esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2
## 1 Introduction
The MobileNet V2 architecture [26] was designed to create small, low-latency applications such as computer vision and IoT applications in many fields, namely fruit image classification for higher fruit production rates [27]; an artificial intelligent diagnostic system was built for cholelithiasis [28]. In this work, we propose a new method to combine transfer learning with feature selection to improve the state-of-the-art medical classification methods. After this section, we list some of the previous state-of-the-art lung disease classification methods in the related work Section 2. Dataset and disease distribution are discussed in Section 3. We present our model architecture and evaluation procedure in Section 4. Experimental results and comparisons with other works are presented in Section 5, The conclusions are presented in Section 6. \begin{table}
\begin{tabular}{l l c} \hline \hline  & **Method** & **Accuracy (\%)** \\ \hline \multirow{2}{*}{[PERSON], [PERSON] et al. [9]} & A Novel Method for Detection of Tuberculosis in Chest & \multirow{2}{*}{Dataset 1: 90.2\%} \\  & Radiographs Using Artificial Ecosystem-Based Optimisation & \\  & of Deep Neural Network Features & \\ \hline \multirow{2}{*}{[PERSON] et al. [11]} & Parallel Deep Learning Algorithms with Hybrid Attention & \multirow{2}{*}{94.61\%} \\  & Mechanism for Image Segmentation of Lung Tumors & \\ \hline \multirow{2}{*}{[PERSON], [PERSON] et al. [18]} & Customized VGG19 Architecture for Pneumonia Detection in & No Threshold filter: 95.70\% \\  & Chest X-rays & Threshold filter: 97.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: New classification method with an accuracy higher than 90%.
# Single-Pixel Color Imaging Method with a Compressive Sensing Measurement Matrix
## 1 Introduction
On the basis of the original single-pixel camera system, the design scheme of the single-pixel color camera is utilized in this study to acquire color information and restore real images and scenes.
## 2 Theory
### Traditional Cam-Shift Algorithm
#### 2.2.3 Cam-Shift Tracking  
The key point of a Cam-Shift algorithm is that it can adjust the size of search window and keep tracking when the size of the target area changes. The second moments \(M_{11}\), \(M_{20}\), and \(M_{02}\) of the search window reflect the target's orientation information. Specifically, the Cam-Shift algorithm adjusts the search window with the orientation information, which is obtained using a Mean-Shift iteration and determines the size of the search window in the next frame. \(M_{11}\), \(M_{20}\), and \(M_{02}\) are calculated using:  
\[\left\{\begin{array}{l}M_{11}=\sum\limits_{x}\sum\limits_{y}I(x,y)xy\\ M_{20}=\sum\limits_{x}\sum\limits_{y}I(x,y)x^{2}\\ M_{02}=\sum\limits_{x}\sum\limits_{y}I(x,y)y^{2}\end{array}\right. \tag{3}\]  
According to the center of mass and second moments, there are three variables \(a\), \(b\), and \(c\) that can be expressed as:  
\[\left\{\begin{array}{l}a=\frac{M_{20}}{M_{00}}-x_{c}^{2}\\ b=\frac{M_{11}}{M_{00}}-x_{c}y_{c}\\ c=\frac{M_{02}}{M_{00}}-y_{c}^{2}\end{array}\right. \tag{4}\]  
Then the direction angle \(\theta\), the major axis \(l\), and minor axis \(\omega\) are obtained using:  
\[\left\{\begin{array}{l}\theta=\frac{1}{2}tan^{-1}(\frac{2b}{a-c})\\ l=\sqrt{\frac{(a+c)+\sqrt{(2b)^{2}+(a-c)^{2}}}{2}}\\ \omega=\sqrt{\frac{(a+c)-\sqrt{(2b)^{2}+(a-c)^{2}}}{2}}\end{array}\right. \tag{5}\]  
Thus, the initial size of the search window of the next frame is:  
\[\left\{\begin{array}{l}width=max(l\cos\theta,\omega\sin\theta)\\ height=max(l\sin\theta,\omega\cos\theta)\end{array}\right. \tag{6}\]
## 5 Optimization of Scan Parameters
### Uncertainty Area Size  
The uncertainty area size can also be varied during the scanning phase. There is an initial offset error \(\sigma\) that exists between the transmitter and receiver satellites, which arises due to pointing errors, satellite attitude, thermal distortions, and ephemeris data. However, as demonstrated by the authors in [40], changing the uncertainty area size without altering the overlap factor or the beam divergence in the presence of vibrations does not reduce the scan gaps. Nevertheless, in terms of the mean acquisition time, two different values, i.e., \(u=3\sigma\) or \(u=1.3\sigma\), can be used.  
Since the radial error follows a Rayleigh distribution according to Equation (9), the acquisition probability \(P_{s}\) of the target receiver satellite appearing in the uncertainty region can be obtained by integrating Equation (17) as  
\[P_{s}=\int\limits_{0}^{u}f(r)\,dr=1-\exp\left(-\frac{u^{2}}{2\sigma^{2}}\right), \tag{80}\]  
where \(u\) is half the size of the uncertainty area.  
Figure 8 depicts the acquisition probability versus the ratio \(\frac{u}{\sigma}\). This demonstrates that setting \(u=3\sigma\) is sufficiently large to cover the probable positions of the target satellite and acquire it with a probability exceeding 98%. The authors in [46] suggested and demonstrated that the optimum uncertainty area size, \(u=1.3\sigma\), is better than the traditional approach, especially at larger values of \(\sigma\).
## 4 Simulation Verification
### The Parameter Settings  
In the 3-DOF structure, the window length \(L\) is 1024, sample frequency \(f_{s}\) is 40 Hz and sample time is 2000 s. Frequency resolution is \(\triangle f=0.039\) Hz, the first average frequency variation is \(\triangle f_{L}(1)=9.25\times 10^{-4}\)Hz, the second average frequency variation is \(\triangle f_{L}(2)=0.0130\) Hz, the third average frequency variation is \(\triangle f_{L}(3)=0.0426\) Hz. Therefore, the length \(L=1024\) satisfies average frequency variation \(\triangle f_{L}(i)\) cannot be lager too much than frequency resolution \(\triangle f\).  
In the cantilever beam structure, the window length \(L\) is 2048, sample frequency \(f_{s}\) is 10,000 Hz and sample time is 4 s. Frequency resolution is \(\triangle f=4.88\) Hz, the first average frequency variation is \(\triangle f_{L}(1)=0.15\) Hz, the second average frequency variation is \(\triangle f_{L}(2)=1.09\) Hz, the third average frequency variation is \(\triangle f_{L}(3)=3.06\) Hz. Therefore, the length \(L=2048\) satisfies average frequency variation \(\triangle f_{L}(i)\) cannot be lager too much than frequency resolution \(\triangle f\). The parameter \(K\) in LPP algorithm is 40, and the low-dimensional embedded dimension is 3.
## 1 Introduction
In addition, our EMMNet has following features compared with other existing remote meter monitoring systems:  
* Dedicated design of radio-frequency (RF) circuit. Application of automatic electricity meter monitoring faces various challenges, such as tall buildings in urban areas, long distances in suburban regions, and signal interference. However, the indoor communication range of the most typical sensor network platforms is about 25 m and this distance cannot meet the requirements of electricity meter monitoring. Therefore, we added broadband, fixed-gain, linear amplifiers, and low noise amplifiers to the data collectors and the data concentrators. A data concentrator is equivalent to a base station or gateway in sensor networks. * High reliability and performance. Some existing automatic meter monitoring systems utilize ZigBee [12], which is a specification for a suite of high level communication protocols for wireless personal area networks. But ZigBee can only operate in three radio bands, which limits its applications. A high-performance dynamic tree (DT) protocol is proposed in this paper. It is based on CSMA/CA and has no frequency limit. Even if some nodes (data collectors) lose their connection frequently because of environmental changes, other nodes can quickly repair the routes. * Low operational cost. Compared with other existing remote meter monitoring systems based on GPRS [13], the operational cost of EMMNet is relatively low because only the data concentrators incur in costs for using GSM/GPRS network. * Changeability. China Power System is currently being reconstructed, and the requirements of the automatic electricity-meter-reading system change from time to time. Data collectors and data concentrators in EMMNet support wireless reprogramming, which can satisfy customer requirements in a timely manner.
## 5 Numerical Results and Discussion
In Figure 4a,b, we illustrate the profiles of the DSP versus dust parameter \(P_{d}\) with different values \((0.1,0.4,0.8)\) of Xenon ion streaming velocity [\(U_{Xe^{+}}=m_{x_{Xe^{+}}}V_{Xe^{+}}/2T_{e}\)] and Argon ion streaming velocity [\(U_{Ar^{+}}=m_{x_{Xe^{+}}}V_{Ar^{+}}/2T_{e}\)] with a fixed value of spectral indices \(\alpha\)\((=0.02)\) and \(q(=0.95)\) by using Equations (10) and (14), respectively. We observed that by increasing the values of \(U_{Xe^{+}}(0.1,0.4,0.8)\) and \(U_{Ar^{+}}(0.1,0.4,0.8)\), the magnitude of DSP increases, while the value of negative streaming velocity stays the same. In Figure 5a-d, the DSP curves are plotted as a versus \(P_{d}\) for different cases of negative ions (\(U_{F^{-}}\) and \(U_{S^{-}_{Ti_{6}}}\)) for both types of plasma. Figure 3: (Color online) \(\psi\) versus \(P_{d}\) for \(q(0.9,0.95,1.0)\) with \(\alpha=0.01\); (**a**) xenon–fluorine–sulfur hexafluoride as given in Equation (10) (**b**) for argon–fluorine–sulfur hexafluoride plasma as given in Equation (14). Figure 5a shows the variation of the DSP against the \(P_{d}\) of \(Xe^{+}-F^{-}-SF_{6}^{-}\) plasma for different values of \(U_{F^{-}}(=0.1,0.4,0.8)\), with \(\alpha=0.02\) and \(q=0.95\) kept the same. It is observed that the DSP increases by increasing the values of streaming fluorine ion. Figure 5b examines the streaming effect of \(U_{\text{${}_{SF_{6}^{-}}$}}\) on dust potential with \(U_{F^{-}}\), and the remainder of the parameters are the same as in Figure 2a. The streaming influence of \(U_{\text{${}_{SF_{6}^{-}}$}}\) on DSP is the same as \(U_{F^{-}}\), but the difference between the curves of \(U_{\text{${}_{SF_{6}^{-}}$}}\) is smaller than \(U_{F^{-}}\). The effects of \(U_{F^{-}}\) and \(U_{\text{${}_{SF_{6}^{-}}$}}\) on DSP for \(Ar^{+}-F^{-}-SF_{6}^{-}\) plasmas are shown in Figure 5c,d. The non-monotonic change in dust potential can also be seen in Figure 5c,d, with comparable consequences to those shown in Figure 5a,b. The physical significance for the observed trend is mainly due to the positive/negative ion streaming that gradually increases the relative potential (\(\psi\)) of ions as well as the negative charge dust particles. The dust-surface potential increases with the increase in ion velocity but decreases with the collisions of ions with dust particles.
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
### Contributions and Organization  
Any-angle pathfinding methods usually result in shorter paths. Inspired by the delayed strategy of Lazy Theta*, we present Late LoS-Check A* (LLA*) to further delay the LoS-Check while updatingthe values of different successors with discriminatory principles. LLA* presented in this paper is based on the tree structure, which is especially useful for path planning under differential constraints. The contributions of this paper include:  
* Combine LoS-Check, the late evaluation strategy, and discriminatory updating together.
* Use the prioritized trees to implement the discriminatory updating.
* Present an any-angle post-smoothing method for path planning.
* Evaluate our algorithms on some benchmark maps and integrate them into the Open Motion Planning Library (OMPL) [51].  
This paper is a modified and extended version of a publication of IETEElectronics Letters [52]. The original version only described a simple and short outline of the LLA* algorithm, which is based on the graph structure. In this paper, we present additional experiments and implement LLA* on the basis of OMPL with a tree structure. We also present a post-smoothing method based on LoS-Check to validate the performance of the LLA* algorithm. The final results show that LLA* runs faster and generates shorter paths than Lazy Theta*.  
LLA* and Lazy Theta* both use the lazy evaluation strategy, and the biggest difference between them is that LLA* also employs the discriminatory principles. In Lazy Theta*, every state has the equivalent chance to be performed with LoS-Check, while in LLA*, LoS-Check is only performed on those states that own a lower estimate cost. It is essentially a \"winner takes all\" strategy. Namely, the state that has a low estimate cost will get even lower cost after LoS-Check. The discriminatory updating is a kind of greedy strategy, which could speed up LLA*. The worst case for LLA* is degrading into the A* algorithm, so it always generates shorter than or equivalent paths to A*.  
Paper organization: The remainder of this paper is organized as follows. Section 2 defines some important notations, which will be used in context. Section 3 describes the mechanisms of A*, Theta*, and Lazy Theta*. The key part of this paper is given in Section 4, which explains the details of the LLA* algorithm, including the late LoS-Check operation and the discriminatory updating strategy. A kind of post-smoothing method based on the LoS-Check is introduced in Section 5. To verify the performance of LLA*, Section 6 presents the results of the sufficient experiments comparing LLA* with the other three algorithms on some benchmark maps. Lastly, we give a conclusion about our work in Section 7.
# A Novel 1-D CCANet for ECG Classification
## 3 Proposed Method
### Hand-Crafted Feature Extraction  
Given an isolated heartbeat \(x_{h}\ (h=1,2)\), hand-crafted features are extracted with three different methods: frequency-domain, time-domain, autoregressive modeling.
# Nano-HPLC-HRMS Analysis to Evaluate Leptin Level in Milk Samples: A Pilot Study
## 3 Results  
To quantify leptin in milk samples (human, bovine, and infant food) we used the selected peptide with \(m\)/z ratio of 708.3599, z = 2. The calibration curves in bovine milk were in the ranges between 0 and 3000 ng mL\({}^{-1}\). No leptin was quantified in bovine whole milk without fortification used for the calibration curves. Curves were repeated three times and the coefficients of correlation R\({}^{2}\) were very close to the unit (Figure 1). The obtained lower limit of quantitation (LLOQ) was 100 ng mL\({}^{-1}\), and the LOD (limit of detection) was 27 ng mL\({}^{-1}\).  
With the developed calibration curve leptin was detected and quantified in milk samples. Each sample was repeated three times with the use of one immunoaffinity column for each. Human breast milk samples showed an average concentration of leptin of 6.70 ng mL\({}^{-1}\). Bovine milk, bovine colostrum and infant formula products showed an amount of protein under lower limit of quantitation. However, bovine leptin was identify using Mascot software in bovine colostrum, as expected. In Table 3, the results of milk samples are listed.  
\begin{table}
\begin{tabular}{c} \hline
**Human Leptin (Homo Sapiens)** \\ \hline MHWGTLCGFLWLWPYLFYVQAVPIQKVQDDTKTLIKITVTRINDISHTQSVSSKQKVTGLDFIP \\ GLHPILTLSKMDQTLAVYQQILTSMPSRNVIQISNDLENLRDLLHUAFSKSCHLPWASGLETLD \\ SLGGVLEASGYSTEVVALSRLQGSLQDMLWQLDLSPGC \\ \hline Bovine leptin (Bos taurus) \\ \hline MRCGPLYRFLWLWYPLSYVEAVPIRKVQDDTKTLIKITVIRINDISHTQSVSSKQRVTGLDFIPG \\ LHPLLSLSKMDQTLAYQQILTSLPSRNVVQISNDLENLRDLLHLAASKSCFLPQVRALESLES \\ LGWVLEASLYSTEVVALSRLQGSLQDMLRQDLSPGC \\ \hline \end{tabular}
\end{table}
Table 2: Comparison between sequence of human (UniProt KB code P41159, Homo sapiens (Human)) and bovine (UniProt KB code P50595, Bos taurus (Bovine)) leptin. Bold letters denote the different amino acids between human and bovine leptin sequence. Italic and underline letters indicate the selected peptides to qualify and quantify human leptin. The first peptide is in common between species.  
Figure 1: Calibration curve in breast milk sample processed with immunoaffinity purification. LLOQ and LOD were 100 and 27 ng mL\({}^{-1}\) respectively.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 2 Methodology
### Multi-Arc Parallel Normal Equations Construction Based on MPI  
In the multi-GNSS ultra-rapid orbit determination, the sequential batch processing method is adopted. The double-differenced carrier observation equation can be expressed as:  
\[LC_{kl}^{ij}(t)=\rho_{kl}^{ij}(t)+\Delta T_{kl\mathit{t}top}^{ij}(t)+N_{kl}^{ ij}\cdot\lambda_{c}+\epsilon^{\prime}{}_{\Phi} \tag{1}\]  
where \(t\) represents epoch, \(LC_{kl}^{ij}\) represents the ionospheric-free phase observation of station \(k,l\) and satellite \(i,j\), \(\rho_{kl}^{ij}\) denotes pseudorange, \(\Delta T_{kl\mathit{t}top}^{ij}\) denotes tropospheric delay, \(N_{kl}^{ij}\) is ambiguity, \(\lambda\) is the wavelength of ionospheric-free observations, and \(\epsilon^{\prime}_{\Phi}\) is the observation noise. The observation equation (1) is linearized and expressed as:  
\[LC_{kl}^{ij}(t)=\rho_{kl0}^{ij}(t,X_{r0},X^{s}(t))+B\cdot\delta X_{r}+H\cdot \delta X_{0}^{s}+\Delta T_{kl}^{ij}(t)+N_{kl}^{ij}\cdot\lambda_{c}+\epsilon^{ \prime}_{\Phi} \tag{2}\]  
where \(B=\partial\rho_{kl}^{ij}(t)/\partial X_{r}\) is the partial derivative of the measured value with respect to the state parameters, \(\Psi(t,t_{0})\) is the transition matrix which can be obtained by solving the variational equation, \(X_{r0}\), \(\delta X_{r}\) are the approximate coordinates of the station and their corrections, respectively, \(X^{s}(t)\) is the state of satellite at time \(t\), which is obtained by solving the differential equation of satellite motion, \(\rho_{kl0}^{ij}(t,X_{r0},X^{s}(t))\) is the approximate value of the double-differenced satellite-ground geometric distance at time \(t\), and \(\delta X_{0}^{s}\) is the correction of the satellite state at the reference time. Other symbols have the same meaning as in (1). Based on the observation (2), the GNSS satellite reduce-dynamic precise orbit determination (RDPOD) can be performed. Table 2 shows the main parameter settings of the RDPOD strategy. In this paper, we have no a priori box-wing model used for the orbit determination, which may generate orbit errors, especially for Galileo satellites due to the elongated shape of the satellite buses. The a priori box-wing model may be a solution for twice-per-revolution empiri-cal parameters from the CODE's new solar radiation pressure model are used for GNSS orbit determination [32]. In the paper, we adopt the one-step orbit determination of the GPS, GLONASS, Galileo and BDS in order to study that the parallel algorithm improves the efficiency of satellite orbit prediction, which does not affect the computational efficiency of the parallel algorithm. For BDS, no official metadata for a priori box-wing models are available. However, estimated parameters can be still used [33].
###### Abstract  
One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than \(C/2\) corrupted errors where \(C\) is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than \(C/2\) corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve [PERSON] and [PERSON]'s model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.  
network coding; error propagation; error correction; L1 optimization; social network +
Footnote †: journal: Sensors  
18450
# LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites
## 5 Experimental Setup
### Data Augmentation  
Deep learning models need to be trained on a large dataset. A small data set could cause over-fitting (i.e., performing well on the training data but not on the testing data). To deal with this challenge of having a limited number of the training images, label-preserving data augmentation is used. In this research similar to krizhevsky et al. [47], different methods of data augmentation such as random cropping, random color jitter, and flipping are implemented (see Figure 7).  
Random cropping randomly selects a smaller region of the raw image, along with its corresponding annotation--therefore, \"label-preserving\" as mentioned previously. Then the region is resized to the raw image size and considered as a new sample. Random cropping utilizes the translation-invariance feature of the object in the dataset (see Figure 7a. Random color jitter makes a slight change in the color values of the image. The purpose of this operation is to simulate and generate different lighting conditions (see Figure 7b. A gamma correction function has been used as the Equation (7) shows. \(V_{in}\) is the color value of every pixel in the input images. \(V_{out}\) is the value in the output images, corresponding to the input images pixel by pixel. This equation randomly chooses the parameter \(\gamma\) from 0.5 to 1.5 for each image. Finally, regarding Figure 7c, flipping flips the images horizontally. These three methods are also applied simultaneously to generate more samples. During training, making data regularized is a common trick in deep learning, and gray-scale images are rescaled from 0-255 to 0-1. Each image in the original dataset could generate 32 new images through this data augmentation approach.  
\[V_{out}=V_{in}^{\gamma} \tag{7}\]  
Figure 6: Dataset: (**top**) proposed dataset image, (**bottom**) proposed dataset labels.