, impacting program performance. To address this, methods are employed to reduce the initial dimensions of the feature matrix derived from reference images while retaining essential data for the identification process. This involves removing extraneous information from reference photos, such as background elements or irrelevant points that do not represent critical biometric features of the face. A specialized algorithm extracts only the points corresponding to these essential features, as described in the preceding subsection. Identification mode is commonly used to identify individuals with multiple faces in videos. To achieve this, an additional face detection algorithm is applied to the video recording, extracting all faces within the entire video frame. The algorithm should provide the program with the minor possible data matrix containing only face pixels to enhance system efficiency. This allows the subsequent algorithm to accurately determine the characteristic biometric points on those faces [36].
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.2 Network Analyzer  
A network analyzer [9] is employed to measure the performance of RF components, including antennas and filters. It evaluates parameters such as signal loss and reflection, ensuring that these components are effectively reducing or rejecting unwanted frequencies that could cause interference.
## 6 Conclusions  
In this paper, the load transfer mechanism and bearing capacity of driven PHC nodular piles were investigated through a group of field tests and numerical simulation. The conclusions can be gained according to the field test and ABAQUS simulation results:  
1. The ultimate bearing capacity of three 450 (500) mm driven PHC nodular piles were in the range 1.23-1.38 times the ultimate bearing capacity of 450 mm driven PHC pipe pile after being cured for 40 days; this can be used for the design of PHC nodular piles in future engineering practice.
2. The simulation results showed that the bearing capacity of the PHC nodular pile would decrease with the increase in nodular spacing. The ultimate bearing capacity of the PHC nodular pile decreased from 2357 kN to 1678 kN as the nodular spacing increased from 1.0 m to 4.2 m. The PHC nodular pile with a nodular spacing of 1 m was suitable in engineering projects.
3. The bearing capacity of the PHC nodular pile would decrease with the increase in nodular length. The ultimate bearing capacity of the PHC nodular pile decreased from 2984 kN to 1827 kN as the nodular length increased from 25 mm to 225 mm. The type A PHC nodular pile could fully mobilize the ultimate capacity of the pile.
4. The bearing capacity of the PHC nodular pile would increase with the increase in nodular diameter. The ultimate bearing capacity of the PHC nodular pile increased from 2359 kN to 3583 kN as the nodular diameter increased from 500 mm to 600 mm. The diameter of the nodule can be increased moderately to improve the pile capacity.
5. The test PHC nodular piles were not equipped with strain gauges or fiber optic sensors to measure the axial force. More experimental and theoretical research is needed to investigate the load transfer mechanism of the PHC nodular pile and the load undertaken by the nodules.  
Conceptualization, B.L.; Investigation, R.Z.; Writing--original draft, Q.L.; Project administration, X.G.; Funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Natural Science Foundation of China (No. 52108350), the Scientific and Technological Innovation 2025 Major Special Projects of Ningbo (2022Z030 and 2022Z224), and Ningbo Transportation Science and Technology Project (202217). Their support is gratefully acknowledged.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Power China Huadong Engineering Corporation Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Analysis of Human Body Shadowing Effect on Wireless Sensor Networks Operating in the 2.4 GHz Band
## 3 Results
### Measurements of Human Body Shadowing in the Indoor Environment  
The results of computer simulations of the body shadowing effect performed with a simplified human body model in the XGtd(tm) program showed that the presence of a human body between the transmitting and receiving antenna can increase path loss significantly. To verify the results of the computer simulations, experimental measurements were performed in an indoor environment. The experiment was conducted in a room that had the same dimensions as the interior used for the simulations. A transmitter operating at 2.4 GHz was connected to a dipole antenna located in the middle of the room. The position of the receiving antenna was changed by approximately 1 m from 2.617 m to 3.67 m with 6 cm increments. Measurements were performed both for an empty room and with a human subject positioned between the transmitting and receiving antennas. The measurement setup is presented in Figure 13.  
Figure 14 presents the path loss measurements obtained for the first indoor scenario. Similar to the data obtained in simulations, the path loss changes dynamically with changing positions of the receiving antenna, which is the result of multipath propagation. For the purpose of comparison with the results of simulations obtained with and without human body models, the median, minimum, and maximum value was measured for the path loss with displacements of the receiving antenna over a distance of 1 m. The people were placed in the places showed in Figure 9. In the case of no human body between the antennas, the median path loss was 53 dB, the minimum value was 46 dB, and the maximum was 59 dB. In the case of one person in position \"A\", the median path loss increased to 63 dB, the minimum value was 57 dB, and the maximum 68 dB. With two people in positions \"A\" and \"B\", the median path loss was 68 dB, the minimum value was 65 dB, and the maximum was 75 dB. The presence of human bodies was therefore found to increase the path loss in the wireless link.  
Figure 13: Setup for experimental measurements.  
Figure 15 presents the path loss measurements performed according to the second scenario presented in Figure 11. In the case of two persons in the \"D\" and \"E\" positions, the median path loss was 54 dB; that is 1 dB more compared to the result obtained with no obstacle. The minimum value was 49 dB and the maximum was 60 dB. With three people in positions \"A\", \"D\", and \"E\", the median path loss was 63 dB, the minimum value was 60 dB, and the maximum was 78 dB. The presence of human bodies surrounding the transmitting antenna increased the path loss also in this geometrical case.  
Figure 14: Results of path loss measurements in the indoor environment—first scenario.  
Figure 15: Results of path loss measurements in the indoor environment—second scenario.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 3 RFF Identification Scheme
### Group-Based DCTF Identification Scheme  
The randomness of PRACH sequence selection makes it difficult for the gNB to obtain sufficient learning samples for all the preamble sequences through long-term monitoring (i.e., the gNB has only partial knowledge of the PRACH preambles). In response to this problem, Scenario 2 studies the case where the PRACH preambles used for training are only a subset of the PRACH preambles used for testing (i.e., certain PRACH preambles received for testing have not been trained by the gNB). Based on the characteristics of the ZC sequences, we note that the PRACH preambles generated using the same root \(\mu\) (i.e., the preamble sequences of the same _group_) were the cyclic shifted version of the same root sequence. As a result, DCTFs of the same _group_ exhibited similarity. Hence, when one preamble sequence within a _group_ was trained, other preamble sequences within the same _group_ could also be classified based on similar features. Therefore, a group-based DCTF identification scheme is proposed for Scenario 2, as illustrated in Figure 9. The CNN model was trained separately for each _group_. During the testing stage, the group label was determined by the sequence index, and then the extracted DCTFs would be entered into the corresponding CNN model for device classification. Hence, the number of CNN models  
Figure 8: Multi-channel DCTF-based CNN structure.  
to be trained was same as the number of _groups_. The group-based DCTF identification scheme uses a single-channel CNN because a multi-channel CNN reduces the similarity among DCTFs within the same _group_ by highlighting the transient parts, which weakens the differences among DCTFs between different groups.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 4 Simulation Results
This reduction of SNR happens because the CPI covers a range up to 3.2 m. Beyond this range limit, ISI occurs, and the processing gain is also reduced for that target located at 10 m. In comparison, the scenario mentioned above is also processed for sensing using our proposed method by finding the zero-delay for each target, as shown in Figure 4d-f. Our proposed method offers better performance for the target at 10 m by avoiding the loss in processing gain. Figure 5 represents the results when multiple targets exist and one of them is moving. In Figure 5a, the conventional approach provides accurate results for two targets, one static target located at 0.6 m and the other target moving with a speed of 5 m/s and situated at 0.85 m. In comparison to Figure 5a, Figure 5b represents the results obtained by using our proposed method where peak height is similar to the peak height in Figure 5a, but ISI effect is used for locations away from the targets. In order to verify the proposed method for the range beyond the OFDM symbol, we also simulated the case that three targets are placed at 0.85 m (moving with 2.34 m/s), 12.65 m, and 17.65 m, respectively, and the sensing results are shown in Figure 5c,d. The farthest target at 17.65 m is beyond the range of an OFDM symbol duration (12.77 m) and it does not appear in Figure 5c using the conventional approach, but it is detectable with our proposed method, as shown in Figure 5d. There is another peak with height \(\approx-20\) dB at 4.88 m (12.77 m behind the target at 17.65 m) in Figure 5d, which appears due to the CP effect. Figure 4: Simulation results of the conventional OFDM–based sensing vs. the proposed range extension method. (**a–c**) Range plots for a target at 0.6 m, two targets at 1.3 m and 1.5 m, and a target at 10 m, respectively, by using conventional OFDM–based sensing. (**d–f**) Range plots obtained by using the proposed range extension method.
## 3 Results and Discussion
### Calibration Model for Quantitative Estimation
#### 3.3.3 C12A7-CaS Samples  
According to the analysis presented in Table 6, C12A7-CaS binary samples that indicated a peak intensity ratio for the Raman peak at 517 cm\({}^{-1}\) for C12A7 and 416 cm\({}^{-1}\) for MA were the most suitable selection in this study, compared to the other identified peaks ratios. The coefficient of determination values and the MAE for a peak ratio of 517/157 were the best values. Additionally, the ratio of the suggested peaks (517/157) with better values in Table 6 can be observed as the most intense Raman peaks for C12A7 and CaS, as illustrated in the previous figure (Figure 6), showing the Raman spectra for C12A7-CaS samples. Furthermore, Figure 13 may explain how the stability of the Raman shift ratio 517/157 performed, compared to other Raman peaks ratios.  
Figure 11: Evaluation of relative stabilities (Si) of the calibration variable candidates and mean absolute error values obtained for training and validation sets for MA–CaS.  
Figure 12: Raman spectroscopy estimation for the phase content (wt. %) for MA as a function of the phase content of MA in MA–CaS sample.  
Therefore, using a relative intensity ratio of 517/157 for the C12A7-CaS samples provided a potential indicator for the analysis phase fraction changes in C12A7-CaS samples, based on the C12A7/CaS ratio. Figure 14 shows a linear regression plot between the phase content for C12A7 as a function of the relative intensities of 517/157 for C12A7-CaS samples.
## 5 Conclusions  
1. The PGS-loaded microspheres have lower burst release profiles than the PTX-loaded microspheres. The kinetics of drug release from both types of microspheres are also well characterized by the Korsmeyer-Peppas model, with release exponents \(n\) within the range of 0.45 \(<n<\) 0.89. This range of \(n\) corresponds to drug release occurring by anomalous non-Fickian release and is associated with drug diffusion and the relaxation of the polymer chains between the networks.
2. The in vitro drug release profiles obtained in this study exhibit a biphasic-controlled release of anticancer drugs (paclitaxel and prodigiosin) from drug-loaded PLGA-CS-PEG microspheres with >50% drug released at day 30. This controlled release is attributed to the presence of CS and PEG blends that limit the diffusion and erosion processes associated with drug transport through the blended microspheres.  
3. The thermodynamic analysis of in vitro drug release from PLGA-CS-PEG microspheres loaded with drugs revealed positive values of \(\Delta G\) and \(\Delta H\), and negative values of \(\Delta S\) at set temperatures of 37, 41, and 44 \({}^{\circ}\)C. This is compatible with endothermic, nonspontaneous, ordered release of anticancer drugs.
4. Controlled in vitro drug release from cancer drug (paclitaxel and prodigiosin)-loaded PLGA-CS-PEG microspheres reduces the viability of MDA-MB-231 cancer cells.
5. The controlled release of cancer drugs (paclitaxel and prodigiosin) from drug-loaded PLGA-CS-PEG microspheres occurs at slower rates than the controlled release of cancer drugs from PLGA-PEG microspheres during the first 30 days of controlled cancer drug (paclitaxel and prodigiosin) release.
6. Prior to in vivo studies, further work is required to provide more insight into the nonspecific toxicity to noncancer cells. There is also a need for further studies of biodegradability.  
Conceptualization, J.D.O. and W.O.S.; Data curation, S.M.J.; Formal analysis, S.M.J., J.D.O. and A.A.S.; Investigation, S.M.J., J.D.O., C.C.N. and V.O.U.; Methodology, V.O.U.; Supervision, J.D.O., A.A.S., O.S.O. and W.O.S.; Validation, S.M.J., J.D.O., A.A.S. and C.C.N.; Visualization, W.O.S.; Writing--original draft, S.M.J.; Writing--review & editing, J.D.O., A.A.S. and W.O.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by African Union Commission (Mwalimu Nyerere Scholarship), World Bank African Center of Excellence (ACE) Program through the Pan-African Materials Institute (PAMI) (Grant No. P126974) at the African University of Science and Technology (AUST), Abuja, Federal Capital Territory, Nigeria, and Worcester Polytechnic Institute (WPI), MA, USA.  
Not Applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 3 Results
### Connection with the Cortex  
Until recently, the relationship between the cerebral cortex and SN was believed to be only indirect, with connections via cortex-striatum-SNr or cortex-subthalamic nucleus SNr circuits. However, several studies have revealed direct projections from the cortex to the SN, the so-called cortico-nigral pathway.  
[PERSON] et al. (2016) conducted a study that utilized probabilistic tractography to identify pathways connecting broad regions of the cerebral cortex to the SN [34]. The most prominent connections were observed to be with the prefrontal cortex and with sensorimotor regions, including the gyrus precentralis, postcentralis, and superior parietal lobule. These findings expanded the network of SN connections to include direct cortical connection, a finding that is supported by a study by [PERSON] and [PERSON] (2014) [7], which also suggested that the SN has high connectivity with cortical areas. In healthy subjects, the SN demonstrated connections (>70% probability) with virtually all cortical areas tested. The authors observed the highest SN connectivity to areas of the frontal, parietal, and occipital lobes, to the primary motor and somatosensory cortex, and to the cerebellum. The presence of these pathways in humans has contributed to the clarification of certain ambiguities, such as the reasons for the alterations in SN activity during specific cognitive tasks or sensory inputs [34].
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 4 Metabolic Regulation of Secondary Metabolites
### Metabolic Regulation by Abiotic Stresses
#### 4.1.6 Metabolic Regulation by Ultraviolet Irradiation  
UV irradiiton is very convenient for inducing the production of rice phytoalexins, because it is easy to irradiate many rice leaves with ultraviolet light [9].  
An accumulation of oryzaeins A (**163**), B (**164**), C (**165**), and D (**166**), and of momilactones A (**148**) and B (**149**) accompanied by the appearance of brown spots on the leaf surface was observed in ultraviolet-irradiated detached rice leaves. Momilactone A (**148**) was detected in abundance, and among the oryzaeins, oryzaein D (**166**) was a major substance. The content of these diterpenoid phytoalexins in rice leaves was dependent on leaf aging, the accumulation of these phytoalexins in the uppermost leaves being much lower than that in the aged leaves (lower leaves), and brown spots scarcely ever appeared on the surface of the uppermost leaves [190].  
UV irradiation increased not only the concentration of momilactone B (**149**) in rice seedlings but also the secretion of momilactone B (**149**) into rice rhizosphere. As momilactone B (**149**) acts as an antimicrobial and allebotathic agent, the secretion of momilactone B into the rhizosphere may provide a competitive advantage for rice root establishment through the local suppression of soil microorganisms and the inhibition of the growth of competing plant species [191].  
UV irradiation also stimulated flavonoid and phenylalanine biosynthetic pathways in rice leaves. Five phytoalexins, i.e., sakuraneetin (**81**), \(N\)-benzoyltryptamine (**250**), \(N\)-_trans_-cinnamoyltryptamine (**253**), \(N\)-_trans_-cinnamoyltryptamine (**253**), and \(N\)-\(p\)-coumaroylserotonin (**255**) were isolated from UV-treated rice leaves [58]. In response to UV treatment, sakuraneetin accumulated in rice leaves may serve as an antioxidant against UV-induced oxidative stress [42].