esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 3 Energy-Efficient Amplifiers Based on QFG Techniques
### AC-Coupled Amplifiers  
AC-coupled amplifiers allow accurate amplification (set by capacitance ratios), providing, at the same time, blocking of near-DC inputs. They are widely used to remove electrode offsets in physiological signals and also in interface circuits for monitoring of seismic activity or mechanical vibrations. For instance, electrode-skin interfaces may lead to offsets larger than 1 V [6], which can limit dynamic range and even saturate the amplifier if they are not removed.  
Efficient AC-coupled amplifier implementations can be made using QFG input transistors, as shown in Figure 5[5,6]. The circuit of Figure 5a can be used for singled-ended input and output, while Figure 5b provides single-ended output for differential inputs. The circuits of Figure 5c,d are fully differential topologies. Note that one (Figure 5a) or two (the other topologies) two-input QFG transistors are employed at the amplifier input. The QFG input capacitor C\({}_{1}\) goes to the input terminal, and the other QFG input capacitor C\({}_{2}\) is connected to the output. The midband gain is, in all cases, C\({}_{2}\)/C\({}_{1}\). Note that the QFG pseudo-resistor is connected to the output in Figure 5a-c to provide DC-negative feedback. This is not the case in Figure 5d, where the amplifier is in open loop in DC. Hence, adequate design is required, in this case, to avoid saturation of the output [5]. An advantage of Figure 5d is that the amplifier DC input \(V_{BIAS}\) can be set to a supply rail, so the circuit can work with ultra-low-supply voltages. Another advantage is that the pseudo-resistor experiences little voltage swings, so a simple pseudo-resistor implementation can be used [5].  
Figure 5: AC-coupled amplifiers. (**a**) Single-ended input and output. (**b**) Differential input and single-ended output. (**c**) Fully differential. (**d**) Alternative fully differential.
# Diagnostic Accuracy and Agreement Between AI and Clinicians in Orthodontic 3D Model Analysis
###### Abstract  
Background: Artificial intelligence (AI) is increasingly integrated into orthodontic workflows, including digital model analysis modules embedded in orthodontic software. While these systems offer efficiency and automation, the accuracy and clinical reliability of AI-generated measurements and diagnostic assessments remain unclear. Therefore, to use AI systems safely and effectively in clinical orthodontics, it is important to check their results by comparing them with those of experienced orthodontists. Methods: Digital models of 48 patients were analyzed by the Orthodontist group and two AI platforms: Titan (full) and SoftSmile (Bolton only). Three orthodontists independently measured all variables using 3 Shape OrthoAnalyzer, and group means were used for comparison. A subset of models was reanalyzed after two weeks to assess consistency. Data distribution was evaluated, and appropriate statistical tests were applied. Reliability was assessed using intraclass correlation coefficients (ICC) and Cohen's kappa. Results: Almost perfect agreement was observed between the orthodontists and Titan AI in molar classification (\(\kappa\) = 0.955 right, \(\kappa\) = 0.900 left; \(p\) < 0.001), with perfect agreement reported across all groups--including between the orthodontists themselves--for Angle classification (\(\kappa\) = 1.00). In anterior and overall Bolton analyses, no meaningful agreement was found between the orthodontists and AI platforms. However, in a subset of patients where all three methods identified the tooth size discrepancy in the same arch (either maxilla or mandible), no significant differences were found in anterior (\(p\) = 0.226) or overall Bolton values (\(p\) = 0.795). Overjet, overbite, and space analysis values showed significant differences between the orthodontist and Titan groups (\(p\) < 0.001). ICC analysis indicated good to excellent intra- and inter-rater reliability within the orthodontist group (\(\geq\)0.77), while both AI systems demonstrated excellent internal consistency, with ICC values exceeding 0.95. Conclusions: AI-based platforms showed high agreement with orthodontists only in Angle classification. While their performance in Bolton analysis was limited, significant differences were observed in other linear measurements, indicating the need for further refinement before clinical use.  
digital orthodontics; model analysis; AI-based diagnosis; titan dental design; SoftSmile ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 7 Signal Processing:
### Single-Degree-of-Freedom Displacement Response  
The system parameters were also identified using the displacement response, as the system was released from a known initial displacement that was easily identifiable from the response. The displacement response was obtained by integrating the filtered acceleration data twice and removing DC components and numerically generated noise with detrending and filtering at each step. The initial displacement values for vertical and torsional degrees of freedom can be found from the initial data points of the time histories, as shown in Figure 14.  
Figure 12: Recorded response of SDOF pitch: (**a**) raw acceleration time history at \(U=0\) m/s; (**b**) raw acceleration time history at \(U=10\) m/s.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 8 Correlation of Natural Frequencies with Temperature and Axial Force
### Regressions between Natural Frequencies and Temperature or Axial Force  
The regressions between the identified raw frequencies and temperature and between the identified raw frequencies and axial force are shown in Figure 9. The raw frequencies used for the graphs are obtained with the peak-picking method for Mode 1. Results of all data sets for the first three vibration modes are similar to those shown in Figure 9, regardless of the adopted approach (peak-picking, Cov-SSI and PolyMAX); therefore, the following considerations can be applied to all results.  
The following considerations can be drawn from the linear regression results:  
* discretizing the data set in 5-min or 10-min records leads to almost equal correlation results: 10-min records give generally better \(R^{2}\) values of the regression than 5-min ones, but the differences are negligible.
* The correlations between ambient temperature and the frequencies show different and distinct regressions for each dataset for both beams.
* The correlations between the axial force and the identified frequencies for Beam 1 present distinct regressions for each dataset, except for the two datasets where the same damage is simulated (same additional mass). Beam 2 (undamaged) shows the same behavior in each dataset. Thus, correlations between axial force and frequencies are much better than correlations between temperature and frequencies, as will be explained in detail in the next paragraph.  
Figure 9: Linear regressions between frequencies and ambient temperature, and between frequencies and axial force. (**a**) Beam 1, discrete datasets in 5-min intervals, correlation with temperature. (**b**) Beam 1, discretization datasets in 5-min intervals, correlation with axial force. (**c**) Beam 1, discretization datasets in 10-min intervals, correlation with temperature. (**d**) Beam 1, discretization datasets in 10-min intervals, correlation with axial force. (**e**) Beam 2, discretization datasets in 5-min intervals, correlation with temperature. (**f**) Beam 2, discretization datasets in 5-min intervals, correlation with axial force. (**g**) Beam 2, discretization datasets in 10-min intervals, correlation with temperature. (**h**) Beam 1, discretization datasets in 10-min intervals, correlation with axial force.
## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
The output is stored on the host party, which is separate from the three guest parties. The overall distributed scenario is also described in Figure 8.

#### 4.1.2 Existing Methods Used for Verification

Including the proposed TT-VFDL-ST, the following methods described in Figure 8 are used for verification. Each method is selected since the distributed mechanism of it is one of the representative mechanisms of existing research. Since our study was conducted from the perspective of the distributed learning mechanism, our experiments are also designed to analyze the proposed TT-VFDL-ST compared to other representative vertical federated learning mechanisms. As mentioned in Section 2.3, from the perspective of distributed learning mechanisms, even the latest vertical federated learning methods [29; 30; 31]

Figure 8: Implementation of representative mechanisms of existing methods and proposed methods under scenarios of integrated IoT services construction. fall into one of the groups we classified. Therefore, methods that are implementations of representative mechanisms of vertical federated learning methods were selected as follows. * **TT-VFDL-ST:** The proposed method. It takes our previous design approach in [7] but the newly proposed self-transfer partial training mechanism is adopted to derive a single consistent method with advanced performance. \"TT-VFDL-ST\" in Figure 8.
* **Previous Work [7]:** In [7], we first suggested our own design approach used also in TT-VFDL-ST. A specific method taking that approach is used for verification of TT-VFDL-ST, with available options. That method in [7] has a limitation that consists of several options depending on each data characteristic. \"TT-VFDL-SIM\" in Figure 8.
* **[8; 9; 10; 11]:** Implementation of the mechanism used in [8; 9; 10; 11] transferring features trained from unsupervised representation learning. An autoencoder is used for each representation learning. \"Representation Learning using Autoencoder\" in Figure 8.
* **CMP:** A designed method for a comparison with [8; 9; 10; 11]. It also transfers features trained from unsupervised representation learning but it uses regression tasks to reproduce its input. \"Representation Learning using Regression\" in Figure 8.
* **[12; 13; 14; 15]:** Implementation of the mechanism used in [12; 13; 14; 15], which is called \"Split Learning\". * **[34]:** Implementation of [34], which takes the local gradient update mechanism for \"Split Learning\". \"Split Learning with Local Gradient Update\" in Figure 8.
* **BASE:** Centralized method on a single machine. \"BASE\" in Figure 8.

#### 4.1.3 Experimental Settings

We designed target task models for each dataset as described in Table 4. Layers, number of units, and activation functions of the FE part for each dataset in Table 4 were determined from our pre-experiments on a single machine. FE part structures might be different for each input of the dataset. However, for experiments, we applied the same FE part to all input streams that came from the same dataset.
## 4 Simulation Results
### Behavior of PI Feature  
When conductivity and thickness of samples changed, the PI time and amplitude were changed, as shown in Figures 10 and 11. It was found that the PI amplitude and time changed when the thickness changed from about 0 to 2 mm and the conductivity changed from about 0 to 10 MS/m, which signified that the thickness and conductivity of sample could be measured by testing the PI amplitude and time. When the thickness was larger than 2 mm and conductivity changed from 0 to 3 MS/m, the PI time and amplitude changed with increasing conductivity. When the thickness was larger than 2 mm and the conductivity changed from 3 MS/m to 10 MS/m, the PI time did not change. The results demonstrated that the detection range of conductivity was from 0 to 10 MS/m when the thickness was smaller than 2 mm and the detection range of conductivity was from 0 to 3 MS/m when the thickness was larger than 2 mm. It also indicated that the conductivity measurement was only suitable for a certain range when sample thickness was determined.  
When the conductivity of sample was 3.2 MS/m, the PI time and amplitude curves are depicted with thickness in Figure 12. It was observed that the PI time and amplitude were changed when thickness was smaller than 3.6 mm. When sample thickness was larger than 4 mm, the PI time and amplitude were almost unchanged, which signified that the thickness of sample did not affect the PI feature with increasing thickness. The PI time and amplitude curves are shown with conductivity in Figure 13 for a thickness samples of 0.8 mm. It was found that the PI time and amplitude increased with increasing conductivity. The PIs changes along a curve with increasing conductivity. The results indicated that the PI time and amplitude could serve as an index of conductivity when sample thickness was constant, which signified that the conductivity of ferromagnetic samples could be determined by  
Figure 10: PIs time change with thickness and conductivity.  
Figure 9: Thrice subtraction signals. Permeability invariance (PI) is shown by the red circle at the intersection of the signals.
# Propagation of Non-Linear Lamb Waves in Adhesive Joint with Micro-Cracks Distributing Randomly
###### Abstract  
In this paper, the propagation of non-linear Lamb waves in a tri-layer adhesive structure model with micro-cracks distributing in a random way is systematically investigated using the described numerical simulation method. This study is expected to provide a deeper understanding of the characterization of the damage of the adhesive layer with micro-cracks for adhesive joints by effectively employing the non-linear Lamb waves.  
Keywords:non-linear Lamb wave; micro-crack damage; adhesive joint; acoustic non-linearity parameters +
Footnote †: journal: Applied
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 2 Background and Motivation
### Smart Manufacturing Running Example
#### 2.2.2 Example Processes  
We will use the following two example manufacturing processes in our smart factory to illustrate the new concepts. Note that depending on the goal and use case of the data analyst, these processes can already be known from domain knowledge or be discovered from the detection of activity executions. In the latter case, there is no prior process knowledge (_unsupervised setting_) and the analyst attempts to detect and label activities from the raw IoT data and their knowledge about the CPS components. If processes are known, the analyst attempts to identify known activities and sequences of activities from the raw IoT data (_supervised setting_).  
**Storage Process**: This process starts with a new raw workpiece in the delivery and pickup area of the VGR_1 station. The vacuum gripper robot picks up the workpiece and moves it to the station's color sensor. Then, the workpiece color is determined. Then, the vacuum gripper robot picks up the workpiece and moves it to the HBW_1 station. Then, the vacuum gripper robot holds the workpiece until the high-bay warehouse retrieves an empty container to store the new workpiece. Then, the vacuum gripper robot moves to its initial position. We use the business process modeling standard BPMN 2.0 [23] to provide an abstract representation of this process in the form of a graphical process model. This process model is shown in Figure 3.  
**Production Process**: This process starts with a human entering the color of a workpiece that should be produced. Then, the HBW_1 station unloads a corresponding workpiece from the warehouse. Then, the vacuum gripper robot of the VGR_1 station picks up the workpiece and moves it to the OV_1 station. Then, the workpiece is burnt in the oven. Then, the WT_1 station moves the workpiece to the MM_1 station. Then, the workpiece is milled. Then, the workpiece is sorted in the SM_1 station according to its color. Then, the vacuum gripper robot of the VGR_1 station picks up the workpiece (now: product) and moves it to the pickup and delivery area. The corresponding process model in BPMN 2.0 is shown in Figure 4.
# A Review of the Applications of OCT for Analysing Pharmaceutical Film Coatings
## 3 Pharmaceutical Film Coatings  
### Measurement Principle  
OCT is a high-resolution imaging modality providing depth profiles of semi-transparent and turbid materials non-destructively and without physical contact. Extensive reviews of applications have been presented [21; 22]. In a standard measurement in pharmaceutics, an optical beam is incident onto a pharmaceutical dosage form, where a fraction of light reflects off due to a change in the material refractive index while the majority of the remaining portion penetrates into the coating structure, which undergoes reflection and/or scattering when it encounters subsequent change in material  
Figure 1: Schematic diagram of a time-domain optical coherence tomography (TD-OCT) system. L—low-coherence light source; Ref—reference mirror; BS—beam-splitter.  
Figure 2: Schematic diagram of a spectral-domain optical coherence tomography SD-OCT system. L—low-coherence light source; Ref—reference mirror; BS—beam-splitter.  
interfaces. Film coating thickness at a given point on a pharmaceutical tablet surface, provided that it is oriented perpendicular to the incident beam, is therefore directly proportional to the separation between adjacent reflection peaks in the time-domain OCT signal or A-scan and is determined by \(d~{}=~{}c\Delta t/(2n)\), where \(c\) is the speed of light, \(d\) is the coating thickness, \(\Delta t\) is the peak separation, and \(n\) is the coating refractive index at the optical regime. The coating refractive index can be estimated either from an independent measurement of the absolute film coating thickness using techniques such as terahertz pulse imaging (TPI) [23] or X-ray microtomography (X\(\upmu\)CT) [24], or more precisely by spectroscopic methods such as spectroscopic ellipsometry. By varying the relative distance between the OCT sensor and the dosage form laterally, a cross-sectional image or B-scan can be constructed as shown in Figure 3.
## 4 Application of the SKD Model to a Super-Resolution-Type Problem
The previous equalities assume that the nodal values (point values) are given by the means: this approximation is tacitly assumed in application models. The implementation of the WKSG model in terms of measured values highlights, by comparison, the errors coming from this assumption, as in Figure 5c, with the indication of the \(PSNR\) (see, e.g., Ref. [24]), calculated compared to the original HR corresponding image. In the test set, a constant frame is surrounding the image of the paint, such that the \"far field\" condition is satisfied. If any approximation (rounding) is avoided in the mean process, the SKD model provides exact results (\(MSE=0,PSNR=+\infty\)); see Figures 5d,e and 6e,f. Indeed, each equation given in the system of Section 4 needs a division by 4 (since it is a mean): this is a source of error, because the result of the division could be a non-integer number affecting, the same way, the values calculated for \(I\) after the reconstruction process. On the other hand, \(I\) must assume integer values (e.g., 256 levels in grayscale images). To avoid this source of error, in our specific case, it is enough to multiply by 4 the given equations: this is equivalent to request that the measurement equipment (e.g., a photographic camera) has a number of quantization levels \(L\) (i.e., a precision in the measurements, e.g., the number of gray levels) four times the resolution of the native instrument. E.g., in the case here examined, where \(P=256\) (number of gray levels), \(L\) must be chosen such that \(L=4P\). Figure 5: \(Cont.\)  
As expected, the main differences between the two models are more evident in the areas of the image where the signal exhibits higher variations, while they are null where the signal is constant (see Figure 5f). Further examples are provided in Appendix A. We finally remark that it is understood that the above considerations can be subsequently applied to the corresponding operators, for example to further rescale the image, and obviously the measurement methods will have their impact on the operators, e.g., on the rescaling process. Figure 5: Super resolution using the WKSG and the SKD models. The reconstruction using the SKD model together with the hypothesis of far field is exact. From left to right, top to down: original high resolution (HR) image (**a**); scaled low resolution (LR) version halved in size of the original image (\(M_{R}\), (**b**)); reconstruction by mean of the WKSG model and relative \(PSNR\) with respect to the original image (**c**); reconstruction by the SKD model and relative \(PSNR\) with respect to the original image (**d**); differences, in modulus, between the original image and the SKD reconstruction (**e**); differences, in absolute value, between the WKSG and the SKD models (**f**): the resulting image has been equalized to highlight the differences (black color means no differences). Figure 6: \(Cont.\)
# Fuzzy MLKNN in Credit User Portrait +
## 4 Experiments  
### Evaluation Metrics  
The general performance evaluation metrics of multi-label learning algorithms have been extensively studied and sorted out by researchers [46; 47]. In this paper, we select the five most commonly used indicators to compare the performance of the algorithms. Among them, _HammingLoss_ is considered from the perspective of samples. The other four indicators are considered from the perspective of label ranking. They include _Average_Precision, _RankingLoss, OneError_, and _Coverage_. The specific calculation form of these indicators will be explained below.  
_HammingLoss_ refers to the average number of misclassifications of multiple labels on a single sample. The smaller the indicator, the better the performance of the algorithm.  
\[HL=\frac{1}{t}\sum_{i=1}^{t}\frac{1}{m}|Z_{i}\Delta Y_{i}|\]where \(t\) is the number of test samples, \(m\) is the number of labels, \(Z_{i}\) is the predicted label set, \(Y_{i}\) is the real label set, and \(|\ |\ \) represents the difference between the two sets, that is, the number of errors between the predicted labels and the true labels.  
_Average_Precision_ is different from Precision in single-label classification. It is not the average Precision of all training samples on each label but represents the average probability that the order of the predicted relevant labels is before the specific real relevant labels. The larger the index, the better the performance.  
\[averagePrecision=\frac{1}{t}\sum_{i=1}^{t}\frac{1}{|Y_{i}|}\sum_{y\in Y_{i}} \frac{|\{y^{\prime}|rank_{f}(x_{i},y^{\prime})\leq rank_{f}(x_{i},y),y^{ \prime}\in Y_{i}\}|}{rank_{f}(x_{i},y)}\]  
_RankingLoss_ indicates an incorrect ranking in the ranking sequence of the label set owned by the sample, that is, the number of times that the ranking of the relevant labels appears behind the irrelevant labels. The smaller the index, the better the performance of the algorithm.  
\[RL=\frac{1}{t}\sum_{i=1}^{t}\frac{1}{|Y_{i}||\overline{Y_{i}}|}|\{(y^{\prime},y^{\prime\prime})|f(x_{i},y^{\prime})\leq f(x_{i},y^{\prime\prime}),(y^{ \prime},y^{\prime\prime})\in Y_{i}\times\overline{Y_{i}}\}|\]  
where \(\overline{Y_{i}}\) is the complementary set of \(Y_{i}\) to the total label set L.  
_OntError_ refers to the number of times that the first-ranked label in the predicted label of the sample does not belong to the sample-related label. The smaller the index, the better the performance of the algorithm.  
\[oneError=\frac{1}{t}\sum_{i=1}^{t}\left(\underset{\underline{l}_{i}\in L}{ \operatorname{argmax}}f(x_{i},l_{j})\
otin Y_{i}\right)\]  
_Coverage_ can be understood as the step size in the sorted sequence of predicted label sets that needs to be traversed to get all the true relevant label sets. Likewise, the smaller the metric, the better the algorithm performance.  
\[coverage=\frac{1}{t}\sum_{i=1}^{t}\underset{y\in Y_{i}}{\operatorname{max}} f(x_{i},y)-1\]  
where \(-1\) ensures there is no limit case of misclassification; that is, the top-ranked predicted labels are their true labels.