esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 5 Further Analysis  
### Freeze Different Layers  
A common method in transfer learning is to freeze some layers, fix their parameters, and fine-tuning updates the parameters of other layers. In this experiment, we, respectively,  
Figure 10: The test angle error distribution before and after the transfer learning. (**a**) Before transfer learning. (**b**) After transfer learning.  
Figure 9: Comparison of the test angle and actual angle of the model before and after transfer learning. (**a**) Before transfer learning. (**b**) After transfer learning.  
froze the first 0, 1, 2, 3, 4, and 5 convolutional layers. Freezing the first 0 convolutional layers will open all network layers for fine-tuning. Freezing the first five convolutional layers will freeze all convolutional layers, and fine-tuning updates the parameters of the fully connected layer. Freeze different network layers and use the same training dataset \(T_{2}\) so that fine-tuning may train the model. Test results on the four evaluation metrics are as shown in Table 4.  
We can observe from Table 4 that when different network layers are frozen, the test results of the model are different. When the first convolutional layer is frozen and the next four convolutional layers and the fully connected layer are fine-tuning trained, the test result is better than other freezing approaches. Although the maximum angle error is 4.02\({}^{\circ}\), which is higher than the minimum value of 3.775\({}^{\circ}\), this may be due to the large individual error caused by the randomness of the samples. When all the convolutional layers are frozen and only the fully connected layer is fine-tuning trained, the performance of the model is much lower.  
From the above test results, we can draw inferences as follows:  
1. The convolutional layer is the main feature extraction module, and the fully connected layer is the classification module. When all convolutional layers are frozen, the test results of the model are poor, while when some convolutional layers are opened, the test results of the model are significantly improved. This is because the feature extraction module is needed to be fine-tuned when training samples are transferred from the ideal condition to the actual condition.
2. In CNN, different convolutional layers extract different features from the input data. The first few convolutional layers extract the common features (bottom features) of the data, and the last few extract the individual features (high-level features), and the transition from the common features to the individual features occurs on some middle layers.
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 3 Ophthalmological Examination: A Key Tool in the Diagnosis and Management of Sphingolipidoses
### Electroretinography  
ERG records the retinal electrical response to light stimuli. A flash or patterned stimulus generates a biphasic waveform, with the initial negative a-wave originating from photoreceptors (rods and cones) and the subsequent positive b-wave arising from inner retinal layers [48].  
Different ERG modalities serve distinct purposes. Full-field ERG (ffERG) is valuable for detecting widespread retinal dysfunction, aiding in the diagnosis of conditions such as diffuse retinopathies [49]. mfERG, on the other hand, provides a topographic map of retinal function, pinpointing localized defects within specific retinal layers and regions [50]. In sphingolipidoses, the ERG is useful for detecting early functional retinal impairment in the absence of structural signs, often preceding the onset of manifest anatomical alterations. In contrast, this examination may fail to reveal abnormalities even in the presence of early retinal alterations [19].  
A limitation of conventional ffERG is that it provides a cumulative response from the entire retina. Consequently, unless a pathological condition affects at least 20% of the retina, the ERG results usually remain within normal thresholds [48].
# Effect of the Transition Points Mismatch on Quanta Image Sensors  
[PERSON]  
1 School of Microelectronics, Tianjin University and Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, No. 92 Weijin Road, Nankai District, Tianjin 300072, China; [EMAIL_ADDRESS] (J.X.); [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (K.N.); [EMAIL_ADDRESS] (L.X.); [EMAIL_ADDRESS] (J.M.)  
[PERSON]  
1 School of Microelectronics, Tianjin University and Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, No. 92 Weijin Road, Nankai District, Tianjin 300072, China; [EMAIL_ADDRESS] (J.X.); [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (K.N.); [EMAIL_ADDRESS] (L.X.); [EMAIL_ADDRESS] (J.M.)  
[PERSON]  
2 School of Electrical and Information Engineering, Tianjin University and Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, No. 92 Weijin Road, Nankai District, Tianjin 300072, China  
The influence of ADC transition points has been simply mentioned in [12], and this paper would go further and discuss the non-uniformity of ADC transition points. Transition points of fabricated ADCs always deviate from their design values, and, thus, the quantization process does not execute ideally. So, the relationship between the mismatch of ADC transition points and the BER needs to be investigated for further QIS design.  
In fact, the voltage signal that needs to be resolved in a QIS is only a few hundred microvolts, so it is better to implement a pre-amplifier before the ADC no matter whether the QIS is single-bit or multi-bit [13]. A pre-amplifier suffers from an unavoidable input-referred offset that is caused by the device mismatch, so the transition points mismatch of well-calibrated ADC mainly comes from the input-referred offset of the pre-amplifier. In [14], a charge transfer amplifier is reported to have about a \(\pm 1\) mV offset. It is good enough in conventional applications. However, due to the extremely small jet signal, a QIS has a stricter requirement for ADC transition points that result from the pre-amplifier. Bit errors caused by column [15] or cluster [5] parallel ADCs may lead to stripes or plaques in the final image.  
This work exhibits BER as a function of ADC transition points mismatch. It would guide the design of the ADC in the future. The imaging model shows that the ADC design process for a QIS could be flexible according to the spatial and temporal oversampling factors.  
The remainder of this paper is organized as follows. Section 2 presents mathematical models for the single-bit QIS and the multi-bit QIS to show the relationship between ADC transition points and the BER. Section 3 presents imaging models on the basis of the mathematical models. The imaging models are used to observe the imaging quality of a QIS intuitively. Section 4 concludes the paper.
## 1 Introduction
### Reactive Power Support for Voltage Regulation
#### 3.4.1 Proposed Definition and Its Justification
It is also reminded that voltage regulation refers to the positive sequence and fundamental frequency component of the voltage. In the same context, recent standards propose that the applicable voltages for DRES reactive power control are quantified in one of the following ways:  
IEEE 1547-2018 Std [10]: (i) the average of the RMS values; (ii) positive-sequence component of voltages over one fundamental frequency period. EN 50549 Stds. [11; 12]: (i) the positive sequence of the symmetrical components; (ii) the average of voltages of a three-phase system; (iii) the voltage of every phase to determine the reactive power for every phase.
# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 6 Visual Adaption Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Conveyor Moving Speed**} & \multicolumn{1}{c}{**Speed**} & \multicolumn{1}{c}{**Speed**} & \multicolumn{1}{c}{**Speed**} \\  & **20 mm/s** & **40 mm/s** & **60 mm/s** & **70 mm/s** \\ \hline \multirow{3}{*}{Square} & Moving Distance (m) & 0.133 & 0.245 & 0.326 & 0.430 \\  & Standard Deviation (m) & 0.008 & 0.020 & 0.020 & 0.034 \\  & Success Rate (\%) & 100\% & 100\% & 100\% & 70\% \\ \hline \multirow{3}{*}{Circle} & Moving Distance (m) & 0.132 & 0.247 & 0.315 & 0.429 \\  & Standard Deviation (m) & 0.007 & 0.013 & 0.025 & 0.022 \\  & Success Rate (\%) & 100\% & 100\% & 100\% & 85\% \\ \hline \multirow{3}{*}{Rectangle} & Moving Distance (m) & 0.131 & 0.244 & 0.315 & 0.411 \\  & Standard Deviation (m) & 0.008 & 0.017 & 0.021 & 0.021 \\ \cline{1-1}  & Success Rate (\%) & 100\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Visual servoing results at different conveyor speeds. Figure 27: Comparison of speed and position of the TCP in tracking task. Consequently, the gripper can grasp different types of objects of any shape in any orientation, regardless of whether the object is moving or stationary. Figure 28 shows the vision tracking results for two different objects moving on a conveyor. With the vision adaptive function, the general adaptive gripper can not only grasp irregular objects but also moving targets.
## 6 Analysis of Results
### Definition of the Main Acoustic Parameters  
Before the analysis of the main acoustic parameters, it is important to explain the main characteristics of them.  
Figure 5: Comparison of scenarios A to E applied to the Roman theatre of Gubbio in relation to the acoustic parameters: EDT without audience (**a**), EDT with audience (**b**), T\({}_{20}\) without audience (**c**), T\({}_{20}\) with audience (**d**), D\({}_{50}\) without audience (**e**), and D\({}_{50}\) with audience (**f**).  
* The early decay time (EDT) is computed by considering the direct sound and the early reflections occurring between 0 dB and \(-\)10 dB of the decay, after the interruption of the sound impulse [34]. This computation is based on the integrated Schroder curve.
* The reverberation time is related to the time for which the sound energy will be absorbed by the surfaces of a room. To determine the optimal range of reverberation time, the room function, or destination of use, is of primary importance [35].
* The definition (D\({}_{50}\)) was introduced as a parameter to link the energy level and the delay in late reflections with respect to direct sound [36]. The optimal values for speech definition can range from 50% to 100%, while, for music, the values should range from 0% to 50%.
* The clarity index is an acoustic parameter based on the ratio between the sound energy arriving within 50 ms (for speech) or 80 ms (for music) and the sound energy arriving in the following decay instants. Clarity can be compared to the transparency of the perceived signal, having the optimal value fixed at 0 dB, representing the best balance, with some tolerance ranging from \(-\)2 to +2 dB [37; 38].
* Another acoustic parameter that is a function of the position assumed in the seating area is strength (G), which is directly related to the power level of the sound source [39]. In general, for outdoor spaces, G can vary between 0 dB and 6-7 dB, while, for enclosed spaces, G can be higher, depending on the surface finishes and the volume of the space.
## 5 Comparison of Predicted and Measured Values Using a Prediction Model  
### Prediction Model  
The change over time in the concentration of gas in a room from its initial state is generally expressed by the following Equation (1), when the amount of gas generated is constant [14].  
\[C=C_{0}+\frac{M}{NV}(1-e^{-Nt}) \tag{1}\]  
\(C\) is concentration of CO\({}_{2}\) in indoor air (mg/m\({}^{3}\)), \(C_{0}\) is concentration of target air outside (mg/m\({}^{3}\)), \(t\) is elapsed time (h), \(V\) is studio volume (m\({}^{3}\)), \(M\) is amount of gas generated (mg/h), \(N\) is number of ventilation cycle (cycle/h). The ventilation cycle is defined as the ventilation amount (m\({}^{3}\)/h) of a room divided by the volume (m\({}^{3}\)).  
In this study, there are deals with CO\({}_{2}\) diffused from human exhaled air, and the concentration is expressed as volume concentration (ppm). Therefore, if we rearrange the equation to align the units, we obtain Equation (2) ([PERSON]'s equation).  
\[C=(C_{0}+\frac{nE}{NV}(1-e^{-Nt}))\times 10^{6} \tag{2}\]  
\(C\) is concentration of CO\({}_{2}\) in indoor air (ppm), \(n\) is number of subjects in the room, \(E\) is CO\({}_{2}\) emission per subject (m\({}^{3}\)/h).  
In the prediction model, the concentration of CO\({}_{2}\) increases in an almost linear manner at the beginning of the measurement, but as time passes, it ascends to a constant concentration. In this study, Equation (2) is used as a prediction model for the CO\({}_{2}\) concentration in a room, and the predicted CO\({}_{2}\) concentration until the steady state is reached is evaluated by comparing the predicted values with the actual values measured by simultaneous multipoint sensing of the CO\({}_{2}\) concentration sensor.
# A New Stochastic Process of Prestack Inversion for Rock Property Estimation
## 2 Methods
### Adaptive Particle Swarm Optimization
4: Compare all distances and sort the maximum distance and minimum distance to compute the evolutionary factor. An evolutionary factor \(f\) is defined as follows,  
\[f=\frac{d_{g}-d_{\min}}{d_{\max}-d_{\min}}\in[0,1] \tag{5}\]  
Here, \(d_{g}\) is the global best of \(d_{i}\), and \(f\) is initialized to 1 at the start of process. 5: Define the adaptive inertia weight \(\omega\) and then adjust parameters to balance the ability of searching local minimum and global minimum by  
\[\omega(f)=\frac{1}{1+1.5e^{-2\xi f}}\in[0.4,0.9],\forall f\in[0,1] \tag{6}\]  
\(\omega(f)\) is decreasing from 0.9 to 0.4 in each generation. Hence, \(\omega(f)\) is expected to vary with more efficiency than PSO due to evolutionary states. Since \(f\) is initialized to 1, \(\omega\) is therefore initialized to 0.9 at the beginning. 6: Define the acceleration coefficients \(c_{1}\) and \(c_{2}\), as follows. The values of \(c_{1}\) and \(c_{2}\) are changing in each generation, with larger \(c_{1}\) and smaller \(c_{2}\) at the beginning, and smaller \(c_{1}\) and larger \(c_{2}\) at the end for convergence. \[c_{t}(i+1)=c_{t}(\text{i})\pm\delta,t=1,2 \tag{7}\]  
Note that \(c_{1}\) and \(c_{2}\) are changing in a range [1.5, 2.5] and their summation is within [3.0, 4.0] in order to adjust between local minimum and global minimum. While in conventional PSO, \(c_{1}\) and \(c_{2}\) are equal to 2 without the ability of jump-out search. \(\delta\) is a uniformly generated random value in a range [0.05, 0.1]. 7: Elitist learning strategy (ELS) for global best. After identifying the global best from each generation, it will be assigned with slight momentum to move around using a learning strategy via Gaussian perturbation as follows,  
\[p^{k+1}=p^{k}+(X_{\max}^{k}-X_{\min}^{k})\times Gauss(\mu,\sigma^{2}) \tag{8}\]  
where \(Gauss(\mu,\sigma^{2})\) represents Gaussian distribution with a mean \(\mu\) and a time-varying standard deviation \(\sigma\). \(X_{\max}^{k}\) and \(X_{\min}^{k}\) can be estimated from model space. In impedance inversion, assuming that models obey Gaussian distribution,  
\[\sigma=\sigma_{\max}-(\sigma_{\max}-\sigma_{\min})\times(g/G) \tag{9}\]  
where \(\sigma_{\max}=1.0\) and \(\sigma_{\min}=0.1\) learnt from empirical study. \(i\) is the current iteration number. \(G\) is the total number of iterations. Note that the new \(p^{k}\) will be accepted only if it has a better fitness than the current position. 8: Set the stopping criterion that the _Gbest_ is satisfied when the predefined minimum value or \(G\) reach the maximum number of iterations. If _Gbest_ is satisfied, then output the global best after iteration. If it is not satisfied, then return, as shown in Figure 1, until it is satisfied. Therefore, the ASPO could ensure the effect of finding the global best value.
## 4 Discussion
### Analysis of the Applicability of Multisource SIF Data in Estimating GPP
[66], who utilized comparative experiments to demonstrate CO\({}_{2}\) characteristics in the Taklimakan Desert. This indicates that the overall measurability of SIF in the desert vegetation area is relatively low, thus resulting in a weaker applicability of SIF data for GPP estimation on these underlying surfaces. In this study, in the desert vegetation area (Gurbantunggut Desert), the GPP during the growing season of short-lived vegetation shows a trend of increasing initially and then decreasing. Outside the growing season, the interannual variation of GPP shows an inverted \"U\" shape and indicates a carbon sink. The interannual variations of GPP and SIF are relatively complex, with small overall accumulations of GPP and contributions of SIF. This further validates the aforementioned research conclusion and is consistent with the findings of [PERSON] et al. [67] regarding CO\({}_{2}\) fluxes in the Gurbantunggut Desert. This study validated the applicability of SIF data for GPP estimation in arid regions, the findings of this research are crucial for subsequent comprehensive estimation and feature analysis of GPP in Xinjiang and even the entire arid region based on SIF data.
## 3 Methodology
### Encoder-Only Forecasting  
TSNN-ESTD takes the advantages of diverse time-series forecasters into consideration and integrates both encoder-only and encoder-decoder architectures. The encoder-only forecaster is consistent with the iTransformer. Instead of aggregating different variables with the same timestamp, all variables with different time steps are regarded as the independent input tokens \(h_{i}^{0}\) in the embedding procedure. Specifically, the embedding layer establishes the mapping from \(\mathbb{R}^{\text{T}}\) to \(\mathbb{R}^{\text{D}}\), where \(\{X_{1i},X_{2i},\dots,X_{Ti}\},\ i\in[1,N]\) denotes the values of the \(i\)-th variable with different timestamps. Subsequently, the tokens of a sequence of the \(i\)-th variables are computed as the input layer in Equations (1) and (2).  
\[h_{i}^{0}=Embedding(X_{1i},X_{2i},\dots,\ X_{Ti}) \tag{1}\]  
\[H=\{h_{1},h_{2},\dots,h_{N}\}\in\mathbb{R}^{N\times\text{D}} \tag{2}\]  
In addition, the acquired variate tokens engage in interactions through self-attention mechanisms and are processed independently by the shared feed-forward network within each TrmBlock, as shown in Equation (3). The structure of TrmBlock is consistent with that of the classic Transformer [28].  
\[H^{l+1}=TrmBlock\Big{(}H^{l}\Big{)} \tag{3}\]  
Afterward, the multi-layer perceptron (MLP) as the projection layer is conducted to align the high-level embedding \(H\in\mathbb{R}^{N\times D}\) to the prediction \(H\in\mathbb{R}^{N\times L}\), i.e., the value of \(N\) variable at future \(L\) time steps. Notable, multivariate time-series data for long time steps are forecasted in a straightforward manner, which is formalized as \(Y1_{Long}\in\mathbb{R}^{N\times\text{L}}\) in Equation (4).  
\[Y1_{Long}=Projection\Big{(}H^{l+1}\Big{)} \tag{4}\]  
The encoder-only model is trained independently with the mean squared error (MSE) loss function, which quantifies the discrepancy between the predicted values and the ground truth, as illustrated in Equation (5). Here, \(N_{L}\) represents the number of samples, \(Y_{i}\) denotes the ground truth for the \(i\)-th sample, and \(Y1_{Long_{i}}\) is the final forecast value for the \(i\)-th sample.  
\[Loss_{1}=\frac{1}{N_{L}}\sum_{i=1}^{N_{L}}\big{(}Y_{i}-Y1_{Long_{i}}\big{)}^{2} \tag{5}\]
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 2 Materials and Methods
### Hyperspectral Imaging and Data Analysis  
#### 2.3.1 Leaf Hyperspectral Measurement  
A portable hyperspectral camera (SIS-I, EBA Japan Co., LTD., Tokyo), standard reflector, and halogen lamp (CHP-500, Caster) were employed to obtain the spectral data of the tomato leaves from the bottom, middle, and top leaf positions. The wavelength range of the hyperspectral camera is 900 nm to 1700 nm, which is within the near-infrared (NIR) region. A dedicated NIR CCTV lens was set up in front of the camera using a C mount. A standard reflector (White Balance, X-Rite) and a halogen lamp were adopted in this experiment to reduce experimental errors. The hyperspectral image data of the terminal leaflet were immediately obtained after measuring the FW by using NIR Capture software (SIS-I, EBA Japan Co., LTD., Tokyo). The camera was set to a frame rate below 80 fps and to the maximum exposure time of 4.5 ms. The spectral resolution and image resolution of the hyperspectral camera were 10 nm and 400 \(\times\) 320 pixels, respectively.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 7 Conclusions  
In this paper, an enhanced ALPR system is proposed, constructed, and successfully evaluated utilizing a dataset of 500 license plate images for parked vehicles as well as vehicles in movement. To identify possible margins of improvement in each of the system components, a systematic ceiling analysis is first performed. Several enhancements to the detection and segmentation components are then suggested and implemented based on the obtained results. The main enhancements are (i) vertical-edge histogram analysis, (ii) size estimation of candidate regions and (iii) de-rotation of misaligned license plate photos. When compared to an existing similar ALPR system, these additions have resulted in significant performance gains. The detection accuracy has increased from 82% to 94.4%, the segmentation accuracy has increased from 74.4% to 91.9%, and the recognition accuracy has increased from 68.5% to 91.5%. In addition, the total execution time of the developed algorithm is determined to be comparable to that of the prior system despite the inclusion of additional processing stages. This is achieved by improving the programming structure of the algorithm and reducing the number of potential regions of interest. The overall performance of the developed ALPR system, however, is still open for further developments by utilizing more advanced machine learning approaches that can learn and make intelligent decisions on their own rather than making informed decisions based on what it has learnt. The authors are currently investigating such enhancements, as well as others.  
Author Contributions: Conceptualization, A.A. and M.A.-Y.; Data curation, S.H.; Formal analysis, M.A.-T. and M.A.-Y.; Investigation, H.A.M.; Methodology, H.A.M. and M.A.-Y.; Resources, S.H.; Software, H.A.M.; Supervision, M.A.-T.; Validation, A.A. and M.A.-Y.; Visualization, S.H. and H.A.M.; Writing--Original draft, H.A.M. and M.A.-Y.; Writing--Review & editing, A.A. and M.A.-Y. Supervision, M.A.-Y. Writing--review & editing, A.A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.