esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Approach
### Numerical Examples
\begin{table}
\begin{tabular}{c c c c} \hline \(k\) & **Date** & **Event** & \(\mathbf{s}\) \\ \hline \multirow{6}{*}{\(1\)} & 1 January 2020 & Start & - \\  & 1 July 2020 & PM & 1.0 \\  & 1 January 2021 & PM & 1.0 \\  & 1 July 2021 & PM & 1.0 \\  & 1 January 2022 & PM & 1.0 \\  & 1 July 2022 & End & - \\ \hline \multirow{6}{*}{\(2\)} & 1 January 2020 & Start & - \\  & 1 May 2020 & Failure & - \\  & 1 November 2020 & PM & 1.0 \\ \cline{1-1}  & 1 February 2021 & Failure & - \\ \cline{1-1}  & 1 May 2022 & Failure & - \\ \cline{1-1}  & 1 May 2022 & Failure & - \\ \cline{1-1}  & 1 May 2022 & End & - \\ \hline \end{tabular}
\end{table} 
Table 4: Failure data of the third case example. Past PMAs were performed with an intervention level of 100%. System \(k=1\) has no observed failures. \begin{table}
\begin{tabular}{c c c c} \hline \(K\) & **Date** & **Event** & \(\mathbf{s}\) \\ \hline \multirow{6}{*}{\(1\)} & 1 January 2020 & Start & - \\  & 1 July 2020 & PM & 0.7 \\  & 1 January 2021 & PM & 0.7 \\  & 1 January 2021 & PM & 0.7 \\  & 1 January 2022 & PM & 0.7 \\  & 1 July 2022 & End & - \\ \hline \multirow{6}{*}{\(2\)} & 1 January 2020 & Start & - \\  & 1 May 2020 & Failure & - \\  & 1 November 2020 & PM & 0.7 \\  & 1 February 2021 & Failure & - \\  & 1 May 2022 & Failure & - \\  & 1 February 2022 & PM & 0.7 \\  & 16 May 2022 & Failure & - \\  & 16 November 2022 & PM & 1.0 \\  & 31 December 2022 & End & - \\ \hline \end{tabular}
\end{table} 
Table 3: Failure data of the second case example. Past PMAs were performed with an intervention level of 70%. Future PM may have any intervention level as defined in the first column of Table 2.  
For the first case example, additional scenarios were created by varying preventive and corrective maintenance costs to perform a sensitivity analysis of the proposed models.
## 3 Results and Discussion
### Failure Modes
#### 3.1.1 Specim LSW  
When the horizontal displacement reached 6 mm, an initial horizontal crack occurred at a height of about 350 mm from the top surface of the base beam on the left side of the LSW specimen (i.e., the straight-edge member). When the horizontal displacement was 8 mm, a horizontal crack appeared at the bottom of the right side of the shear wall (i.e., L-shaped-edge member). As the displacement level reached 10 mm, the original crack on the left side of the shear wall extended obliquely to the top surface of the foundation along the direction of 45\({}^{\circ}\). At the same time, with the increase of displacement, multiple horizontal cracks appeared on both sides of the wall. The longitudinal reinforcement on the left side of the shear wall yielded in tension at a displacement level of 16 mm, and with the increase of loading displacement, multiple 45\({}^{\circ}\) inclined cracks appeared on the shear wall. When the loading level was 24 mm, vertical cracks in concrete under compression appeared at the bottom of the left and right sides of the specimen, and the oblique cracks on both sides of the specimen intersected approximately at the centroid of the wall. When the loading displacement approached 32 mm, the concrete in the compression zone began to spall off, which indicated that the outermost concrete in the compression zone had reached the ultimate strain. As the loading displacement reached 48 mm, during the second cycle of negative loading, the concrete on the left side of the wall was crushed and fell off in a large area, and the rebars buckled, which indicated that the wall reached the limit state.  
Figure 5: Dimensions of specimens and arrangement of displacement gauges: (**a**) front elevation of the specimen; and (**b**) side elevation of the specimen.
## 3 Hybrid Weight Assignment Model  
### Entropy Weight Method  
The entropy weight method is a mathematical method that calculates a comprehensive index (\(W^{e}=\left(w_{1}^{e},w_{2}^{e},\ldots,w_{n}^{e}\right)\)) based on the amount of information transmitted to the decision maker by various factors [41]. As an objective weighting method, the entropy weight method could solve the problem of large information and difficult quantification of UUSR evaluation indicators, and express the amount of information contained in the indicators to the greatest extent. On the basis of the objectivity and data dependence of the entropy weight method, it has been used for the weight assignment of UUSR evaluation indicators in many cases.  
The entropy weight method has nothing to do with personal experience, only the data value of indicators. As a matter of fact, the bigger the difference in values between the same indicator, the greater the weights. Combined with the characteristics of the panel data displayed by the data value of UUSR evaluation indicators, the entropy weight of indicator dimension (\(w_{j}^{e}\)) is determined by calculating the indicator entropy value (\(H_{j}\)). The calculation steps are shown concretely below.  
(1) The standardized value (\(a_{ij}\)) of the original statistical value (\(x_{ij}\)) of evaluation indicator can be obtained as:  
\[a_{ij}=\frac{x_{ij}-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
\[a_{ij}=\frac{x_{max}-x_{ij}}{x_{max}-x_{min}} \tag{2}\]  
where \(x_{ij}\) is the statistical value of the \(j\)th indicator of object \(i\) (\(i=1,2,\cdots,m\); \(j=1,2,\cdots,n\)). Equations (1) and (2) are used for the positive indicators and negative indicators respectively.  
(2) The entropy value (\(H_{j}\)) can be calculated by the standardized value (\(a_{ij}\)). Its expression is:  
\[H_{j}=-\frac{1}{lnm}\sum_{i=1}^{m}\frac{a_{ij}}{\sum_{i=1}^{m}a_{ij}}ln\frac{ a_{ij}}{\sum_{i=1}^{m}a_{ij}} \tag{3}\]  
where \(H_{j}\) is the entropy value of the \(j\)th evaluation indicator, and an assumption is made:  
\[\frac{a_{ij}}{\sum_{i=1}^{m}a_{ij}}=0,\ \frac{a_{ij}}{\sum_{i=1}^{m}a_{ij}}ln \frac{a_{ij}}{\sum_{i=1}^{m}a_{ij}}=0.\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{4}{c}{\(t_{1}\)} & \multicolumn{4}{c}{\(t_{2}\)} & \multicolumn{4}{c}{\(\ldots\)} & \(t_{s}\) & \\ \cline{2-13}  & \(x_{1}\) & \(x_{2}\) & \(\ldots\) & \(x_{n}\) & \(x_{1}\) & \(x_{2}\) & \(\ldots\) & \(x_{n}\) & \(\ldots\) & \(x_{1}\) & \(x_{2}\) & \(\ldots\) & \(x_{n}\) \\ \hline \(O_{1}\) & \(x_{11}^{t_{1}}\) & \(x_{12}^{t_{1}}\) & \((3) The entropy weight of indicator dimension (\(w_{j}^{e}\)) can be calculated as follows:  
\[w_{j}^{e}=\frac{1-H_{j}}{\sum_{j=1}^{n}\left(1-H_{j}\right)} \tag{4}\]  
where \(w_{j}^{e}\) is the entropy weight of the \(j\)th evaluation indicator, and \(\sum_{j=1}^{n}w_{j}^{e}=1\).
## 2 Materials and Methodology
### Pre-Processing for Data Analysis
#### 2.2.4 Data Standardization through T-Score Conversion  
Data standardization is a technique for determining the relative position of each variable when it is difficult to evaluate in the same standard because the units and ranges of the acquired data are different. In this study, T-score conversion was applied, taking into account the characteristics of the data. In order to compare the effects on the increase in the flowrate and the decrease in the leakage rate, it was converted into a T-score that was converted into an average of \(50\) and a standard deviation of \(10\) and applied. This can be calculated as in Equation (1).  
\[Tscore=10\bigg{(}\frac{X-\mu}{\sigma}\bigg{)}+50 \tag{1}\]  
where \(X\): variables, \(\mu\): average of variable \(X_{1},\ X_{2},\cdots X_{N}\), \(\sigma\): standard deviation of variable \(X_{1},\ X_{2},\cdots X_{N}\).
# Conceptual Design and Preliminary Verification of Distributed Wireless System of Weigh-in-Motion
## 3 Design of Distributed WIM System
### Charge Amplifier Circuit  
When the wheel loads act on the wireless sensor for WIM based on piezoelectric materials, in order to detect the charge produced by piezoelectric quantitatively, a charge amplifier circuit is then introduced to transform it into the voltage signal, which can be collected easily. To successfully collect signals, the charge conversion circuit must satisfy the following requirements: (1) the output of the voltage signal is not sensitive to the external influence; (2) a high magnification is needed; and (3) the range of the measured frequency range should be wide enough. In summary, this paper adopted the OPA128 operational amplifiers, which are produced by TI company in America, as the core of the module. Additionally, its main technical indexes include (1) the maximum value of an ultra-low bias current of 75 fA, (2) the maximum value of an offset voltage of 500 uA, (3) the temperature drift of 5 uA/\({}^{\circ}\)C, (4) the minimum value of a high open-loop gain of 110 dB, and (5) the minimum value of the high Common Mode Rejection (CMRR) of 90 dB. These performance indexes meet the use requirements of the charge-amplifying and conversion of the circuit in the signal measuring module of the vehicle WIM sensor.  
Based on the chosen operational amplifiers, the charge-amplifying and conversion circuit has been designed with the circuit diagram shown in Figure 6. The generated signal frequency band between the vehicle and sensors may be high when the vehicle speed is high as well, so the OPA128 LM was placed in parallel connection with the capacitor C10. Meanwhile, to protect the operational amplifier, a series connection was achieved with a resistor R8 in the input terminal of OPA128 LM. Additionally, a capacitor C7 was connected in parallel to R8, which was intended to avoid another pole leaded by the input capacitance of OPA128 and R8. Finally, due to the great stress difference in each contact point, the stability of the output signal can be satisfied by adjusting the circuit gain. Based on the schematic figure of the charge-amplifying and conversion circuit, the corresponding circuit was made shown in Figure 7.  
Figure 6: Circuit diagram of charge-amplifying and conversion.
## 5 Experimental Analysis
### Results
#### 5.1.2 Assessment of FS with a Classification Algorithm  
Six classification algorithms assess the feature subsets selected by the four FS algorithms. The reliability of the FS algorithms is measured by Precision, Recall, F-measure, Kulczynski Index, and Fowlkes-Mallows Index [49; 50]. These measures depend on True Positive (TP), False Positive (FP), and False Negative (FN).  
Precision = TP/(TP + FP)  
Recall = TP/(TP + FN)  
F-Measure = 2 * ((Precision X Recall)/(Precision + Recall))  
Kulczynski Index = \(\frac{1}{2}\) (Precision + Recall)  
Fowlkes-Mallows Index=\(\sqrt{\text{(Precision * Recall)}}\)  
The results of the proposed framework of this study are evaluated using the performance measures (Equations (1)-(5)) of the 10-fold classification methods [51; 52]. Table 3 shows the results of performance measures, in which many classifiers have revealed good results (\(>\)0.8) with feature subset of proposed RSIHSQR and RSIHSRR feature selection algorithms. The validity measure greater than 0.8 represents the accuracy of the prediction rate, particularly with the peptide dataset [6].
# A Novel 2D Micro-Displacement Measurement Method Based on the Elliptical Paraboloid
## 2 System and Principle
### Light Spot Image Processing  
The image of the light spot obtained by the CCD detector is shown in Figure 4. After a series of digital image processing operations, including extraction of the region of interest (ROI) and filtering, the gray centroid algorithm was applied to the light spot on the image and can be expressed as  
\[x_{0}^{\prime}=\frac{\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N}F(x^{\prime}, y^{\prime})x^{\prime}}{\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N}F(x^{\prime}, y^{\prime})},y_{0}^{\prime}=\frac{\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N}F(x^{ \prime},y^{\prime})y^{\prime}}{\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N}F(x^ {\prime},y^{\prime})} \tag{7}\]  
Figure 3: Measurement principle based on autocollimation.  
Figure 2: Model of the elliptical paraboloid.  
where \(\left(x^{\prime},y^{\prime}\right)\) is the pixel coordinate, \(\left(\text{M},\text{N}\right)\) is the image size, \(\text{F}(x^{\prime},y^{\prime})\) is the gray value of the pixel and \(\left(x^{\prime}_{0},y^{\prime}_{0}\right)\) is the centroid coordinate to be used.  
In order to improve the stability of the measurement system, a median filtering algorithm based on statistical ranking principle was applied. A queue with a length of seven was designed. When new data enter the head of the queue, the old data located at the tail of the queue are removed. Meanwhile, the median of the data in the queue will be calculated as the value presented at the current spot location. The experimental results show that the median filtering algorithm makes the system stable within \(\pm\) 0.05 pix for 4 h.
# Establishment and Numerical Analysis of Rolling Force Model Based on Dynamic Roll Gap
## 5 Conclusions  
In the this work, the study initially focused on important factors such as lubrication, friction, stress, tension, and roll-flattening radius in the rolling process. Mathematical models were separately established for the friction coefficient and roll-flattening radius. Subsequently, the mathematical models for the non-steady-state friction coefficient and roll-flattening radius were decoupled from the Bland-Ford-Hill rolling-force-calculation formula, proposing a novel rolling force model that considers the coupling of multiple parameters in the cold rolling process. Next, production data of thin strip steel from a large-scale company were utilized to analyze the effects of the friction coefficient, stress, tension before and after rolling, and roll-flattening radius on the rolling force. The following conclusions were drawn from the study: (1) The rolling force increases with an increase in the friction coefficient. When the friction coefficient exceeds a certain value, the rate of increase in the rolling force decreases. As the friction coefficient increases, the fluid lubrication decreases, approximating a dry friction state. However, an excessively low friction coefficient can also impact the rolling quality. (2) The rolling force exhibits a linear increase with increasing stress. Maintaining a constant stress level in high-quality rolled products helps ensure the stability of the rolling force. (3) The rolling force decreases with increasing tension before and after rolling. Effective control of the inter-stand tension enhances the rolling stability. (4) By comparing the actual measured rolling forces at each pass of the cold rolling process with the calculated values from the newly developed rolling force model and the rolling force model that does not consider the roll-flattening radius, it was found that the calculated values from the new model closely approximate the measured values. This indicates that an accurate prediction of the roll-flattening radius can improve the precision of rolling force calculations.  
This research establishes a robust foundation for simulating rolling forces in the cold rolling process and provides valuable guidance for controlling rolling forces in practical production. However, cold rolling is an exceedingly complex process, and future research should also consider the influence of rolling interface roll vibrations and bending forces on the rolling force.  
Conceptualization, L.T.; data curation, H.W.; methodology, Q.W.; validation, H.W.; writing--original draft, L.T.; writing--review and editing, Q.W. All authors have read and agreed to the published version of the manuscript.  
The work was supported by National Natural Science Foundation of China (Project No. 51875146) and the Natural Science Foundation of Zhejiang Province, China (Project No. LY21E050005).  
**Institutional Review Board Statement:** Not applicable.  
Figure 8: The numerical comparison.
# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
###### Abstract  
The human facial bone is made up of many complex structures, which makes it challenging to accurately analyze fractures. To address this, we developed advanced image analysis software which segments and quantifies spaces between fractured bones in facial CT images at the pixel level. This study used 3D CT scans from 1766 patients who had facial bone fractures at a university hospital between 2014 and 2020. Our solution included a segmentation model which focuses on identifying the gaps created by facial bone fractures. However, training this model required costly pixel-level annotations. To overcome this, we used a stepwise annotation approach. First, clinical specialists marked the bounding boxes of fracture areas. Next, trained specialists created the initial pixel-level unrefined ground truth by referencing the bounding boxes. Finally, we created a refined ground truth to correct human errors, which helped improve the segmentation accuracy. Radiomics feature analysis confirmed that the refined dataset had more consistent patterns compared with the unrefined dataset, showing improved reliability. The segmentation model showed significant improvement in the Dice similarity coefficient, increasing from 0.33 with the unrefined ground truth to 0.67 with the refined ground truth. This research introduced a new method for segmenting spaces between fractured bones, allowing for precise pixel-level identification of fracture regions. The model also helped with quantitative severity assessment and enabled the creation of 3D volume renderings, which can be used in clinical settings to develop more accurate treatment plans and improve outcomes for patients with facial bone fractures.  
facial bone fractures; segmentation refinement; radiomics feature analysis Article
## 2 Materials and Methods
### Data Processing and Comparison Method
#### 2.3.3 Data Filtering and Comparisons
If the length of the considered GPS (or GLONASS) vector, referred to the t-th epoch, was greater than 15, a one-sample \(t\)-test [53] was executed to establish if the GPS (or Galileo and GLONASS) average parameter values were statistically different from the same Galileo parameters.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy-Preserving Defense Strategies
#### 4.2.1 Gradient Noise Addition and Compression  
Despite the performance of the Deep Leakage from Gradients (DLG) algorithm to infer data from gradients, [PERSON] et al. [7] demonstrated that in FL, and more generally in ML, the addition of noise to the gradients makes the inference more difficult. The authors show that from a variance larger than \(10^{-2}\), the accuracy drops significantly and leads to an inability to infer the original data. Another solution proposed in [50] is the compression of the model gradients. This approach sparsifies gradients, which impacts the sensitivity of algorithms such as DLG. The authors of [50] show that the gradients can be compressed by 300\(\times\) before affecting the accuracy of the model. Conversely, the tolerance of the DLG algorithm is around 20% of sparsity, and the compression makes it ineffective.
## 2 Methodology
### RPG-HATPRO-G5 Microwave Radiometer  
The RPG-HATPRO-G5 microwave radiometer (hereafter referred to as MWR) is capable of performing the real-time and continuous monitoring of tropospheric thermodynamic structures across a vertical range of 0-10 km. The products contain the temperature, the absolute humidity, the relative humidity, the total water vapor content, the liquid water path, the liquid water contours, the cloud base height, the atmospheric stability index, and the surface meteorological variables [10; 22; 23]. The study by [PERSON] and [PERSON] (2012) shows that the differences between MWR observations and radiosondes are small and suitable for boundary layer observations [28]. This MWR operated a continuous observation with a temporal resolution of 1 s and provided a temperature and humidity profile data from the ground to a height of 10 km at a 10-30 m resolution up to 0.5 km, a 40-70 m resolution from 0.5 to 2.5 km, and a 100-200 m resolution from 2 to 10 km. For the details of MWR, please refer to [[http://www.radiometer-physics.de](http://www.radiometer-physics.de)]([http://www.radiometer-physics.de](http://www.radiometer-physics.de)) (accessed on 7 January 2024).
# Weather Radar in Complex Orography
## 4 Solutions and Results
### Pulse Length  
Another aspect of complex orography that merits consideration is the choice of the pulse length. Although some sensitivity is lost with a short pulse, it has the great advantage of keeping the area contaminated by clutter small and it is possible to obtain clutter-free measurements of precipitating clouds just before and after the gates where the beam hits the ground. This is illustrated by the reflectivity measurements, at an 83-m radial resolution, obtained with a pulse length of 0.5 microseconds, shown in Figure 15.  
Figure 15: Reflectivity for horizontal polarisation in the Alps, where the main lobe of the beam of the Plaine Morte radar hits steep mountain crests; see also the mountains in the background of the photograph shown in Figure 13. The pulse length is 0.5 microseconds, which corresponds to 75 m. Reflectivity is sampled at a radial resolution of 83 m. (**a**) Reflectivity of ground clutter in a moment with clear sky conditions. (**b**) Reflectivity for a mixed situation with ground clutter superimposed onto precipitation signals. (**c**) The same time as (**b**), but for clean reflectivity data after clutter cancellation and radial integration for 500 m gates. The same colour scale is used for reflectivity as in Figure 8. (**d**) Differential phase shift, a key variable in the clutter cancellation algorithm (Section 4.10), for the same time as (**b**) and (**c**). The values range from 0 to \(2\pi\).  
Moreover, using short pulses, more independent samples can be obtained to reduce stochastic errors from signal fluctuations. This improves the precision of polar radar measurements. Furthermore, short pulses make it possible to reveal fine-scale signatures, which may be of particular interest in the presence of severe convective storms. These may allow a better assessment to be obtained of the hazards related to supercells, hail cores and local rainfall extremes, a topic of ongoing research at MeteoSwiss and EPFL.