esa-sceva/satcom-chunk-collection huggingface dataset:
# Meta-Data-Guided Robust Deep Neural Network Classification with Noisy Label
## 3 The Proposed CoNet-MS Framework and Its Main Modules  
The main notations and their meanings used in this paper are given in Table 1 for clarity of presentation.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(N\) & The number of samples in the training set \\ \(M\) & The number of samples in the clean validation set \\ \(n\) & The batch size of training set \\ \(m\) & The batch size of clean validation set \\ \(D_{tra}\) & The set of all training samples \(\{\left(x_{i},y_{i}\right)|i=1,\cdots,N\}\) \\ \(D_{val}\) & The set of all validation samples \(\left\{\left(x_{i}^{v},y_{i}^{v}\right)|i=1,\cdots,M\right\}\) \\ \(D_{test}\) & The set of all test samples \(\left\{\left(x_{i}^{t},y_{i}^{t}\right)\big{|}i=1,\ldots,S\right\}\) \\ \(f_{\theta}\) & The classifier parameterized by \(\theta\) \\ \(f_{\varphi}\) & The classifier parameterized by \(\varphi\) \\ \(L\) & Cross-entropy loss function \\ \(y_{i}^{pseudo}\) & Pseudo-label of the sample \(x_{i}\) \\ \(C\) & The number of classes in dataset \\ \(f_{\theta}(x_{i})\) & The class probability \(\{p_{1},p_{2},\ldots,p_{C}\}\) of \(x_{i}\) output by classifier \((\theta)\) \\ \(P(t)\) & The high-confidence proportion of samples in the \(t\)-th epoch \\ \(\alpha_{i}\) & The weight loss of the pseudo-label for \(x_{i}\) \\ \(\beta_{i}\) & The weight loss of the observed label for \(x_{i}\) \\ \(\alpha_{i}^{*},\beta_{i}^{*}\) & The normalized weights of \(\alpha_{i}\) and \(\beta_{i}\) \\ \(\{x_{\theta},y_{\theta}\}\) & The high-confidence dataset selected by \(f_{\theta}\) \\ \(\{x_{\varphi},y_{\varphi}\}\) & The high-confidence dataset selected by \(f_{\varphi}\) \\ \(\theta^{*}\) & Parameters of the meta-net \\ \(\theta^{(t)}\) & Parameters of the upper-branch classifier in the \(t\)-th epoch \\ \(\eta\) & The step size for updating \(\alpha,\beta\) \\ \(\lambda\) & The step size for updating \(\theta,\varphi\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main notations and their meanings throughout this paper.
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
## 6 Results
### Experimental Laboratory Results
The points which are not mentioned in Table 4 are selected similarly as illustrated in Figure 5. The angle \(\varphi\) of these points differs from zero. These points are placed at positions covering the entire FOV. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**Normalized Image Distance H (+)**} \\ \cline{2-7}  & **0** & **0.17** & **0.33** & **0.50** & **0.67** & **0.83** \\ \hline
4 th order & 4.6 & 5.1 & 5.3 & 7.8 & 7.5 & 9.4 \\
6 th order & 5.4 & _7.7_ & 6.5 & 9.4 & 7.7 & 12.5 \\
8 th order & 5 & 6.4 & 6.1 & 7.7 & 7.6 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected on-axis point results for the MAXDIFF difference between the original and the estimated model, where \(H\) is from 0 to 0.83 and \(\varphi\) is equal to zero. The MAXDIFF differences of the polynomial orders are given as a percentage. Highlighted columns contain results used later in the comparison. Figure 13: Experimental results for the MAXDIFF difference up to the 8 th order. The graph contains results from Table 4 and ten other points according to the normalized image distance \(H\). The points which are not mentioned in Table 4 are selected similarly as illustrated in Figure 5. The angle \(\varphi\) of these points differs from zero. These points are placed at positions covering the entire FOV. When we compare our method for estimating field-dependent PSF with other approaches mentioned in the introduction, we obtain similar results. Results from all implemented approaches are illustrated in Figure 14. An approach based on work by [PERSON] (2010), labeled as the Interpolated model, provide results of the MAXDIFF operator from 7.8% to 9.9% over the FOV. Another approach based on [PERSON]'s method [PERSON] (2011), labeled as the Sectorized model, which divides the image into smaller invariant parts, provides similar results to the Interpolated model. However, it can be seen that the Sectorized model may fail when the PSF model in one sector does not precisely fit to all PSFs inside the sector. This can be seen in Figure 14, where the results for the Sectorized model (marked with a black+) at \(H\) = 0.83, 0.88, and 0.92 vary from 8% to 17%. The last method included here is fitting the PSFs with a Gaussian function (Rasm  
description of space-variant imaging systems. The method was also used for modeling the WILLIAM all-sky camera [51], where the modeling method faced the issue of the presence of the Bayer mask in the system.
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 4 Method: A Design of an Extensive Rehabilitation Platform
#### RehabHand: An Example of an Extensive Rehabilitation Platform  
In this paper, we will concentrate on the communication layer mentioned above. We believe it needs to be described formally, close to domain terminology, and support automation (verification, validation, and tool generation). This can be achieved with the use of DSLs instead of using classical APIs or services. We developed RehabHand, which is based on language processing technology, as shown in Figure 3. The medical staff writes a new exercise in the form of a program in the language RehabHand. This program represents customized exercises for an individual patient. The DSL program is then taken by the parser, which checks the exercise structure. The next step in the language processing algorithm is the code generation, that generates the target device's program. The RehabHand application then ensures that the translated code is uploaded to the target device. The last step is that the rehabilitation application starts the communication with the rehabilitation device (for example, the Arduino-based rehabilitation hand).  
All the alternatives in the domain of Hand Rehabilitation are combined inside the DSL, and a specific hand exercise is actually defined with a programming language program. We believe preparing exercises in such a manner is easy, transparent, and unambiguous. Such a program is used as input in the communication layer to connect with a specific rehabilitation device. As will be shown in our examples, the exercise defined with the language RehabHand is intuitive. We used the code generation technique's benefits and compiled our code to three different devices. We have two professional rehabilitation devices: Bionic arm (bionic arm manipulator connected with the Arduino Mega 2560 bought on [[https://world.taobao.com/](https://world.taobao.com/)]([https://world.taobao.com/](https://world.taobao.com/))), and Cyton (Robot hand produced by the Robai company, [[http://new.robai.com/](http://new.robai.com/)]([http://new.robai.com/](http://new.robai.com/))) [37]. As a third rehabilitation hand, we decided to create a custom-made rehabilitation aid, an Arduino-based device. Most of the hand was 3D printed. This rehabilitation hand was used as a proof of concept and a testing platform during the RehabHand application development.  
Other parts of the communication layer are described just briefly in the paper. One important part is RehabHandInterpret, which enables medical staff to select a list of exercises a patient needs to perform during a rehabilitation treatment. The order of exercises, number of repetitions, and so forth, are selected before being uploaded to specific hardware. These functionalities' main benefit is that all exercises for an individual patient are uploaded simultaneously to the rehabilitation hand. Application controls which exercise is the next to be performed by a patient.
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 1 Introduction
The images on the second row represent the enlarged drawings of the corresponding red boxes in the first row. (**a**) Objects vary greatly in scale. (**b**) Small objects are easily submerged by background noises. (**c**) Objects appear in various orientations and are densely arranged. difficulties of remote sensing images with large variations in object scale, submerged small objects having insufficient detail features and arbitrary direction objects, the proposed method builds a feature enhancement fusion network (FEFN), designs a hybrid attention mechanism (HAM) module, and uses box boundary-aware vectors for targeted treatment. First, for large variations in object scale, FEFN carries out dilated convolution with different dilation rates to enhance the original fused features, and thus fuses multi-scale, multi-receptive field feature maps to make full use of different level information. The enhanced and fused features are more discriminative and robust, which are conducive to multi-scale object detection. Second, for small objects having insufficient detail features, HAM module proposes a supervised pixel attention to reduce background interference and retain certain context, which can locate the object position more accurately. Moreover, the HAM module also uses a channel attention to focus on learning the high correlation channels, which can guide the training to pay more attention to informative and relevant regions. Context dependence and channel correlation are helpful to supplement the missing features of small objects. Thus, the HAM module can suppress background noises to prevent small objects from being overwhelmed and facilitate the detection of small objects. Finally, the objects are located by using box boundary-aware vectors, which contain four vectors pointing from the center point to the edge of the object. Box boundary-aware vectors determine the location of the bounding box more precisely and detect the arbitrary direction object accurately, even if the objects are densely arranged. By solving the above problems, the proposed FEHA-OD can improve the accuracy of object detection in remote sensing images.
## 3 Experimental Setup  
The experimental setup is shown in Figure 2. The 852 nm probe beam was provided by a tunable diode laser (external-cavity diode laser (ECDL) (Toptica, Munich, Germany) or distributed Bragg reflector (DBR) diode laser). Part of the laser output was used to lock the laser frequency to the Cs 5\(p^{6}\)6s \({}^{2}\)S\({}_{1/2}\) (\(F=4\))-5\(p^{6}\)6p \({}^{2}\)P\({}_{3/2}\) (\(F^{\prime}=5\)) transition using polarization spectroscopy (PS). The main beam of the 852 nm laser overlapped with the 698 nm beam provided by an ECDL with a typical linewidth of \(\sim\)200 kHz. The two beams were transmitted through the Cs atomic vapor cell in the opposite direction (CTP configuration). The 852 nm probe beam had a 1/e\({}^{2}\) Gaussian diameter of \(\sim\)600 \(\upmu\)m and the 698 nm coupling beam had a 1/e\({}^{2}\) Gaussian diameter of \(\sim\)840 \(\upmu\)m. The Cs vapor cell was placed in a \(\mu\)-metal box in order to decrease the influence of the background magnetic field. A wavelength meter (Advantest, TQ-8325, Japan) was employed to monitor the wavelength of the 698 nm laser in real-time.
# Power Prediction of Bifacial Si PV Module with Different Reflection Conditions on Rooftop
###### Abstract  
A bifacial solar module has a structure that allows the rear electrode to be added to the existing silicon photovoltaic module structure. Thus, it can capture energy from both the front and rear sides of the module. In this paper, modeling is suggested to estimate the amount of energy generated from the rear of the bifacial photovoltaic module. After calculating the amount of irradiance from the rear side, the estimated power generation is compared with the real power output from the rear side of the module. The experiments were performed using four different environments with different albedos. The theoretical prediction of the model shows a maximum of 5% and average of 1.86% error in the measurement data. Based on the nature of the bifacial solar module, which receives additional irradiance from the rear side, this study compared the output amounts with respect to different rear environments. Recently, installation of floating Photovoltaic has been increasing. As the reflection of irradiation from the water surface occurs, the positive influence of the installation with the bifacial photovoltaic can be expected. We are confident that this research will contribute to zero energy construction by designing systems based on bifacial PV module with high performance ratio when applying solar power in a microgrid environment, which is the future energy.  
bifacial PV module; prediction of back irradiance; rooftop photovoltaic system +
Footnote †: journal: Applied Sciences
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 4 Proposed MS-FL Framework
### Clients' Movements and Handover
where \(\rm n_{j}^{(s_{2})}\) is the number of training samples at client j when it was served by server s, and \(\rm v^{(s)}(t)\) is the set of all vehicles that completed training under server s in round t. Intuitively, \(\rm\xi^{s}\) gives proportional weight to servers that host more data. If one sets \(\rm\xi^{s}=1\) for every server \(s\), then Equation (13) reduces to the standard FedAvg aggregation. Conversely, if \(\rm\xi^{s}=~{}0\) for some servers s, then none of \(s\)'s vehicles' updates are included in s\({}_{2}\)'s aggregation. In practice, a \"crowded\" server--i.e., one with more total training samples-- receives a larger \(\rm\xi^{s}\), thereby granting its updates greater influence on s\({}_{2}\)'s new model. By combining updates from vehicles that trained fully under s\({}_{2}\) and those that handover from any other \(\rm s_{1}~{}\
eq~{}s_{2}\), Equation (13) ensures that no completed local update is lost when a vehicle moves between coverage areas. This application layer handover is independent of the link and physical layer handover, which are managed automatically by standardized cellular procedures. Once verified, the vehicle resumes participation by uploading its model updates or receiving further training tasks from the new server. By integrating this handover mechanism, our framework minimizes update loss and maintains model integrity, even in highly dynamic vehicular environments.
# An Adaptive Biomedical Data Managing Scheme Based on the Blockchain Technique
## 3 Background
### Discrete Wavelet Transform  
As is mentioned in the introduction, one of the essential objectives is to unify various patients' data (text records with images) into a single scheme; during this, it is important to efficiently use the available resources and maintain the security. Biomedical images can be very large, making it difficult to fit them inside the scheme structure. To resolve this problem, biomedical image segmentation, involving discrete wavelet transform (DWT), was done. DWT was used to reduce the image distortion that might have occurred during the segmentation process. This procedure converts bigger images into smaller, more manageable portions [29].  
It is important to develop input images that function with the generated data chains. The DWT can be defined as a mathematical model to perform multi-resolution analysis [30]. A common method to perform spatial frequency analysis is the wavelet transform. Equation (1) [31] presents the 1D continuous wavelet transform (CWT).  
\[CW(a,b)=\frac{1}{\sqrt{a}}\int y(t)\;\varphi\left(\frac{t-b}{a}\right), \tag{1}\]  
where \(y(t)\) represents the input signal and \(\varphi\) defines the wavelet function; \(a\) denotes the scaling parameter and \(b\) signifies the translation parameter. The base functions pertaining to a DWT were captured with the help of sampling from CWT. Equation 2 demonstrates the \(\varphi_{j,\;k}(t)\) function.  
\[\varphi_{j,\;k}(t)=2^{-\frac{n}{2}}\varphi(2^{-n}t-m). \tag{2}\]  
1D wavelet transform was employed to derive 2D DWT by applying filters in columns and rows. The images were segmented by 2D CWT into four diverse sub-bands i.e., LH, LL, HH, and HL. Every sub-band signifies an image, with estimated horizontal, vertical, and diagonal details, correspondingly.
# A Novel Method for Constructing the S-Box Based on Spatiotemporal Chaotic Dynamics
## 5 Performance Analysis of the Constructed S-Box
### Output Bits Independence Criterion  
The work in [5] also proposed the BIC. If an S-box satisfies the BIC property, all the avalanche variables should be pair-wise independent for a certain series of avalanche vectors produced by complementing a single plaintext bit.  
For an S-box satisfying the BIC property, its \(g_{r}\oplus g_{w}(r\
eq w,1\leq r,w\leq n)\) should fulfill the nonlinearity and the SAC, where \(g_{r}\) denotes the Boolean function of the S-box. For the obtained S-box, we calculate the nonlinearity and the SAC of its \(g_{r}\oplus g_{w}\), respectively. The obtained results are listed in Tables 6 and 7. The mean values of BIC-nonlinearity and BIC-SAC of our S-box were respectively 104.64 and 0.5075. This indicates that the obtained S-box fulfilled the BIC performance. Moreover, Table 8 analyzes the BIC property for different S-boxes. It can be noted that the mean value of BIC-SAC of the obtained S-box was consistent with that of other S-boxes, and the mean value of BIC-nonlinearity of the obtained S-box was higher than that of other S-boxes. Therefore, the obtained S-box had better BIC performance than other S-boxes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
0.4844 & 0.5313 & 0.4844 & 0.4688 & 0.4844 & 0.5000 & 0.5938 & 0.4844 \\
0.4219 & 0.4688 & 0.5313 & 0.5000 & 0.4688 & 0.4531 & 0.4531 & 0.4531 \\
0.5469 & 0.5469 & 0.4531 & 0.4688 & 0.5156 & 0.5469 & 0.5156 & 0.4688 \\
0.4844 & 0.4375 & 0.5000 & 0.4375 & 0.4688 & 0.5625 & 0.6406 & 0.4375 \\
0.5000 & 0.5156 & 0.4688 & 0.5000 & 0.5313 & 0.5000 & 0.5313 & 0.4688 \\
0.5000 & 0.4688 & 0.5000 & 0.5000 & 0.5313 & 0.4531 & 0.4688 & 0.4688 \\
0.5000 & 0.5313 & 0.5469 & 0.5156 & 0.4688 & 0.5469 & 0.4844 & 0.5781 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The obtained dependence matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S-Boxes** & **Max** & **Min** & **Mean** \\ \hline The obtained S-box & 0.6406 & 0.4219 & 0.4980 \\ S-box based on CML & 0.6250 & 0.3750 & 0.5015 \\ S-box proposed in [11] & 0.5938 & 0.3750 & 0.5059 \\ S-box proposed in [13] & 0.5703 & 0.3984 & 0.4966 \\ S-box proposed in [14] & 0.6094 & 0.4219 & 0.5000 \\ S-box proposed in [16] & 0.5938 & 0.4219 & 0.5049 \\ S-box proposed in [17] & 0.5938 & 0.3906 & 0.5056 \\ S-box proposed in [18] & 0.5938 & 0.4063 & 0.5012 \\ S-box proposed in [19] & 0.5937 &
Sensors_**2008**, \(8\), 1797-1818  
_Sensors_  
**ISSN 1424-8220**  
(c 2008 by MDPI  
www.mdpi.org/sensors  
_Full Research Paper_  
**Localization Algorithm Based on a Spring Model (LASM) for Large Scale Wireless Sensor Networks**  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON][PERSON] 1,3, [PERSON] 1,Yumei [PERSON] 2,**  
**[PERSON] 1,2 and [PERSON] 1,2**  
1 Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, P. R. China;  
E-mail: [EMAIL_ADDRESS]  
2 Department of Automation, University of Science and Technology of China, Hefei, 230027, P.R. China  
3 Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tian, Hong Kong; E-mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS].  
_Received: 6 December 2007 / Accepted: 12 March 2008 / Published: 15 March 2008_  
**Abstract:** A navigation method for a lunar rover based on large scale wireless sensor networks is proposed. To obtain high navigation accuracy and large exploration area, high node localization accuracy and large network scale are required. However, the computational and communication complexity and time consumption are greatly increased with the increase of the network scales. A localization algorithm based on a spring model (LASM) method is proposed to reduce the computational complexity, while maintaining the localization accuracy in large scale sensor networks. The algorithm simulates the dynamics of physical spring system to estimate the positions of nodes. The sensor nodes are set as particles with masses and connected with neighbor nodes by virtual springs. The virtual springs will force the particles move to the original positions, the node positions correspondingly, from the randomly set positions. Therefore, a blind node position can be determined from the LASM algorithm by calculating the related forces with the neighbor nodes. The computational and communication complexity are O(1) for each node, since the number of the neighbor nodes does not increase proportionally with the network scale size. Three patches are proposed to avoid local optimization, kick out bad nodes and deal with node variation. Simulation results show that the computational and communication complexity are almost constant despite of the increase of the network scale size. The time consumption has also been proven to remain almost constant since the calculation steps are almost unrelated with the network scale size.  
Keywords:Localization algorithm, spring model, large scale wireless sensor networks, robot navigation +
Footnote †: journal: Journal of Computer Science
## 5 Implementation and Evaluation
### Complexity Analysis for the Lifetime of WSNs in CH_Rotations and WC + DT Approaches  
#### 5.2.1 Simulation Environment  
Contiki operating system with Cooja simulator were used to simulate WSN architecture [71]. The modified LEACH protocol was used as a clustering management subsection to manage and control the WSN node's hardware and software. The simulation was run on a machine with a 1.8 GHz Intel Core i5 processor, 6 MB cache, and 8 GB RAM. The default parameters used in the architecture of the wireless network are plotted in Table 9, and parameter values in the table are taken from the values in [72].  
In the simulation, the WSN nodes were initially spread across dimensions of 500 \(\times\) 500 terrain associated with nine CHs at initial values distributed in different subregions within the AP coverage region. The initial energy of the WSN node and the number of used WSN nodes were chosen based on the proposed simulated need, which we will discuss later in detail. Moreover, each ordinary node sent 64 packets per second to its CH node and each packet size was 1000 bits. If the node was CH, the received packets were forwarded to AP.  
Figure 5: A sample of the WC+DT output model for the WSN-DS dataset.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline WSN node size & 60 m \(\times\) 120 m \\ ER location & X = 30, Y = 90 \\ Number of CHs & Changeable \\ Number of WSN nodes & 100 \\ Simulation time & 500 \\ Message size & 6400 bits \\ Control message size & 200 bits \\ Initial energy (Joule) & 1 \\ Two-ray ground propagation models & 0.0013 P/bit/m4 \\ Free space model & 10 P/bit/m2 \\ Power consumed by transmitter & 50 nJ/bit \\ Transition power & 20 nJ/bit \\ Power consumed by receiver & 50 nJ/bit \\ Distance threshold & 87 m \\ \hline \end{tabular}
\end{table}
Table 9: Simulation parameters used.