esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work, the displacement measurement interferometer based on I\({}_{2}\)-frequency-stabilized LD was established. A high modulation frequency at 1 MHz by modulating the injection current was applied to the LD. The frequency of the LD was successfully locked to the b\({}_{21}\) hyperfine component of the transition 6-3, P(33) of I\({}_{2}\). The frequency peak-to-peak fluctuation of the stabilized LD was 4 MHz in 3 h. The modulation excursion was determined to improve the signal-to-noise ratio of both the I\({}_{2}\) absorption signal and the harmonic intensities. The difference in the displacement measurement result between the proposed interferometer and the capacitive sensor was 20 nm. It was reduced by half compared with our previous system. Moreover, the interferometer was used to measure the axial error of a spindle at 500 rpm. The interferometer could measure accurately even in the presence of air disturbance and vibration caused by the spindle operation. For future work, the proposed interferometer should be compared with a heterodyne interferometer to clarify clearly the measurement accuracy and measurement speed. Besides, we might also expect that the measurement speed of our interferometer could exceed the limitation speed of a heterodyne interferometer (\(\sim\)5 m/s) by using a modulation frequency of 40 MHz for the LD (see Equation (6)).  
**Author Contributions:** Supervision: [PERSON]. ([PERSON]); conceptualization: [PERSON]. ([PERSON]) and [PERSON]; methodology: [PERSON]. ([PERSON]) and [PERSON]; software: H.H.H.; validation: [PERSON]. ([PERSON]) and [PERSON]. ([PERSON]); investigation: [PERSON]. ([PERSON]); resources: [PERSON] and [PERSON]. ([PERSON]); writing, original draft preparation: [PERSON]. ([PERSON]) and [PERSON]; writing, review and editing: [PERSON]; project administration: [PERSON]. ([PERSON]); funding acquisition: [PERSON]. ([PERSON]). All authors read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Vietnam Ministry of Education and Training under Project Number B2018-BKA-07 CFVL.  
**Conflicts of Interest:** The authors declare that there is no conflict of interest.
# Analysis of Airglow Image Classification Based on Feature Map Visualization
## 5 Conclusions  
Based on the visualization method of classification models, we analyzed and optimized the model result generated by CNN training, and obtained a high-accuracy (99%) image classifier to select the airglow images captured by ASAI on a clear night from massive amounts of airglow images. The modified model requires less computation and classified more accurate than the previous model. The training process was tracked by the visualized feature maps saved after every epoch, revealing the criteria of classification, exhibiting the wrong focus of the model misled by the strong artificial light at the edge of the field of version, and illustrating how the classification model is generated. According to the feature maps, we cut out the artificial light and chose the best model before overfitting. Through practice, we found that more training epochs and a deeper model could not improve the classification. The model with the highest accuracy was exported to pick up images obtained on a clear night for further research by categorizing airglow images captured by ASAI. Our attempt provides useful experience for the automatic processing of the observation data from the Meridian Project.  
Z.L.: formal analysis, conceptualization, data curation, investigation, methodology, software, validation, visualization, writing--original draft, writing--review and editing; Q.W.: Formal analysis, Conceptualization, Methodology, Data curation, Validation, Writing--review and editing; C.L.: funding acquisition, project administration, conceptualization, methodology, formal analysis, validation, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Foundation of Chongqing, grant number cstc2020 jcyjmsxmX0914; the Specialized Research Fund for State Key Laboratories. The authors acknowledge the use of data from the Chinese Meridian Project.  
Not applicable.  
The raw airglow data can be obtained from the Chinese Meridian Project ([[http://data.meridianproject.ac.cn/en](http://data.meridianproject.ac.cn/en)]([http://data.meridianproject.ac.cn/en](http://data.meridianproject.ac.cn/en))).  
The authors declare no conflict of interest.
## 2 Antenna Model  
The proposed microstrip patch antenna is shown in Figure 1. It combines several key features to provide the desired operational performance at different frequencies, including LTE and 5G bands. Specifically, the proposed design comprises a rectangular microstrip patch with two concentric annular slots. Each ring has four spikes within it. This type of patch antenna is inspired by different works previously presented in the literature [20; 21; 22; 23; 24]. Additionally, the design contains a U-shaped aperture of rectangular slots to obtain the resonant frequency for the LTE band. U-slot patch antennas are well known mainly for their wide-band characteristics and are capable of providing other advantages, including dual band and triple-band operations, due to their ability to be implemented with other patch antenna shapes, such as circular, triangular, or rectangular shapes [25]. The proposed antenna is completed with three meander slots at the bottom of the ground plane, according to the study in reference [26]. Table 1 shows the variables involved in antenna design and the variable ranges considered in this case.  
Note that the optimization of the proposed antenna in order to be operational in the desired frequencies is a hard problem in which metaheuristic algorithms can obtain good results. Note also that the objective function must be calculated starting from the antenna simulation for a given set of antenna parameters. Thus, the hybridization of a simulation software with a meta-heuristic algorithm is necessary to tackle the optimization of this antenna. Specifically, the CRO-SL algorithm will be used as optimizer, since its characteristics of multi-method with different search operators may work fine in this problem. The CRO-SL will be hybridized with the CST simulation software in order to obtain the performance of each antenna in the CRO-SL evolution.  
Figure 1: Variables description of the proposed antenna. (**a**) Antenna top view U-shaped variable description; (**b**) antenna top view the annular patch variable description; (**c**) detail description of the variables in the top view of the antenna that have been used in the optimization process; (**d**) antenna ground plane.
# Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
## 4 Results, Discussion, and Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **Size** & **Ports** & **BW/GHz** & **Gain/dBi** & **Coup/dB** & **DG** & **ECC** & **D** & **Rec.** \\ \hline
[18] & \(130\times 100\) & 8 & 5.1–5.9 & 2.1 & \(-15\) & — & 0.05 & \(\lambda/1.9\) & Not \\
[19] & \(150\times 75\) & 4 & 2.45,5.8 & 2.5 & \(-14\) & 9.8 & 0.06 & \(\lambda/2\) & Not \\
[20] & \(136\times 60\) & 8 & 5.1–5.9 & 1.9 & \(-10\) & 9.3 & 0.09 & \(\lambda/2\) & Diode \\
[21] & \(150\times 75\) & 12 & 4.8–5.1 & 2.6 & \(-12\) & 9.7 & — & \(\lambda/2.1\) & Not \\
[22] & \(150\times 80\) & 8 & 5.1–5.9 & 2.2 & \(-10\) & 9.2 & 0.11 & \(\lambda/2.3\) & Not \\
[23] & \(133\times 133\) & 4 & 2.25 & 2.5 & \(-15\) & 9.3 & 0.10 & \(\lambda/2.1\) & Diode \\
[24] & \(160\times 70\) & 2 & 4.42 & 5.2 & \(-30\) & 10 & 0.06 & \(\lambda/1.4\) & Not \\
[25] & \(30\times 40\) & 4 & 2–3, 3.4–3.9, 4.4–5.2 & 6.3 & \(-20\) & 10 & 0.01 & \(\lambda/15\) & Not \\ This work & \(30\times 50\) & 2 & 3–5.5 & 4.5 & \(-20\) & 10 & 0.01 & 0.08\(\lambda\) & LDR \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The proposed antenna performance in comparison to published results.
# Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
## 2 Methodology
### Feature Extraction  
#### 2.3.1 Mel Spectrogram  
Mel spectrograms provide a valuable means of representing audio characteristics in diverse different classification tasks [34; 35; 36]. During a baby's cry, the acoustic properties of that sound carry information about its physiological and emotional state. The Mel spectrogram is a visual depiction of the frequency spectrum of an audio signal that is transformed onto the Mel scale. This scale is designed to simulate the way the human ear perceives different frequencies. Fundamentally, it serves as a mechanism for representing the energy composition across distinct frequency ranges, providing both temporal and spectral characteristics of an auditory stimulus. In the Mel spectrogram, the temporal aspect is represented by the X-axis, while the Y-axis represents the frequency. The intensity of color corresponds to the amplitude or power of the frequencies. Typically, the process involves a sequential execution of steps, beginning with segmenting the audio signal into short frames and then applying the Fourier Transform to convert the time-domain signal of each frame into the frequency domain. Subsequently, the Mel scale approximates the human ear response and is employed to partition the frequency spectrum into various bands [37]. The Mel spectrogram conversion can be mathematically represented as:  
\[STFT(cry\_recording)=\int cry\_recording(t)\cdot e^{-2\pi ift}\,dt \tag{1}\]  
\[Mel(STFT(cry\_recording))=\log\biggl{(}1+\frac{|S(f)|^{2}}{N_{MELS}}\biggr{)} \tag{2}\]  
where \(STFT\) represents the Short-Time Fourier Transform, \(t\) is the time variable, \(f\) is the frequency variable, and \(S(f)\) is the STFT magnitude. \(Mel\) denotes the Mel filterbank transformation, and \(N_{MELS}\) is the number of Mel filters used. The resulting Mel spectrogram serves as a more perceptually relevant depiction of sound; samples of cry sounds from the donate-a-cr dataset mapped to the Mel spectrogram scale are illustrated in Figure 1. The process of feature extraction using Mel spectrograms entails converting the preprocessed cry recordings into a more computationally comprehensible format, which enables the use of machine learning algorithms.  
Figure 1: \(Cont\).
## 4 Proposed System Design Overview
In the FT-CID, the TL model is associated with gradient-based weight generation for each local learning model and transfers the local learning model parameters, logits, and weights to the global learning model with the help of federated averaging (FedAVG)-based aggregation. Because the global model is trained using the transferred parameters, the FT-CID model optimises each local learning model by transferring knowledge, including the global parameters and logits, through TL in association with parameter transfer and knowledge transfer. Moreover, it selects the relevant source data to train the TL model with respect to the target domain of the IIoT network and thus optimises the local learning model with the assistance of the SGD model to detect intrusion samples in the heterogeneous IIoT networks.
## 5 Treatment
### Conservative Management  
Non-surgical management strategies for MRONJ typically encompass antiseptic rinsing and various pharmacological treatments. Although no systematic reviews on conservative management were identified in the studied period, a clinical case series study shed light on the potential of teriparatide (TPTD) as an adjunctive treatment. This study focused on patients with osteoporosis suffering from chronic, refractory MRONJ, who had not seen improvement with conservative treatment alone for at least two months [24].  
The study meticulously divided 27 patients into three distinct groups: one receiving daily TPTD treatment (20 \(\upmu\)g), another undergoing weekly TPTD treatment (56.5 \(\upmu\)g), and a third group serving as a non-TPTD control. Remarkably, the groups treated with TPTD exhibited a significantly shortened average healing time of 3.3 months, compared to the prolonged 11.9 months observed in the control group [24]. Moreover, the cure rate in the TPTD-treated groups reached 100%, a notable increase from the 67% cure rate in the non-TPTD group [24]. Despite these promising outcomes, it is worth noting that some patients receiving daily TPTD reported mild adverse effects, including palpitations, sickness, and headaches, leading to discontinuation of the treatment [24].  
In summary, the study highlights that when combined with standard conservative therapy, adjunctive TPTD therapy significantly reduces the healing time and enhances the cure rate for chronic, refractory MRONJ, offering a substantial improvement over conservative treatment alone [24]. However, it also underscores the necessity for more comprehensive research and collaborative efforts to confirm these results and further elucidate the role of TPTD in the treatment of MRONJ.
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 4 Applications
### Maintenance and Condition Monitoring
A system identification performed for the purpose of condition monitoring and fault prediction can be found in [9], as it showed the development of a DT fragment of an experimental autonomous ship. The DT development was based on a 1:20 scaled model of a ship being built. The identification was performed using proposed algorithms on data collected during a manoeuvre scenario that couples swaying, surging, and yawing motions.
## 2 Materials and Methods
### Case Study and Methods Comparison  
Experimental Field and Soil Sampling  
The trial was done in a 1-ha olive orchard (_Olea europaea_ L., cv. 'Maiatica'; 70-year-old plants with a distance of 8 \(\times\) 8 m; NE orientation) located in Ferrandina (Southern Italy, Basilicata region; N 40\({}^{\circ}\)29'; E 16\({}^{\circ}\)28\({}^{\prime}\)). The area has a semi-arid climate, with an annual rainfall of 560 mm (mean 1995-2018), concentrated mostly in the winter and a mean annual temperature of 16.3 \({}^{\circ}\)C (mean 1995-2018). The soil is a sandy loam, a Haplic Calcisol, according to the World Reference Base for Soil Resources, with a mean bulk density of 1.30 g cm\({}^{-3}\) and sediment as parental material.  
The orchard was managed accordingly to sustainable/conservation agricultural practices, namely: (a) minimum tillage and cover crop application (30 kg ha\({}^{-1}\) of _Trifolium subterraneum_ seeds and spontaneous grass); (b) guided fertilization based on plant nutrient demand evaluated by leaf mineral analyses and on soil measured nitrogen levels; (c) compost amendment (15 t ha\({}^{-1}\) fresh weight); (d) incorporation of cover crop and pruning residues into the soil (light narrowing at a depth of 10 cm carried out in Autumn); (e) guided drip irrigation based on crop evapotranspiration (3 drip emitters per plant along the tree lines with a capacity of 4 L h\({}^{-1}\) each); (f) pruning aimed to vegetative-productive equilibrium of plants (winter pruning based on the selection of shoots with a high number of floral buds and on a better light interception in the canopy).  
In October 2018, soil sampling was done in the undisturbed inter-row area. Soil sub-samples were picked in 10 points over a 1-m radius area around each olive tree from the topsoil layer (0-10 cm) for bacterial communities' analysis by Biology(r) assay. The 10 sub-samples were pooled on site to constitute a composite soil sample of about 1 kg. Five composite samples (\(n\) = 5), each composed of 10 different sub-samples, were prepared, in order minimize spatial variability. After removing visible crop residues, roots and pebbles with sterile tweezers and slightly mixing and homogenizing with a sterile spatula, the soil composite samples were immediately stored in sterilized plastic bags at 4 \({}^{\circ}\)C and subsequently analysed within 5 days using the procedure described in the previous paragraph.
## 4 SpMV and Gauss-Seidel on Matrix-DSP
### DMA Double Buffering Method  
The Matrix-DSP accelerator supports transferring data and computation simultaneously, so we designed the DMA double-buffering method in the AM space to overlap the computation and transfer. Figures 8 and 9,b describe the flow of the conventional memory access calculation and the flow of the double buffer memory access calculation, respectively. We created two buffers for the \(Vector\_K\), \(Vector\_xi\), \(Vector\_ax\) in the SpMV and for the \(Vector\_K\), \(Vector\_xi\), \(Vector\_diag\), \(Vector\_F\), \(Vector\_x\) in the Gauss-Seidel. Therefore, all computation time, except the last one, can be overlapped by the transfer time. In both algorithms, the DMA memory access time accounts for a large proportion, and the calculation time accounts for a small proportion, so the performance improvement is limited.
## 2 3D Burden Surface Feature Parameter Definition
### 2.1. Phased Array Radar  
BF ironmaking is a typical \"black box\" system with nonlinearity, time lag, and noise. Despite the substantial number of inspection devices installed, measurement of the 3D burden surface is still difficult to achieve. If this point can be broken, it will improve the level of charge control and help the burden surface research.  
The traditional detection method is limited by data points, which cannot fully reflect the actual situation of the burden surface. Industrial phased array radar detection technology can scan the entire burden surface of the BF, which has the characteristics of flexible beam direction, strong adaptability to complex environments, and good anti-interference performance [16]. [PERSON] successfully solved the problem of direct measurement of the burden surface by developing a new phased array radar measuring device and increasing the detection point to more than 2000 points [17]. The high-resolution image of the burden surface acquired by the phased array radar is shown in Figure 1. From bottom to top, each burden surface corresponds to a charging process.  
From the figure, the height of the burden surface and the position of the furnace wall can be found. The burden surface fluctuates in various places, and the furnace wall can also have an impact. Through the multi-point detection technology of industrial phased array radar, the entire burden surface's height information can be obtained. Compared with other radar detection methods, phased array radar can obtain more detection data, and the 3D burden surface contains more shape information, providing a data basis for subsequent research.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 4 Experimental Results and Analysis  
This section demonstrates the performance of the proposed calibration method for bistatic ISAR imaging systems using real calibration satellite echoes and simulated target echoes.
## 4 Proposed Approaches
### Accumulated Ratio of Eigenvalues Gaps  
The main idea of AREG is to detect the NS gap using a ratio of the NS gap to the NN gap. The simplest way to detect the NS gap is computing \(\Delta\lambda_{i}\) with \(i\) in ascending order and then the first non-zero \(\Delta\lambda_{i}\) will be the NS gap. In practice, however, this simplest way cannot be applied because NN gaps are not exactly zero. Nonetheless, NN gaps are probably closer to zero than the NS gap is; the NS gap can be found using the maximum value of the ratios of the eigenvalues gaps because the NN gaps are comparatively close to zero.  
AREG is defined as follows:  
\[\text{AREG}(i)\triangleq\lim_{\delta\rightarrow+0}\frac{\Delta\lambda_{i+1}}{ \frac{1}{i}\sum_{k=1}^{i}\Delta\lambda_{k}+\delta}, \tag{14}\]  
where \(i=1,\ldots,M-2\). From (10) and (14), when \(1\leq D\leq M-3\), we have  
\[\text{AREG}(i)=\begin{cases}0,&i=1,\ldots,M-D-2,\\ +\infty,&i=M-D-1,\\ c_{i},&i=M-D,\ldots,M-2.\end{cases} \tag{15}\]  
When \(D=M-2\), we have  
\[\text{AREG}(i)=\begin{cases}+\infty,&i=1,\\ c_{i},&i=2,\ldots,M-2.\end{cases} \tag{16}\]  
Note that \(c_{i}\) is a real number satisfying \(0\leq c_{i}<+\infty\).  
According to (15) and (16), the source enumeration can be performed by the following criterion:  
\[\hat{D}=M-1-\underset{i=1,\ldots,M-2}{\text{argmax}}\ \text{AREG}(i), \tag{17}\]  
where \(\hat{D}\) is the estimated number of sources.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Question** & **Response** & \begin{tabular}{c} **Question** \\ **Processing** \\ **Time (Pq) [s]** \\ \end{tabular} & \begin{tabular}{c} **Cache Time** \\ **(Pc) [s]** \\ \end{tabular} & \begin{tabular}{c} **Cloud Time** \\ **(Pi) [s]** \\ \end{tabular} & \begin{tabular}{c} **Total Query** \\ **Response** \\ **Time [s]** \\ \end{tabular} \\ \hline Q1 & \begin{tabular}{c} Cloud \\ Cache \\ \end{tabular} & 0.016 & 1.621 & 6.250 & 7.887 \\ Q2 & \begin{tabular}{c} Cloud \\ Cache \\ \end{tabular} & 0.006 & 0.045 & & 0.051 \\ \hline Q3 & \begin{tabular}{c} Cloud \\ Cache \\ \end{tabular} & 0.022 & 0.031 & 3.539 & 3.592 \\ Q3 & \begin{tabular}{c} Cache \\ \end{tabular} & 0.006 & 0.029 & & 0.035 \\ \hline Q4 & \begin{tabular}{c} Cloud \\ Cache \\ \end{tabular} & 0.009 & 0.029 & 6.294 & 6.332 \\ Q5 & \begin{tabular}{c} Cloud \\ Cache \\ \end{tabular} & 0.009 & 0.027 & & 0.036 \\ \hline Q5 & \
\begin{tabular}{c} Cloud \\ Cache \\ \end{tabular} & 0.016 & 0.036 & 3.040 & 3.092 \\ Q6 &
\begin{tabular}{c} Cache \\ \end{tabular} & 0.006 & 0.610 & & 0.616 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of response times from cache and cloud. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Joules (Energy**} & \multicolumn{2}{c}{**Joules (Energy**} & \multicolumn{2}{c}{**Joules (Energy**} \\
**Test** & **Consumption)** & **Raspberry Pi** & **Consumption) VA1** & **Consumption) VA2** \\ \cline{2-6}  & **Cache** & **Cloud** & **Cache** & **Cloud** & **Cache** & **Cloud** \\ \hline Q1 & 0.16 & 24.84 & 0.19 & 29.97 & 0.28 & 43.37 \\ \hline Q2 & 0.11 & 11.53 & 0.13 & 13.64 & 0.19 & 19.75 \\ \hline Q3 & 0.094 & 14.93 & 0.11 & 18.13 & 0.16 & 26.24 \\ \hline Q4 & 0.11 & 19.93 & 0.13 & 24.06 & 0.19 & 34.82 \\ \hline Q5 & 1.9 & 9.55 & 2.34 & 11.74 & 3.38 & 17.00 \\ \hline Total & 2.38 & 80.78 & 2.9 & 97.54 & 4.2 & 141.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Energy consumption on prototype vs. expected energy consumption on initials VAs. Subsequently, the metrics of the proposed mathematical model were verified using the average values measured in the prototype; \(P_{p}\) is defined as the average power used in the query, cache, Internet, and response processes. These metrics yield the following numerical results:  
The metric for the energy consumption of queries performed in the cloud (External query):  
\[ \begin{split} M_{C}=1-\frac{P_{p}\left(\Delta t_{q}+\Delta t_{c}+ \Delta t_{i}\right)}{P_{N}\left(\Delta t_{q}+\Delta t_{c}+\Delta t_{i}\right)} \\ M_{C}=0.21\end{split}  \tag{20}\]  
The metric for the energy consumption of queries performed in the cache (Local query):  
\[ \begin{split} M_{C_{L}}=1-\frac{P_{p}\left(\Delta t_{q}+\Delta t _{c}\right)}{P_{N}\left(\Delta t_{q}+\Delta t_{c}+\Delta t_{i}\right)}\\ M_{C_{L}}=0.99\end{split}  \tag{21}\]  
This result shows that the difference between the consumption metrics for the cache tests and the cloud tests is 0.78.