esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measured Signal Processing  
### Generator Data Processing  
To verify the application effect of the algorithm, different amplitudes of MRS signals were transmitted through a signal generator in the laboratory. The JLMRS instrument was used to acquire noisy MRS data, which were then processed by the MP algorithm based on the oscillating atomic library. The transmitting frequency of the signal generator was 2355 Hz, \(T_{2}^{*}\) = 200 ms. Nine sets of MRS signals with different transmitting amplitudes were generated; the initial amplitude of the obtained signals was in the range of 5 uV\(\sim\)45 uV. The corresponding SNR range was \(-\)10 dB to \(-\)28 dB. The error maps of \(E_{0}\) and \(T_{2}^{*}\) after denoising with the MP algorithm are shown in Figure 4.  
As shown in Figure 4, the uncertainty of \(E_{0}\) and \(T_{2}^{*}\) increased as SNR decreased. When SNR was \(-\)28 dB, the uncertainty of \(E_{0}\) and \  
Figure 6 shows that when the amplitude of the transmission were fixed and the frequency difference was set to 1, 3, and 5 Hz by changing \(f_{l}\), the denoising effects of the MP and SSA algorithms were able to eliminate the noise. In addition, Figure 6 shows that when the frequency difference was 1 and 3 Hz, the denoising effect of the MP algorithm was better than that of the SSA algorithm. As the frequency difference was further reduced, the MP algorithm's denoising effect deteriorated gradually; however, it remained better than that of the SSA algorithm.  
As shown in Figure 7, when \(T_{2}^{*}\) was 200, 300, and 400 ms, the MP and SSA algorithms could eliminate noise effectively, and the denoising effects were close. As \(T_{2}^{*}\) continued to decrease, the denoising effects of both algorithms gradually deteriorated.  
Figure 5: Comparison of the denoising results of the MP, SSA, and HMC algorithms under different SNRs: (**a**) SNR = \(-\)10.7 dB, (**b**) is the corresponding spectrum; (**c**) SNR = \(-\)12.8 dB, (**d**) is the corresponding spectrum; (**e**) SNR = \(-\)14.9 dB, (**f**) is the corresponding spectrum.  
Figure 6: Comparison of denoising reuslts of the MP, SSA, and HMC algorithms with a frequency difference of 1, 3, and 5 Hz: (**a**) \(\Delta f\) = 1 Hz, (**b**) is the corresponding spectrum; (**c**) \(\Delta f\) = 3 Hz, (**d**) is the corresponding spectrum; (**e**) \(\Delta f\) = 5 Hz, (**f**) is the corresponding spectrum.  
In Figures 5-7, the HMC algorithm only realizes the removal of power frequency harmonic interference. Due to the complexity of the measured electromagnetic noise, there is a lot of random noise and other frequency interference in the environment, and the HMC algorithm has no ability to deal with this interference. To sum up, it can be seen that MP algorithm achieves better performance under different signal-to-noise ratios, different Larmor frequencies, and different relaxation times.
## 4 Experiments and Result Analysis
### Generalization Experiments
Accuracy declines slightly as the network deepens, indicating that ResNet-18 offers the optimal trade-off between complexity and performance for this task. Consistent with findings in other studies, larger backbones do not yield accuracy gains commensurate with their parameter growth on birdsong classification [40].
# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 2 Methodology
### Proposed Level 1 and Level 2 Damage Detection  
This section explains the proposed method that intends to detect damage occurrence and location. The proposed method utilizes the displacement reconstruction algorithm explained in an earlier section. In the bridge monitoring task, accelerometers often spread along a bridge span, as illustrated in Figure 7. Assuming that \(i\) number of sensor points (SP) spread along a bridge, as shown in Figure 7 and each sensor records acceleration data. If there is damage occurred, the collected accelerations naturally include two sets of records. Namely, the original and observed sets. These two sets of accelerations are then transformed into reconstructed displacements (\(RDisp\)) using Equation (10). The root-mean-square (RMS) of \(RDisp\) for each sensor point (\(RMSD_{i}\)) is calculated as formulated in Equation (11).  
\[RMSD_{i}=\sqrt{\frac{\sum\limits_{j=1}^{all}RDisp^{2}(j)}{n}} \tag{11}\]  
where \(n\) is the quantity of data.  
Figure 5: (**a**) ADXL 335 accelerometer; (**b**) Nordic Semiconductor nRF9160.  
Figure 6: General scheme of signal transmission from an on-site sensor to an off-site server.  
Each original and observed sets consist of \(i\) number of _RMSD_, and for either set and is normalized with its own maximum value if shown by a diagram with respect to the sensor position will create a deflection shape as shown in Figure 7. Level 1 damage indicator is the shape difference between the original and observed _RMSDs_, as shown in Figure 8. Once there is a significant difference between these two _RMSDs_, the bridge will be diagnosed as a damaged one. The larger the damage indicator, the more severely damaged the bridge. A threshold could be suggested based on available data. As shown in Figure 8, the difference between two _RMSDs_ at the _SP4_ position is zero since two sets of data are normalized with respect to the same midpoint.  
For Level 2 damage indicator, the _dRMSD_, which is the difference between adjacent _RMSDs_ as formulated in Equation (12), is calculated ( _RMSD\({}_{i}\)_ is Zero ). Same as Level 1 damage indicator, original and observed _dRMSDs_ are computed as illustrated in Figure 9. Subsequently, the difference between original and observed _dRMSDs_ are plotted with respect to the sensor point, as shown in Figure 9. Each segment of the bridge is represented by a single value of damage indicator (i.e., _dRMSD_) and If one of the values is particularly prominent, indicating that a particular segment is in a damaged status. The more sensors, the more accurate in locating the damaged position.  
\[dRMSD=\left[\begin{array}{l}|RMSD_{1}|,|RMSD_{2}-RMSD_{1}|,|RMSD_{3}-RMSD _{2}|,\dots,\\ |RMSD_{i}-RMSD_{i-1}|\end{array}\right] \tag{12}\]  
Figure 8: Illustration of Level 1 damage indicator.  
Figure 7: Illustration of sensor points and damage index 1.
###### Abstract
Several studies have shown that this method of remote monitoring can also be used in daily clinical practice to reduce the time and cost of regular checkups, to determine the level of oral hygiene and oral cavity health of patients, to provide rapid and accessible screening, and to provide a means for patients to be monitored in the clinic, which has various benefits for many patients, including people who live far from residential areas and have little opportunity to reach hospitals or private practices, patients with special needs, sick syndromic patients, and people living in socioeconomically disadvantaged conditions [11; 12; 13; 14]. Therefore, this narrative review primarily aims to review the literature, analyzing the trends that today represent useful tools to provide for the oral health of pediatric patients, with particular reference to teledentistry and all the supports it uses to reach remote territories or patients who, for various reasons, cannot have access to dental care. Second, this paper has set the goal of evaluating and identifying the potential criticalities, future challenges, and perspectives that the introduction of this method may have in the daily activities of the dentist.
###### Abstract  
Purpose: To date, the qualitative and quantitative recording of biomechanical processes in dental implants represents one of the greatest challenges in modern dentistry. Modern, dynamic, 3D optical measurement techniques allow highly constant and highly accurate measurement of biomechanical processes and can be superior to conventional methods. This work serves to establish a new measurement method. Materials and Methods: A comparative analysis was undertaken for two different measurement systems, two conventional strain gauges versus the 3D optical two-camera measurement system ARAMIS (GOM GmbH, Braunschweig, Germany), as they detected surface changes on an artificial bone block under masticatory force application. Two implants (Straumann Standard Implants Regular Neck, Straumann GmbH, Freiburg, Germany) were placed in the bone block, and three different three-unit bridges were fabricated. Increasing masticatory forces, from 0 to 200 N, were applied to the bone block via each of these bridges and the inserted implants. Fifteen repetitions of the test were performed using a universal testing machine. The computer unit of the ARAMIS system was used to simultaneously integrate the surface changes recorded by the strain gauges and the ARAMIS system. The areas on the bone block examined by the dynamic 3D optical measurement method corresponded exactly to the locations and extent of the strain gauges. A statistical comparative analysis was carried out separately for the strain gauges and the corresponding optical measuring surface at the defined force magnitudes. The equivalence test and the intraclass correlation served as statistical means. Results: In the case of the intraclass correlation, a clear concordance of both measurement methods could be shown for all examined cases. For the equivalence test, no significance could be shown in individual cases. Conclusion: The accuracy of the modern, dynamic, 3D optical measurement method is comparable to that of conventional strain gauges. On this basis, versatile new research approaches in the field of biomechanics of dental implants can be pursued by establishing this method.  
implant biomechanics; dental implants; strain; bone deformation
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 3 Analysis of OFDM/FHMA System Based on Mini-Slot Frame Structure  
In this section, the signal analysis of the OFDM/FH system is derived in detail. The derived decision variable can be utilized to evaluate the reliability in terms of the BER by Monte Carlo simulations.  
Then, the latency due to the (re)transmission on the basis of the OFDM/FH signal is analyzed. In this section, the numerical tradeoff between the reliability and the latency is obtained.
## 3 Ambiguity Suppression Method
### Anti-Compression and Imaging  
Since the imaging and ambiguity suppression process are reversible, echo data can be obtained by anti-compression in range and azimuth direction to the data after ambiguity suppression. The image after ambiguity suppression are provided with Chirp-Scaling algorithm.  
The proposed ambiguity suppression method has two limitations:  
* Only for ambiguous areas with odd ambiguous numbers;
* Only for strip data with tiny range migration.
## 1 Introduction
Building on FASM, [PERSON] and [PERSON] proposed the MHS (moment-height stability) method [17], which is primarily used for assessing the stability of robots such as aerial manipulators. MHS is defined as the minimum value of the product of the unit vector along the support polygon edge, the equivalent moment acting on that edge, and the robot's moment of inertia relative to the same edge. [PERSON], [PERSON], and others conducted a comparative analysis of ZMP, FASM, and MHS, and performed tipping experiments on robots. The results demonstrated that FASM and MHS are more effective than ZMP in dynamic stability evaluation. Among them, FASM showed higher sensitivity and a certain degree of predictive capability compared to MHS. Therefore, this paper adopts the force-angle stability margin (FASM) as the stability criterion, using the minimum tipping angle as the stability index. Corresponding state flags are output and integrated with a finite state machine for decision-making control. The subsequent content is arranged as follows: Section 1 provides a review of existing control strategies for legged robots. Section 2 analyzes the leg structure of the legged robot and presents the kinematic and dynamic modeling. Section 3 sets the minimum tipping angle index for the entire robot based on the FASM method and makes decisions on tasks such as pit detection and ground force adjustment using a state machine. Section 4 proposes a position-based impedance control scheme, optimizes impedance control parameters, and conducts simulation analysis. Section 5 proposes a force-based impedance control scheme, performing stability and simulation analysis on the designed control. Section 6 presents prototype experiments based on the previous research, comparing and analyzing the results from simulations and experiments. Section 7 summarizes the work of the entire paper.
## 5 Scanning the Coverage Area with Directional BS Antennas (LOS Channels)
### Performance Comparison between Deterministic and Random Scanning Algorithms (LOS Channels)  
To compare the performance of different scanning strategies, Figure 9 shows the misdetection probability versus the distance of a UE from the BS for three scenarios: (i) omnidirectional BS; (ii) random scanning with an \(8\times 8\) BS antenna array; and (iii) scanning with the proposed pattern of an adaptive array as described in Table 3.  
Figure 8: Deterministic optimal scanning pattern for BS using adaptive directional antennas under LOS condition; (**a**) slot 1 with \(4\times 4\) antenna array; (**b**) slots 2–4 with \(4\times 4\) array; (**c**) slots 5 to 10 with \(8\times 8\) array (**d**) slots 11–25 with \(12\times 12\) array; (**e**) Coverage for 25 slots over the entire cell segment.  
We observe that the misdetection rate is significantly improved when an adaptive antenna is exploited under the proposed scanning scheme. With an omnidirectional antenna at BS (blue line), the detection of PSS signals does not perform well at UEs located at distances greater than 100 m away from the BS. Within the 100 m to 200 m range, the UEs can still detect the PSS signals, but the misdetection rate sharply increases as a function of distance. For UEs located at distances greater than 200 m, it becomes impossible to detect the PSS signal. This is consistent with the results in Figure 4. For the random scanning case using an 8 \(\times\) 8 array antenna at BS, the misdetection rate (green-dashed-x line) fluctuates over the region. Although the random scan method is simple and can be applied to different coverage areas, it is not necessarily efficient to achieve a short search time. The mean misdetection probability of the UEs is very high within a required search time of 125 ms (25 time slots). This is due to the fact that the antenna beam width versus the area of coverage is not considered, which results in the rise of the misdetection rate on these regions. The proposed deterministic scanning scheme using an adaptive array achieves low misdetection rates (red dashed line) even for UEs located at -400 m away from the BS. For UEs located beyond 400 m, although the misdetection probability starts increasing, it is still better compared to the case of omnidirectional transmission or random scanning with a directional antenna.
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 9 Conclusions  
After a comprehensive examination of the current state of research on the electrical properties of rock and ore specimens, this study integrates the latest trends in electronic technology and geophysical instrumentation to design and develop an electrical impedance tomography system for rock samples with high-density microelectrodes based on an Android system and STM32 microcontroller.  
The system features a high-density array comprising 384 electrodes, consisting of 192 potential electrodes and 192 current electrodes, allowing for flexible configuration during measurements as needed. We have endeavored to minimize the power consumption of the instrument, thus requiring only a compact power bank for operation. Internally, the instrument incorporates a small-scale signal source designed by us, without compromising accuracy. The integration of smartphones as the interface for human-machine interaction markedly diminishes the complexity entailed in instrument operation.  
The testing results demonstrate that the developed acquisition system has successfully met the anticipated design requirements, providing robust technical support for  
Figure 16: Inversion results of chargeability. (**a**) Chargeability distribution of the specimen’s cross-section. (**b**) Highlighted positions of crack.  
the investigation of electrical properties in rocks and ore samples. Consequently, this study holds considerable significance in aiding mineral exploration and development by providing valuable assistance in mineralized zone detection, subsurface structure analysis, and mineralization type prediction.  
Conceptualization, R.C., S.C. and B.S.; methodology, R.C. and S.C.; software, X.P., S.H., H.Z. and B.S.; investigation, X.P. and S.C.; resources, X.P. and C.X.; data acquisition and processing, X.P. and B.S.; writing--original draft preparation, X.P.; writing--review and editing, R.C., S.C. and B.S.; visualization, X.P. and B.S.; supervision, R.C.; project administration, S.C.; funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
Data Availability StatementData is contained within the article, further inquiries can be directed to the corresponding author.  
We would like to express our gratitude to the research team led by [PERSON], from China University of Mining and Technology, for their assistance in designing and implementing the mainframe mechanical structure of this system. [PERSON] from China Nonferrous Metals Changsha Investigation and Design Research Institute Co., Ltd. provided support for the experiments in this study. We also extend our thanks to [PERSON] and [PERSON], from Central South University, for their valuable support with this research. Additionally, we acknowledge the support and permission granted by Giant Sequoia Artificial Intelligence Technology (Changsha) Limited in making our research findings publicly available.  
The authors declare no conflicts of interest.  
References
## 4 Applications of GenAI-Driven CV in Smart Agriculture
### Crop Health Monitoring and Pest Detection
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Technique** & **Methods** & **Advantages** & **Limitations** \\ \hline
[92] & Classical CV & \begin{tabular}{c} Colour segmentation, \\ thresholding, \\ morphological operations \\ \end{tabular} & \
\begin{tabular}{c} Simple, computationally \\ inexpensive, intuitive \\ feature extraction \\ \end{tabular} &
\begin{tabular}{c} Sensitive to illumination, \\ requires manual features, \\ lacks scalability \\ \end{tabular} \\ \hline
[94; 101; 104] & Deep Learning & \begin{tabular}{c} ResNet, VGGNet, Inception, \\ YOLO variants, Hybrid \\ CNN models, \\ 3D-CNN, CNN-LSTM \\ \end{tabular} & \
\begin{tabular}{c} High accuracy, automatic \\ feature extraction, suitable \\ for complex patterns \\ \end{tabular} &
\begin{tabular}{c} Requires extensive datasets, \\ performance degrades with \\ unseen conditions \\ \end{tabular} \\ \hline
[109; 113] & \begin{tabular}{c} Generative \\ Adversarial \\ Networks \\ \end{tabular} & \begin{tabular}{c} DCGAN, CycleGAN, \\ spatiotemporal GANs \\ \end{tabular} & \
\begin{tabular}{c} Addresses data scarcity and \\ class imbalance, enhances \\ dataset diversity, facilitates \\ cross-domain adaptation \\ \end{tabular} &
\begin{tabular}{c} Training instability, potential \\ mode collapse, semantic \\ consistency challenges \\ \end{tabular} \\ \hline
[115; 116] & \begin{tabular}{c} Multimodal \\ Integration \\ \end{tabular} & \begin{tabular}{c} Fusion of hyperspectral, \\ thermal, and RGB imagery, \\ conditional GANs for \\ modality translation \\ \end{tabular} & \
\begin{tabular}{c} Improved resilience to \\ sensor variability, enriched \\ feature representation \\ \end{tabular} &
\begin{tabular}{c} Complex data alignment, \\ sensor integration challenges \\ \end{tabular} \\ \hline
[118; 120] & \begin{tabular}{c} Explainable \\ and Edge AI \\ \end{tabular} & \begin{tabular}{c} SHAP, LIME, edge AI \\ accelerators, quantised \\ GAN architectures \\ \end{tabular} & \
\begin{tabular}{c} Interpretability, real-time \\ deployment, reduced \\ computational overhead \\ \end{tabular} &
\begin{tabular}{c} Limited semantic consistency, \\ stability concerns \\ \end{tabular} \\ \hline
[121; 123] & \begin{tabular}{c} Foundation \\ Models \\ and VLMs \\ \end{tabular} & \begin{tabular}{c} Few-shot learning, \\ GPT-Vision, dialogue-based \\ advisory systems \\ \end{tabular} & \
\begin{tabular}{c} Improved generalisation, \\ accessibility, enhanced \\ human interaction \\ \end{tabular} &
\begin{tabular}{c} Data privacy, \\ resource-intensive pretraining \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of techniques for crop health monitoring and pest detection.
###### Abstract  
Due to the difficulties in differentiating bilingual children with Developmental Language Disorder (DLD) from bilingual children with temporary language difficulties that may be caused by heterogeneous language input, language assessments of bilingual children are challenging for clinicians. Research demonstrates that assessments of bilingual children should be in all the languages a bilingual child speaks. This can be arduous for clinicians, but computerised screening approaches provide potential solutions. MuLiMi is a new web-based platform designed to automatise screening procedures for bilingual children at risk of DLD. To validate this procedure and investigate its reliability, 36 Spanish-speaking children, aged 4-6 years old, living in Italy, were tested remotely using the Italian-Spanish MuLiMi DLD screening. Sixteen of the participants were previously diagnosed with DLD. L2 (second or societal language) as well as L1 (first or family language) language abilities in static (nonword repetition, grammaticality judgement, and verb comprehension) as well as dynamic tasks (dynamic novel word learning) were assessed. Speed and accuracy of the children's responses were automatically recorded (except nonword repetition). Significant associations emerged between the results obtained in the screening tasks when comparing them to parental questionnaires and standardised tests. An exploratory analysis of the diagnostic accuracy indicates that the single screening scores as well as the overall total score significantly contribute to DLD (risk) identification.  
Keywords:bilingualism; Developmental Language Disorder; screening; web-based platform; computerised assessment; remote testing; dynamic novel word learning; nonword repetition +
Footnote †: journal: _Applied Sciences_  
processes have resulted in multilingual being firmly rooted in everyday life, particularly in educational and SLT contexts [4]. In fact, recent surveys report a high proportion of multilingual children attending SLT services [5]. At the same time, SLTs are not confident in assessing multilingual children or in the service provided to them [6].  
Assessing the language abilities of bilingual children continues to be a challenge for clinicians as reported by policy reports [7] and numerous survey studies with Speech and Language Therapists (SLTs), for example [8]. The literature points to the risk of misdiagnoses [9]. Previous research has demonstrated that by using computerised screening solutions, an assessment in both languages spoken is possible, see [10; 11; 12]. However, no such instrument is currently available that examines the risk of Developmental Language Disorder (DLD) in Italian-L2 preschool children applying tasks across linguistic domains. We will describe the features and outcomes of the screening platform MuLiMi for the detection of risk of DLD in bilingual preschool children, based on data collected on a group of Spanish-Italian speaking preschool children. The aim of this research is to assess the construct and convergent validity as well as a diagnostic accuracy of screening tasks implemented on the aforementioned newly developed platform.