esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Overview
### Wireless Signals GAN Model  
So as to achieve better balance, the D model must match the performance of G model in the GAN. The discriminative model D plays two roles in the GAN. One is that can output a predicted value comparing with the real label. Another is that the intermediate feature layer of the discriminator D can be used as unsupervised feature extraction. The structure of the model is shown in Figure 2.  
the D model of the GAN is designed as the deep convolutional neural network, as shown in Figure 2. Here, the dimension of the input sample signal feature reaches 4096. The numbers of feature channels of each convolutional layer are \([16,32,32,64,64,128,128,256,256]\), and the corresponding feature dimensions are \([2048,1024,512,256,128,64,32,16,8]\). Especially, the convolution kernel of each layer is \(31\times 1\), whose nonlinearly mapped function is based on the _Leakyrelu_. Meanwhile, the stride is 2. At last, the output data of the final convolutional layer transpose into one-dimensional features after through the fully connected layers, whose dimension is \(1\times 8\). At the moment, the output of the penultimate layer can be used as the feature, while the output of the last dimension is to judge whether the input signal is real or fake.  
To keep the balance of D and G model, the framework of the generator G is similar to the discriminator D without the fully connected layers, but the convolutional layers of the G model is defined as deconvolution, whose numbers of channels are \([256,256,128,128,64,64,32,32,16,1]\), and the corresponding feature dimensions are \([8,16,32,64,128,256,512,1024,2048,4096]\). The convolution kernel of each layer is also 31. The middle layer takes _Prelu_ as the nonlinear mapping, while the last layer takes _tanh_ as the activation function.  
It is important to choose the correct loss function to achieve the balance of the GAN model. In this paper, we utilize the mean square error to calculate the degree of loss, as Equation (3).  
Figure 2: Framework of the discriminator D in the GAN.  
Also, a regularized loss term is added to the generator at the beginning of the training as in Equation (4) to achieve stable training quickly.  
\[D=avg((h(x)-label)^{2}) \tag{3}\] \[L_{2}=avg(||x_{real}-x_{fake}||) \tag{4}\]
# Realisation of a Novel Functionally Redundant Actuation System for a Railway Track-Switch
###### Abstract  
This paper focuses on modelling, control, realisation and performance analysis of a full-scale demonstrator for a novel railway track switch. For over a century, railway track switches (or points) have been allowing trains to safely change between routes. As they are safety-critical elements of the rail network, when they fail, the signalling system will prevent trains from using that route. This means poor reliability (or lack of availability) leads to significant delays and costs; hence there is huge interest from researchers and engineers in improving the overall reliability of track switches. This paper presents new results, which represent a meaningful first step toward a revolution in the way track switches are actuated. A \"REPOINT-Light\" railway track switch demonstrator is introduced which uses a new concept of locking to allow redundant actuation with three actuator bearers operating under closed-loop feedback control. The new concept, its control system and its mechanical viability are tested in experiments at the Great Central Railway in the UK. To support the design of the actuators and the control system, a dynamic simulation model is developed by co-simulation involving Simulink and Simpack. The experimental results presented are used to validate the models and the paper discusses how the models themselves are used as the vehicle for the design of feedback controllers. Virtual testing of the controllers in simulation is a vital step prior to the implementation and deployment of the controllers in the demonstrator switch. The major contribution of this work is demonstrating, for the first time at full scale on a real-world track switch, that it is possible to use one-out-of-three actuator redundancy to provide fault-tolerant operation of railway track switch.  
railway; track switch; control design; actuator redundancy; fault tolerance; simulation +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Prediction of the Erosion Surface Geometry  
#### 3.3.1 Segmentation of the Erosion Surface Curve  
The mechanism of erosion surface evolution indicates that the soil-breaking depth increases sharply at erosion speeds of \(0.5u_{nz}-u_{nz}\); while at erosion speeds of \(0\)-\(0.5u_{nz}\), the soil-breaking depth increases at a gradually slower rate. Combined with the geometric characteristics of each working condition shown in Figure 8, the erosion surface curve can be divided into straight and curved sections. The slope of the straight line is large, accounting for a large proportion of the erosion depth. The slope of the curve segment gradually decreases to 0, and the shape is similar to a longer 'trailing'. The segmentation of the erosion surface curve is shown in Figure 11. The length and angle of the straight-line segment have important influences on the soil-breaking depth.  
As shown in Figure 10, section AB of the erosion surface is approximately straight, section BC is a curve, \(h_{max}\) is the maximum soil-breaking depth, and \(x_{max}\) is the maximum distance of the erosion surface in the horizontal direction. The determination standard of section AB is as follows: The erosion surface ABC is divided into N points according to the jet distance, and the equal points are fitted to the straight line from point A. The part with correlation coefficients not less than 0.95 belong to the straight-line section, and the rest is a curved erosion surface section. According to this, the position of point B is determined. Combined with the shape of the erosion surface, the proportions of the straight line in the transverse distance, the depth, and the jet distance (erosi  
Figure 11: Division of erosion surface geometry.  
Figure 10: Erosion speed along the way.  
were obtained. The calculation results are shown in Table 4. The average slope angle of section AB is \(70.6^{\circ}\), accounting for 33.1% (\(\alpha_{x}\)) of the horizontal distance of the entire erosion surface, 65.0% (\(\alpha_{h}\)) of the depth, and 49.2% (\(\alpha_{s}\)) of the jet distance. It can be seen that the AB straight-line section has a significant effect on the soil-breaking depth.
## 4 Results and Discussion
### Subject Wise Glucose Level Prediction
#### 4.2.1 Statistical Approach  
In subject wise tests, performance of both PLS- and PCR-based models degraded slightly as can be seen in Table 6. PCR models produced a slightly better result than PLS PCR model built with DeIT features had the highest SEP for both PLS and PCR which are 27.78 and 28.57 mg/dL, respectively. The lowest SEP of 25.50 mg/dL was recorded with PCR model built with first derivative features which is 1.92 mg/dL lower than the PLS-based best performing model. It is important to note that same model had the lowest SEP in the previous experiments too.
## 4 Model Design and Implementation
### Model Algorithm  
The algorithm process of the distributed intelligent edge computing technology proposed in this article for disease risk prediction is summarized as follows:  
In Algorithm 1, \(\{I_{1},\ I_{2},\dots,\ I_{n}\}\) represents the \"Acute Ischemic Stroke MRI\" data set published on Kaggle. \(T\) represents the training sample data, \(V\) represents the verification sample data, \(S\) represents the test sample data, and epoch represents the number of training iterations.  
```
Input:\(\{I_{1},\ I_{2},\dots,\ I_{n}\}\) Output: Disease Risk Prediction Model
1\(T,\ V,\ S\leftarrow\) use the image feature preprocessing method designed in this article to preprocess \(\{I_{1},\ I_{2},\dots,\ I_{n}\}\)
2 for\(n=0\) to epoch do
3 Training a predictive model using Linge network
4 Evaluate training effectiveness
5 Model parameter optimization
6 if the evaluation indicators are qualified:
7 Output prediction model
8 break
9 else:
10\(n=n+1\)
11 Output the model with the highest evaluation
12 The edge side uploads model weights to the central server
13 The edge downloads the model from the central server and performs inference on the edge.
```  
**Algorithm 1** Algorithms for intelligent edge computing technology  
In Algorithm 1, each edge end uses the image preprocessing method to first preprocess the data set published on Kaggle. Then, the preprocessed samples were used to train the lightweight neural network model designed in this article. Secondly, each edge end uploads the model weight to the central server. The edge then downloads the central server model. Finally, the edge side completes the inference.
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 3 Background
### Malicious Behavior in Vishing Apps
#### 3.1.4 Application Repackaging  
Application repackaging is a technique that involves decompiling an application, modifying its code, and recompiling it into a distributable format (such as Android Application Package (APK) or iOS Application Archive (IPA)). Attackers exploit this method by decompiling signed applications, adding malicious code, and repackaging them [26].  
The repackaged applications maintain the layout of the original applications, making it difficult for victims to detect the alterations [27]. Attackers have deceived victims by utilizing vishing applications that encompass the previously mentioned behaviors. Experts caution that individuals and businesses incur annual financial losses amounting to millions of dollars as a result of vishing-related fraud [2; 3]. To prevent vishing fraud, researchers have proposed various approaches to detect vishing behaviors using different methods [4; 28; 29; 30]. Unfortunately, despite these efforts, incidents of financial loss resulting from vishing continue to rise [31]. Therefore, we propose an approach to detect malicious behaviors, specifically Display Overlay, Call Redirection, and Duplicated Contacts, without modifying the Android OS.
## 1 Introduction  
Energy storage and return (ESR) prosthetic feet serve as designated spring systems, storing energy in mid-stance of gait, that again is released for propulsion of the foot in late stance [1; 2]. Increased energy return has been reported to benefit users with increased body propulsion and decreased sound limb loading [3]. Deformation of the foot allows roll-over throughout stance, imitating the response of a biological ankle. Stiffness of the foot contributes to this roll-over, affecting the forces and moments exerted on the residual limb, and users' whole body kinematics [4]. The flexible, energy-storing parts of ESR feet are generally leaf springs, fabricated from fiber reinforced polymer materials. Stiffness curve and damping response is fixed and depends on material properties, shape, and thickness [5; 6]. Feet are prescribed to patients in stiffness categories, primarily based on weight and activity levels [7; 8].  
It has been indicated that ESR foot performance during diverse ambulatory tasks and conditions could be improved further by varying the stiffness of the prosthetic device [4; 9; 10]. Hence, adjustable or adaptable stiffness could be beneficial to users, especially in tasks that differ from the walking motion for which the foot is optimized. Recent publications demonstrate an increased research interest in prosthetic feet that have adjustable stiffness and the effect of prosthetic stiffness on user in different tasks. Prosthetic feet with modular ankle stiffness have been proposed by [PERSON] et al. [11] and [PERSON] et al. [12]. These devices alter stiffness by shifting a support in a beam deflection system, allowing a change in forefoot stiffness between steps. [PERSON] et al. [13] reported that users could, on average, feel a stiffness change of approximately 8% and were consistent when choosing preferred stiffness. [PERSON] et al. [4] showed the effect of forefoot and hindfoot stiffness on walking mechanics at different speeds on a prosthetic with adjustable component stiffness.  
The application considered in this work is the prosthetic foot, Pro-Flex  
a standardized mechanical test [28], where the foot performs a full roll-over under a dynamic load profile. The construction of the model is meant to serve as a platform for iteratively modifying designs. The analysis is transient to enable damping in the model. A new joint, restrained with a spring and damper element, is defined in the modified model. This will result in a more compliant force response of the foot, as shown in previous work [29]. The resulting rotational stiffness of the modified design, for varying damping coefficient in the element is then compared to the original model. Furthermore, the strain energy is analyzed, in order to quantify the dissipation of using damped force coupling for change in stiffness.
# Uncertainty Analysis and Improvement of Propellant Gauging System Applied in Space
## 3 Measuring Uncertainty Analysis and the Modified Model
### Measuring Uncertainty Analysis
#### 3.1.3 Uncertainty Analysis of Each Factor  
The Monte Carlo Method is used in the uncertainty analysis above. The effect of each factor in the uncertainty is obtained. Then, the main factors are chosen to be improved.  
The analysis results of Equations (6) and (7) are given in Figure 2. The effect of \(V_{0}\), \(V_{P}\), \(P_{P_{t}}\), \(T_{P_{t}}\), \(P_{P_{t}}\), and \(T_{P_{t}}\) on the measuring uncertainty is within 0.03% FS. The effect of \(P_{T_{t}}\), \(T_{T_{t}}\), \(P_{T_{t}}\), and \(T_{T_{t}}\) on the measuring uncertainty can reach approximately 4% FS.  
Figure 2: _Cont._It can be easily understood that there is little effect on the measuring uncertainty of the total tank volume, propellant volume, and the temperature and pressure of the injection room before and after gas injection. However, the effect of the temperature and pressure in the tank has a great effect on the measuring uncertainty. In order to improve the final measuring uncertainty, it is necessary to improve the uncertainty of the temperature measurement and pressure gauge.
## 2 Materials and Methods
### Glutamine Synthetae Activity  
The glutamine synthetase (GS) activity was determined based on Hipkin and Syrret [34]. The measure of enzyme activity is the amount of \(\gamma\)-glutamyl hydroxamate formed, which gives a coloured reaction due to the reaction with the ferrous reagent. Plant material (1 g) was ground with the addition of 100 mg polyvinylpyrrolidone (PVP) and 3 cm\({}^{3}\) of cooled extraction buffer [0.05 M Tris HCl, pH 7.2, containing 10 mM MnCl\({}_{2}\), 1 mM dithiothereitol]. The obtained extracts were centrifuged at 15.000 g [Beckman J2-HC Centrifuge] for 15 min at 4 \({}^{\circ}\)C, and then enzyme activity was determined in the supernatants. The following were pipetted into centrifuge tubes: 0.2 cm\({}^{3}\) of incubation buffer (0.05 M Tris HCl, pH 7.2), 0.2 cm\({}^{3}\) of 7.5 mM adenosine diphosphate (ADP), 0.5 cm\({}^{3}\) of 140 mM glutamine, 0.1 cm\({}^{3}\) of 0.3 mM sodium edetate, and 1.0 cm\({}^{3}\) of extract. The control sample was prepared by replacing the glutamine solution with the incubation buffer. The samples were initially incubated for 10 min at 30 \({}^{\circ}\)C, then 0.2 cm\({}^{3}\) of hydroxylamine (10 mM, pH 7.2) was added to each and set again for 30 min at the same temperature. The reaction was stopped by adding 1 cm\({}^{3}\) of iron reagent (FeCl\({}_{2}\) in 4% trichloroacetic acid), and then the samples were cooled and centrifuged at 10.000 g for 10 min. Enzyme activity was determined in the supernatant at 500 nm using a reagent sample (2 cm\({}^{3}\) H\({}_{2}\)O and 1 cm\({}^{3}\) iron reagent) as a reference. The difference between the absorbance of the test sample and the control sample corresponded to the amount of \(\gamma\)-glutamyl hydroxamate read from the standard curve. The activity of the GS enzyme was expressed in micromoles of \(\gamma\)-glutamyl hydroxamate formed within one hour, converted to g of fresh weight [umoles \(\gamma\)-glutamyl hydroxamate \(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW].
## 3 Results
### Exhaust Aerosol Measurements
#### 3.2.2 Real-Time Emission Traces  
Figure 6 shows example real-time traces of non-volatile PN of the HD vehicle over a cold start WHVC test performed. Over the first 200 s of the cycle, the inlet SCR temperature (dashed dark-red curve) was below the operating range of the SCR and no urea was injected. The emission levels were also elevated as is typically the case during cold start operation of vehicles equipped with wall-flow particulate filter [36]. All PEMS instruments agreed to within \(\sim\)25% with the reference PN methodology (REF@23) over this period. Similarly, the 10 nm CPC (REF@10) installed on the reference system measured only 5% higher concentrations than the REF@23, suggesting that the mode of the emitted particles lies well above the regulated size limit.  
After approximately 1000 s, the LD PN-PEMS started progressively measuring higher concentrations than the reference PN, yielding on average 65% higher concentrations over the last 800 s of the cycle. Urea injection was already activated and the exhaust temperature at the inlet of the SCR ranged between 290 to 320 \({}^{\circ}\)C. The excess particle counts registered with the 10 nm CPC compared to the 23 nm one of the reference PN system increased also to 60% over this section, suggesting that the size of the distribution has shifted to lower particle sizes. Despite these changes in the exhaust aerosol, the agreement between the two technical solutions and the reference PN (REF@23) remained within 25% (23% for LD + EF and 12% for LD + TC). The performance of the CPC based PEMS system (OBS) was also affected by the presence of such small particles, yielding on average a 35% difference from the REF@23 concentrations.  
Figure 7 summarizes the results obtained over a segment of a torque-ramp test (1000 rpm/55% load) of the HD vehicle. The concentrations measured with the reference PN system with both the 23  
Figure 6: Real-time PN and SCR inlet temperature traces over a cold start world harmonized vehicle cycle test of the HD vehicle. The section of the cycle from 200 to 900 s is removed for the sake of clarity.  
and 10 nm CPC remained relatively stable (\(\pm\)10%). The 10 nm CPC (REF@10) measured on average -50% higher concentrations than the 23 nm one (REF@23) (Figure 7a). The SMPS measurements (Figure 7b) revealed a stable size distribution peaking at \(\sim\)15 nm, which explains the higher particle counts registered by the 10 nm CPC. The LD + TC and OBS systems exhibited a constant difference from the reference PN, of \(<\)5% and 25%, respectively. The LD system agreed to within 5% with the reference PN over the first 200 s of the test, but then gradually started measuring higher concentrations with the difference reaching 70% over the last 100 s of the test. This gradual deterioration of the LD performance coincided with an increase in the inlet SCR temperature from \(\sim\)300 \({}^{\circ}\)C to 400 \({}^{\circ}\)C and an accompanying increase of the average exhaust aerosol charge from \(\sim\)0 to as high as 1.9 charges/particle.
## 3 Method
### Global Pooling for Aggregation  
The proposed DSNet aims to predict both segment-level and clip-level labels. To train DSNet with only weak labels (clip-level labels), we need to aggregate segment-level predictions to form clip-level predictions. A common approach would be taking an average over all segment predictions corresponding to a clip-level prediction. In this approach, all segments of the clip have the same influence on the clip-level prediction. However, clips with a positive label can also contain negative segments, which disturb the training process. In the multiple instance learning framework, a global max-pooling is applied to aggregate segment-level predictions into a clip-level prediction. In the max pooling approach, the clip-level prediction focuses on the most positive segment in the clip and disturbance from negative segments can be reduced. However, with global max-pooling aggregation, only the most positive segment in each clip is active in training during backpropagation and other segments are ignored.  
To take advantage of both methods, we apply the LogSumExp (LSE) function, which is a smooth approximation of the max function. The LSE function is given as  
\[y_{k}=\frac{1}{\alpha}\log(\frac{1}{T}\sum_{l=1}^{T}\exp(\alpha*s_{t,k})) \tag{5}\]  
where \(y_{k}\) is a clip-level prediction for class \(k\), \(s_{t,k}\) is the segment label of the \(t\)th segment for class \(k\) and \(T\) is the number of segments in a clip. In Equation (5), \(\alpha\) is a hyperparameter (a parameter whose value is set before training) to control the sharpness of the function. As \(\alpha\) increases, the function approaches to the max function and, as \(\alpha\) decreases, the function approaches the average function. With the LSE pooling, we can use all the segments of the clip during training and also focus on positive segments in positive clips. We set the parameter \(\alpha=0.5\) in our system. To train our model with only weak labels, we apply the mean square error as the cost function, which is given by  
\[C_{cl}=\frac{1}{N}\sum_{n=1}^{N}||L_{n}-Y_{n}||^{2} \tag{6}\]where \(L_{n}\) denotes the true label for the \(n\)th clip, \(Y_{n}\) is the clip-level prediction for the \(n\)th clip, and \(N\) is the number of clips in the training data.
###### Abstract  
Forest canopy height model (CHM) is useful for analyzing forest stocking and its spatiotemporal variations. However, high-resolution CHM with regional coverage is commonly unavailable due to the high cost of LiDAR data acquisition and computational cost associated with data processing. We present a CHM generation method using U.S. Geological Survey (UGS) 3D Elevation Program (3 DEP) LiDAR data for tree height measurement capabilities for entire state of Indiana, USA. The accuracy of height measurement was investigated in relation to LiDAR point density, inventory height, and the timing of data collection. A simple data exploratory analysis (DEA) was conducted to identify problematic input data. Our CHM model has high accuracy compared to field-based height measurement (R\({}^{2}\) = 0.85) on plots with relatively accurate GPS locations. Our study provides an easy-to-follow workflow for 3 DEP LiDAR based CHM generation in a parallel processing environment for a large geographic area. In addition, the resulting CHM can serve as critical baseline information for monitoring and management decisions, as well as the calculation of other key forest metrics such as biomass and carbon storage.  
canopy height model; LiDAR; forest inventory +
Footnote †: journal:  
1
Footnote 1: Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47907, USA; [EMAIL_ADDRESS] (S.O.); [EMAIL_ADDRESS] (G.S.); [EMAIL_ADDRESS] (S.F.)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).