esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Operation of the Proposed Converter  
The proposed converter shown in Figure 2b consists of five reverse conducting power semiconductor switches, three inductors, and a single diode (D2). The diode (D1) is a blocking diode that is normally embedded with the PV panels to prevent reverse conduction through the PV during the unavailability of the PV power and is not part of the proposed converter. V\({}_{\text{PV}}\) is the voltage of the PV array connected to the unidirectional input port of the converter, V\({}_{\text{batt}}\) is the voltage of the battery connected to one of the bidirectional input ports, and V\({}_{\text{SC}}\) is the voltage of the SC connected to the other bidirectional input port.  
The possible operation modes of the proposed MPC as illustrated in Figure 3 are:  
* Single input-double output (SIDO) mode (Figure 3a): where the PV supplies the required load power whilst any excess is charging the battery;
* Single input-triple output (SITO) mode (Figure 3b): where a significant amount of power is produced by the PV that can supply the load power, and due to limitations of maximum charging power of the battery, all excess is stored in the SC;
* Double input-single output mode-I (DISO-I) (Figure 3c): where the PV is unable to supply the load power, and any deficit is provided by the battery, where the load is not excessive (medium power peaks);
* Triple input-single output (TISO) mode (Figure 3d): where the PV, the battery, and the SC are supplying the load power during high power peaks, which exceed the available PV power and the battery discharge power capability;
* Single input-single output (SISO) mode (Figure 3e): where the battery provides power for supplying the load during the unavailability of the PV power;
* Double input-single output mode-II (DISO-II) (Figure 3f): where both the battery and the SC provide power for supplying the load during significant load power peaks that happen during the unavailability of PV power.  
In order to simplify the analysis of the converter, continuous conduction mode (CCM) is considered for all modes of operations, and the power MOSFETs and diodes are assumed to be ideal.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 1 Introduction
To reduce the system's service risk, it is necessary to conduct a statistical analysis of all kinds of faults that occurred since the operation and service of the system, evaluate the service performance of the system's integrity, and provide effective suggestions for identifying weak links, reducing system failure factors, and improving the service capability of the system integrity. The Integrity Failure Modes and Effects Analysis (IFMEA) method evaluate the possible faults in the process by examining the causes and effects of faults, which can take effective measures before faults occur and is an effective method for improving system integrity [14]. [PERSON] et al. used the IFMEA method to count and sort out GPS faults, which not only improved GPS reliability but also provided suggestions for future GPS construction. It can also assist civil and military users in better understanding GPS integrity faults and the impact of various operations [15; 16]. In 2019, [PERSON] and other scholars obtained the SISRE system characteristics and fault probability based on IFMEA and provided support for SBAS DFMC and H-ARAIM life safety services through the pseudorange drift and SISRE monitoring, using Galileo's relevant data for approximately 3.5 years [17]. [PERSON] et al. demonstrated the integrity monitoring design of BDS-3 and preliminarily analyzed the ability of BDS-3 system integrity monitoring mechanism [18; 19; 20]. At present, there is still a lack of research on integrity risk modeling and result evaluation of BDS-3, both at home and abroad. In this paper, the IFMEA of BDS-3 is carried out, the abnormal factors and influence of BDS-3 are analyzed, and the statistical table of abnormal BDS-3 characteristics is designed and proposed. Based on the IFMEA results, the abnormal factors in the space section and the ground control section of the BDS-3 system are sorted, and the system integrity risk tree model is established. The historical occurrence times of abnormal events in each bottom event of the risk tree during the system's operation are counted, and the occurrence times of each bottom event are reasonably predicted according to the 95% confidence level based on the Bayesian estimation theory. According to the risk tree analysis, the risk probability of BDS-3 integrity was obtained. The research results were included in the key updated content of the International Civil Aviation Organization (ICAO) Standard Additionally, Recommendation Practice (SARPs) and can lay a solid foundation for BDS-3 integrity improvement.
## 2 Related Work
### Leather Surface Defect Detection Methods  
Given the magnitude and longevity of the leather manufacturing industry, it is to be expected that a variety of machine vision approaches have been applied over the years to address the challenge of leather surface defect detection and classification. Many conventional image processing techniques were used, specifically in the early years (10+ years) of this application, which produced comparatively outstanding classification results. One very influential method is that of [PERSON] et al. [27], whereby colour and texture features are collected from a small custom leather dataset (258 images), using greyscale co-occurrence matrices (GLCM). To determine the most optimal classification method, these extracted features are used in conjunction with 10-fold cross-validation for comparison of output results between a normalized Gaussian radial basis function network, support vector machine (SVM), and k-nearest neighbours (KNN).  
More recently, the success of neural networks in the field has led to several innovative solutions. [PERSON] and [PERSON] [28] used a class activation mapping technique to find the region of interest for the class of defect after popular CNNs, such as GoogLeNet, SqueezeNet, and ResNet, are trained using leather input images from six categories of defective leather sample images [29]. These same authors present the original high-resolution (4608 \(\times\) 3288 pixels) version of the Leather Defect detection and Classification dataset that is utilised in low-resolution format throughout this paper. Using the original dataset, the authors propose the use of a grey-level co-occurrence matrix (GLCM) to extract statistical texture features from defective and non-defective leather samples for a perceptron neural network classifier to be trained on labelled datasets of these texture features to identify five common leather defects: folding marks, grain off, growth marks, loose grain, and pinholes [30].  
Advancing their previous work in this field of interest, the teams of [PERSON] and [PERSON] adopted a generative adversarial network (GAN) as a means of reliably synthesizing further normal images in order to augment an already limited training set and therefore improve the accuracy of feature extraction and classification of the typical AlexNet architecture [31].  
Due to the ambiguous and unpredictable nature of wet-blue leather, the previous methods are all primarily focused on finished leather. However, [PERSON] et al. [32] proposed a method for wet-blue leather using hyperspectral target detection (HTD) to suppress the background while enhancing the contrast of input images before extracting features, where three neural networks were used. A 1D-CNN focusses on spectral feature defects, while defects with spatial features are the focus of a 2D-Unet, and a 3D-Unet architecture simultaneously processes spatial and spectral information. The combination of these three DL methods allows for the extraction and capture of a wide spectrum of defective textures, shapes, and sizes.
## 7 Challenges and Opportunities
### Energy Efficiency  
The energy efficiency of UAV computing is the most common and pressing concern hindering UAV's commercial deployment. The endurance time of a UAV is limited, especially for small-sized ones, owing to battery technological limitations [199]. The energy consumption of communication, payload, and computation in UAV computing impacts flight duration, and therefore the service time. Furthermore, mobile IoT devices are constantly battery-powered, thus because of the long-distance transmission requiring higher power, task offloading performance is compromised. In certain hard-to-reach regions, such as monitoring devices in IoT [200], changing the batteries of mobile devices is problematic. As a result, to reduce the energy consumption of both the UAV and mobile devices, energy-efficient UAV computing with multicriteria optimization must be designed.
## 2 Method
### Loss Function  
Based on the above architecture design, the segmentation network generates pixel-level segmentation maps and SDM, and the following functions are introduced to convert GT to SDM. The SDM assigns each pixel a value, indicating its signed distance to the nearest boundary of the target object.  
\[\mathcal{D}(a)=\begin{cases}0,&a\in S\\ -inf\|a-b\|_{2},&a\in\mathcal{C}_{in}\\ +inf\|a-b\|_{2},&a\in\mathcal{C}_{out}\end{cases} \tag{4}\]  
where \(\|a-b\|\) is the Euclidean distance between pixels \(a\) and \(b\), \(S\) represents the boundary of the target object, and \(\mathcal{C}_{in}\) and \(\mathcal{C}_{out}\) represent the region inside and outside of the target object, respectively. Typically, SDM takes negative values inside the target and positive values outside the target, with the absolute value indicating the distance from the point to the nearest point on the target object's surface.  
In the network training, for the regression task branch, a \(\mathcal{L}_{2}\) loss is used between the SDM of the network output \(P_{a}\) and the transformed GT map \(\mathcal{D}(Y)\).  
\[\mathcal{L}_{sdm}(P_{a},Y)=\|P_{a}-D(Y)\|^{2}, \tag{5}\]  
where \(Y\) denote the GT map.  
For the segmentation task branch, the combination \(\mathcal{L}_{seg}\) of dice loss and focal loss is applied as the loss function between the segmentation mask and the GT for each decoder output, and then their average is taken as the final segmentation loss.  
\[\mathcal{L}_{seg}(P_{b},Y)=\mathcal{L}_{Dice}(P_{b},Y)+\mathcal{L}_{FL}(P_{b}, Y), \tag{6}\]  
where \(\mathcal{L}_{Dice}\) denotes the dice loss and \(\mathcal{L}_{FL}\) denotes the focal loss. \(P_{b}\) and \(Y\) denote the prediction partition map and label, respectively.  
The final loss is defined as:  
\[\mathcal{L}=\mathcal{L}_{sdm}(P_{a},Y)+\mathcal{L}_{seg}(P_{b},Y). \tag{7}\]
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Deep-Learning-Based Fault Diagnosis Method
#### 5.3.2 Deep Belief Network (DBN)  
A DBN is a probabilistic generative model of multiple Restricted Boltzmann Machines (RBM). Each RBM consists of only two layers of neurons, the visible layer and the hidden layer, which are connected by a symmetrical weighted connection matrix [123; 124]. The visible layer inputs training data, whereas the hidden layer extracts features. In each RBM, the data vector is used to infer the hidden layer, which is then used as the data vector for the subsequent layer to improve the probability variation lower bound of the training data. As feature extraction is a crucial step in the diagnosis process, DBN employs an unsupervised greedy layer-by-layer method to obtain high-level feature representation, which can effectively eliminate the complexity and uncertainty introduced by manual feature extraction and enhance the intelligence of fault diagnosis [125; 126]. In addition, DBN has the capacity to manage high-dimensional and nonlinear data, thereby resolving the issues of data dimensional disaster and inadequate diagnostic capability.  
Since methods like SVM and ANN can only detect faulty data but not faulty sensors [127], [PERSON] et al. [128] proposed a method based on DBN and the generalized likelihood ratio test. DBN was utilized to classify fault data and normal data, the faulty sensor was identified by the maximum deviation between the fault data and the average value of the normal data, and the quantity of fault data and the generalized likelihood ratio test determined the fault mode. However, this method only detected faults when the signal exceeded the threshold; therefore, other methods are required to detect faults when the signal was below the threshold. Due to the loss of potentially valuable information in the original data caused by the layer-by-layer feature compression of the DBN model, [PERSON] et al. [129] employed the strategy of repeatedly stacking the original data during the training phase in order to fully extract the valuable information by extending the DBN model. Each RBM was trained with the original data so that the extracted features were highly correlated with the original data, and potentially valuable information was preserved. Compared to the conventional DBN model, the accuracy rate has been enhanced.  
DBNs can extract advanced features from large amounts of data and directly use raw signals to build end-to-end intelligent diagnostic models to reduce reliance on expert experience and known knowledge. They can fuse the time domain and frequency domain characteristics of each sensor signal and apply this to DBN training, which can not only realize fault diagnosis but also diagnose fault types. They are suitable for applications in advanced composite sensor systems.
# Adaptive Versioning in Transactional Memory Systems +
## 4 Basic Adaptive Versioning
### Computation of Total Number of Stores to Persistent Memories  
The total number of writes (i.e., stores) to the persistent memories are different when transactions are executed using different versioning methods.  
In eager versioning, transactional (undo) logs are stored in the persistent memory and in-place memory updates are performed. Each memory location accessed by a transaction is added to the log. Thus, in default, there are two stores for each memory location accessed by a transaction. Additionally, if the transaction aborts, it needs to be rolled back to the previous consistent state using the undo log, which requires two additional stores to the persistent memory. Let \(ST_{Eager}\) be the total number of stores to the persistent memory in eager versioning, \(N_{commit}\) be the total number of commits and \(N_{abort}\) be the total number of aborts, then,  
\[ST_{Eager}=(2N_{commit}+2N_{abort})\cdot S \tag{10}\]  
where \(S\) is the size of RW set of the transactions.  
In lazy versioning, all the computations are performed in volatile cache. If a transaction commits, then the changes are first persisted to the transactional (redo) logs and then updated to the original memory locations. That means, in lazy versioning, only committing transactions account for PM stores. Let \(ST_{Lazy}\) be the total number of stores to the persistent memory in lazy versioning and \(N_{commit}\) be the total number of commits, then,  
\[ST_{Lazy}=(2N_{commit})\cdot S \tag{11}\]  
From Equations (10) and (11), we can see that the total number of PM stores in lazy versioning is always less than that in eager versioning. So, from the perspective of minimizing total stores to persistent memories, lazy versioning seems better. However, this metric alone can not guarantee the better performance of transactions. Thus, we also consider other performance metrics such as execution time, abort rate and total data movements.
## 3 Synthetic Examples
### Simulation of Signals With Noise  
Next, we added Gaussian noise to \(x\) and \(y_{1}\) with a mean value of 0 and a standard variance of \(1/3\). The traditional correlation coefficient is shown in Figure 8A. It is clear that under noisy conditions, traditional correlation can recognize the correlation between \(x\) and \(y_{1}\) near 30 Hz and the uncorrelation near 100 Hz. Due to the existence of noise, the correlation coefficients changed at other frequencies.  
Figure 5: The traditional correlation coefficient between \(x\) and \(y_{1}\), \(x\) and \(y_{2}\), and \(x\) and \(y_{3}\).  
Figure 6: The SSST-TD TF correlation coefficients of (**A**) \(x\) and \(y_{2}\) and (**B**) \(x\) and \(y_{3}\).  
Figure 7: The correlation coefficients of \(x\) and \(y_{3}\) at (**A**) 100 Hz, and (**B**) 63 Hz.  
The correlation coefficient obtained by SSST-TD is shown in Figure 8B. The overall correlation coefficient trend was consistent with the noiseless one. The correlation near 30 Hz and uncorrelation near 100 Hz can be accurately identified in the case of noise. Figure 9 shows the correlation coefficients at different times. Except at 30 Hz and 100 Hz, the correlation coefficients at the other frequencies fluctuated under the influence of noise. As the proposed method is a time-frequency extension to the ordinary coherence, the method inherits the characteristics of traditional correlation; that is, under the influence of noise, both the traditional correlation analysis and the proposed analysis are influenced.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 2 Principle of Operation
### Area Measurement Principle
#### 2.2.2 Discretization and Polygon Approximation  
Suppose that the boundary curve recorded by the instrument is surrounded by a closed region D, and the plane coordinates of each point in the region D are obtained as shown in Figure 3.  
At the same time, \(\overrightarrow{P_{i}P_{i+1}}\) is expressed as a parameter equation  
\[\overrightarrow{P_{i}P_{i+1}}\ =\ \left\{\begin{array}{l}x\ =\ x_{i}+t \Delta x_{i}\\ y\ =\ y_{i}+t\Delta y_{i}\end{array}\right.(0\leq t\leq 1) \tag{6}\]  
Figure 3: Record point and area diagram. The dotted line represents the omitted sampling points.  
Figure 2: A schematic diagram of the earth’s sphere.  
where \(\Delta x\ =\ x_{i+1}-x_{i}\), \(\Delta y\ =\ y_{i+1}-y_{i}\). Substituting the parameter equation of each line segment into the line integral formula, the piecewise integral result is obtained as  
\[\int_{P_{l}}^{P_{l+1}}xdy-ydx=\int_{0}^{1}\left[(x_{i}+t\Delta x_{i})\Delta y_{i }-(y_{i}+t\Delta y_{i})\Delta x_{i}\right]dt\ =\ x_{i}y_{i+1}-x_{i+1}y_{i} \tag{7}\]
## 3 Results
### Continuous Wavelet Transform (CWT)
#### 3.2.2 Spectral Energy Analysis in the Frequency Domain \((ES_{i})\)  
Figures 26-28 show the normalized energy distributions for each frequency, which are the result of energy accumulated over time for each sensor. The natural frequencies of the structure for the AV, AVD, ID, and IH cases are also identified. In Figure 26, the AVD case exhibits the highest energy across most frequencies and sensors. There are notable exceptions at 9.35 Hz for S3 and S4 and at 10 Hz for S1, where the AV case instead registers the highest energy.  
Figure 27 illustrates the normalized energy distributions for the ID and IH transient-excitation scenarios applied to the structure. The ID case exhibits higher energy at most frequencies across the sensors. However, at 9.35 Hz for S2 and S3 and 5.75 Hz for S2, the IH case displays the highest energy.  
Figure 24: Scalogram images for the ID and IH case.  
Figure 25: Scalogram images for the \(f_{d1}\), \(f_{d2}\), and \(f_{d3}\) cases.  
Figure 28 shows the comparison of the normalized energy distributions for the three shaker-excitation cases (\(f_{d1},f_{d2},f_{d3}\)). Each case concentrates energy around its excitation frequency, although this behavior is not observed for all sensors. In contrast, the energy amplitudes at the experimentally identified natural frequencies are substantially lower than those at the excitation frequencies. The main observations are presented below:  
* \(f_{d1}\) case: Peak energy occurs at [2.04, 2.18] Hz.
* \(f_{d2}\) case: Peak energy occurs at [6.17, 6.61] Hz.
* \(f_{d3}\) case: Peak energy occurs at [9.35, 10.02] Hz.  
Figure 26: Energy of CWT coefficients normalized in the frequency domain for the AV and AVD cases.  
Figure 27: Energy of CWT coefficients normalized in the frequency domain for the ID and IH cases.  
For the \(f_{d1}\) and \(f_{d3}\) cases, the amplitudes of the energy peaks, ranked from highest to lowest, occur at S3, S4, S2, and S1. In contrast, the \(f_{d2}\) case coincides with the second vibration mode of the structure, causing S2 and S4 to register the highest energy peaks at this frequency. Additionally, S1 and S2 exhibit a pronounced third harmonic peak at approximately \(3\times f_{d2}\) for S1 and S2, with an amplitude twice that of the fundamental frequency (\(f_{d2}\)). Similarly, under \(f_{d3}\) excitation, S1 displays a third harmonic peak that exceeds the fundamental frequency in the energy peak.  
Table 6 reports the RE between the experimental natural frequencies \(f_{m}\) and the theoretical values \(f_{ni}\) for the AV, AVD, ID, and IH cases. It is important to note that under harmonic excitation, the \(f_{ne}\) values do not exhibit significant energy peaks, as illustrated in Figures 25 and 28. In the AV case, the \(f_{n\omega}=1.90\) Hz yields the lowest RE at 1.6% for \(f_{n1}\). Subsequently, all cases maintain a RE below 2.1% for \(f_{n2}\). In contrast, the greatest discrepancy occurs in \(f_{n1}\) for the AVD, ID, and IH cases, where \(f_{ne}=1.77\) Hz and the RE increases to approximately 8.3%.
# Ocular Motility Abnormalities in Ehlers-Danlos Syndrome: An Observational Study
###### Abstract  
Purpose: To evaluate ocular motility (OM) abnormalities associated with Ehlers-Danlos Syndrome (EDS). Materials and methods: In this cross-sectional observational study, patients with EDS underwent a complete orthotopic examination. The following orthopedic tests were performed: corneal light reflex test, stereoscopic test, cover test, OM assessment, evaluation of eye pain in different gaze positions and red filter test for diplopia. Results: The corneal light reflex test at 33 cm showed an intermittent divergent deviation in 31.7% of patients and an intermittent horizontal deviation associated with a vertical deviation in 4.9% of patients. A manifest strabismus was observed in 2.4% of patients, whereas 2.4% of patients showed a microstrabismus. The corneal light reflex test at 5 m revealed microstrabismus in 9.8% and manifest strabismus in 2.4% of our patients. Moreover, intermittent exotropia was observed in 2.4% of cases. No significant alterations involving the inferior rectus and the superior oblique muscles were observed. Significant associations were observed between medial rectus muscle deficit of both eyes with pain (\(p\) = 0.020) and diplopia (\(p\) = 0.014). Furthermore, a significant association between lateral rectus muscle alteration of both eyes and pain was observed (\(p\) = 0.004). Conclusions: Our results show various OM alterations in patients with EDS, specifically superior and medial rectus muscle hypofunction. A full orthoptic evaluation in these patients is recommendable to detect OM involvement and possible ligamentous laxity changes over time through an accurate OM assessment.  
Keywords:Ehlers-Danlos Syndrome; collagen alteration; ocular motility; strabismus +
Footnote †: journal: Medical Centre, 1105 AZ Amsterdam, The Netherlands