esa-sceva/satcom-chunk-collection huggingface dataset:
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 3 Materials and Methods
### Clinically Aware Data Augmentation  
Data augmentation strategies were carefully designed to increase dataset variability while preserving thoracic anatomical structures critical for accurate cardiomegaly assessment. Horizontal flipping was applied with a 50% probability, maintaining diagnostic validity due to the inherent left-right symmetry in frontal chest radiographs.  
Rotation augmentation was constrained within a narrow range of \(\pm\)5 degrees to avoid distorting essential structures such as the heart silhouette and mediastinum. Zoom augmentation was limited to a range of 95% to 105%, simulating realistic variations in radiographic magnification without disproportionately enlarging the heart or lung regions.  
Translation was restricted to shifts of no more than 5% of the image width or height (approximately \(\pm\)10 to 12 pixels) along both x- and y-axes, modeling minor variations in patient positioning during imaging without compromising anatomical integrity. Additionally, selective combinations of flipping and translation were employed to further enhance spatial diversity while maintaining clinical interpretability.  
These carefully constrained preprocessing and augmentation techniques ensured the preservation of diagnostically relevant features, ultimately supporting improved model training, generalization, and performance.
## 3 Results and Discussion
### Calculation of Reflectivity Modulation
Results for (i) a thermal distribution with electron temperature change \(\Delta T_{e}=0.16\) K (red line labelled thermal) and (ii) a rigid shift of the Fermi sphere (blue line labelled drift) with the same energy content as (i) are shown.
## 5 BDS Data Description
### Data Collection  
Figure 8 shows the BDS signal collected device, which consists of an antenna, a u-blox F9 GNSS receiver, a laptop, a fisheye camera, and a compass. The collection process consists of two main steps, i.e., (1) the taking of sky pictures by a fisheye camera, and (2) the collection of data from the BeilDou satellite. In detail, we first used a compass to determine the north, adjust the position of the fisheye camera to align with the north, and take an image of the sky view. Then, the antenna, the high-accuracy positioning module, and the laptop computer were connected according to Figure 8, and the computer program was adjusted to collect the raw data of the BeilDou satellite signal in RINEX format. Herein, to collect more accurate positioning data, we used the double differential approach. So we used two antennas, one as a reference station and the other as a mobile station. Finally, the collected.dat file is solved using RTKLIB 2.4.2.  
The detailed solution process is as follows. First, the u-center and serial assistant are debugged to obtain the raw.dat format file, which is stored on the computer. Second, the data are decoded using the rtkconv module in RTKLIB to obtain the GNSS observations and store them in the common RINEX format. In detail, the file header format, satellite system, etc. are configured through the Options option. When all options are configured, \"Convert\" is clicked to start the conversion of the file format to RINEX. Furthermore, the data are decoded using the rtkconv module in RTKLIB to obtain the GNSS observations and stored in the common RINEX format. Specifically, the file header format, satellite system, etc., are configured through the \"Options\". When all options are configured, \"Convert\" is clicked to start the file format conversion to RINEX. In addition, the \(C/N_{0}\) can be observeddirectly from the raw data. The EA and AZ are calculated after calculating the satellite position and station position, and the PR is calculated based on the satellite position, station position, and atmospheric correction. Finally, the satellite position, EA, AZ, and PR can be calculated by running \"Options\" in the rtkpost module of RTKLIB to select the input file, configure the observation parameters such as positioning mode and frequency type, and click the \"Convert\" button to start the process of solving the observation data.
## 5 Conclusions  
As the fourth basic circuit component, the memristor is a nonlinear element. If the memristor is introduced into the chaotic circuit, the complexity of the circuit can be strengthened. In this paper, we discretized the ideal flow-controlled memristor model using the Eluer method, obtained the discrete memristor model, and coupled the discrete memristor model with the one-dimensional discrete chaotic system to obtain the two-dimensional discrete memristor coupling model. In chapter 3, three real numerical examples are given. The Lyapunov index, bifurcation diagram and chaotic hyacinth are used to show that the dynamic behavior of the system has been significantly improved. Secondly, the system has high complexity through sample entropy, permutation entropy, correlation dimension, and the Kaplan-Yorke dimension. Finally, the model proposed in this paper is applied to secure communication. Compared with the one-dimensional chaotic system and the existing two-dimensional memristor model, the transmission efficiency of the model proposed in this paper is significantly improved. Therefore, by coupling the model with the discrete memristor, the dynamic characteristics of the low-dimensional chaotic system can be enhanced.  
[PERSON] was in charge of the methodology, software, validation, and writing--original draft preparation. [PERSON] and [PERSON] were in charge of software and validation. [PERSON] and [PERSON] performed the evaluation methods of the security analysis. Project administration and supervision were done by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61471158; Natural Science Foundation of Heilongjiang Province of China, grant number YQ2020F012.  
Not applicable.  
Data Availability Statement: Data sharing not applicable to this article, as no datasets were generated or analyzed during the current study.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Initial Conditions** & **Control Parameters** \\ \hline Logistic & \(x_{0}=0.4\) & \(\mu=3.87\) \\ Sine & \(x_{0}=0.2\) & \(\mu=0.95\) \\ Tent & \(x_{0}=0.3\) & \(\mu=1.47\) \\
2D-MLM & \(x_{0}=0.5\), \(q_{0}=0.5\) & \(\mu=0.1\), \(k=1.88\) \\
2D-MSM & \(x_{0}=0.4\), \(q_{0}=0.7\) & \(\mu=-0.1\), \(k=1.78\) \\
2D-MTM & \(x_{0}=0.3\), \(q_{0}=0.6\) & \(\mu=-0.3\), \(k=1.68\) \\
2D-DMLM & \(x_{0}=0.2\), \(q_{0}=0.3\) & \(\mu=0.2\), \(k=1.99\) \\
2D-DMLM & \(x_{0}=0.4\), \(q_{0}=0.1\) & \(\mu=-0.05\), \(k=1.82\) \\
2D-DMTM & \(x_{0}=0.2\), \(q_{0}=0.6\) & \(\mu=-0.4\), \(k=1.58\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The initial conditions and control parameters of these models.  
Figure 9: BERs of the RM-DCSK using three groups of chaotic sequence: (**a**) different SNR; (**b**) different M.
## 3 HDELC Algorithm
### High-Dimensional Feature Space Reconstruction Process
The features used for decision tree branching are selected by comparing the information gain [28]\(\varphi\) of different features. The information gain \(\varphi\) is defined as follows:  
\[\varphi(M,X)=Ent(M)-Ent(M|X) \tag{1}\]  
\[Ent(M)=-\sum_{k=1}^{K}\frac{C_{k}}{M}\log_{2}\frac{C_{k}}{M} \tag{2}\]  
\[Ent(M|X)=-\sum_{i=1}^{m}\frac{|M_{i}|}{|M|}\sum_{k=1}^{K}\frac{|M_{ik}|}{|M_{i} |}\log_{2}\frac{|M_{ik}|}{|M_{i}|} \tag{3}\]  
where \(\varphi(M,\ X)\) is the information gain of feature \(X\) on the training dataset \(M\), \(Ent(M)\) is the empirical entropy of the dataset \(M\), and \(Ent(M|X)\) is the empirical conditional entropy of feature \(X\) on the dataset \(M\). The sample size of dataset \(M\) is \(|M|\), \(k\) is the number of classification labels, \(|C_{k}|\) is the number of samples belonging to category \(C_{k}\), \(\sum_{k=1}^{K}\left|C_{k}\right|=|M|\). The values of feature \(X\) are \(a_{1},a_{2},\ldots,\ a_{m}\), and according to the values of \(A_{i}\), \(M\) is divided into \(m\) subsets \(M_{1},\ M_{2},\ldots,\ M_{m}\). \(|M_{i}|\) is the number of samples of \(M_{i}\), \(\sum_{i=1}^{m}|M_{i}|=|M|\). The set of samples belonging to \(C_{k}\) in the feature subset \(M_{i}\) is \(M_{ik}\), \(|M_{ik}|\) is the number of samples of \(M_{ik}\). For each node of the decision tree, the information gain of each feature is calculated, and the maximum information gain feature is selected as the basis for splitting the nodes. After branching continuously recursively, all training samples are assigned to the child nodes according to the branching condition. The feature space size corresponding to the feature space matrix under the constraint of the optimal attribute screening factor is the optimal feature selection set size. Finally, since the PCA dimensionality reduction method can filter the noisy data, while retaining the essential features, the number of features corresponding to the optimal feature selection set size is used as the basis for PCA dimensionality reduction to optimize the size of the original feature space, which ensures the rigor of the dimensionality reduction scale as well as ensures that the original information is not lost. The optimized original feature space is used for subsequent research of classifier integration.
## 5 Path Following Control Design
### 3 DOFALOS Guidance Law
#### 5.1.2 Proportional Guidance Law  
The core of guidance law is to calculate the desired heading through the cross-track error and look-ahead distance. Tradition guidance law is given by Equation (25). LOS angle \(\psi_{los}\) will guide the object until \(y_{e}=0\). However, under unknown sea conditions, AUV is subjected to drift forces mainly caused by external disturbance so that nonzero drift angle \(\beta\) arises during path following. In order to handle ocean currents or any other environmental disturbance, a modified guidance law with an extra proportional term according to drift angle is proposed in the paper, as shown in Equation (26):  
\[\psi_{los}=\gamma_{T}+\arctan\left(-\frac{y_{e}}{\Delta}\right), \tag{25}\]  
\[\psi_{los}=\gamma_{T}+\arctan\left(-\frac{1}{\Delta}\left(y_{e}+\chi\right) \right), \tag{26}\]  
where \(\Delta\in(\Delta^{\prime}_{\text{min}},\Delta^{\prime}_{\text{max}})\). \(\chi\) is the extra proportional term with respect to the drift angle. Assume that the current heading tracks the desired heading perfectly, i.e., \(\psi=\psi_{los}\). Equation (26) can be deduced as Equation (27):  
\[\begin{split}\sin\left(\psi-\gamma_{T}\right)&= \sin\left(\arctan\left(-\frac{1}{\Delta}\left(y_{e}+\chi\right)\right) \right)=-\frac{y_{e}+\chi}{\sqrt{\Delta^{2}+\left(y_{e}+\chi\right)^{2}}},\\ \cos\left(\psi-\chi_{T}\right)&=\cos\left(\arctan \left(-\frac{1}{\Delta}\left(y_{e}+\chi\right)\right)\right)=\frac{\Delta}{ \sqrt{\Delta^{2}+\left(y_{e}+\chi\right)^{2}}}.\end{split} \tag{27}\]  
Figure 10: Relationship between look-ahead and 3 DOF.  
Substituting Equation (27) into Equation (19) gets Equation (28):  
\[\dot{y}_{e} =-\frac{U\left(y_{e}+\chi\right)}{\sqrt{\Delta^{2}+\left(y_{e}+\chi \right)^{2}}}+\frac{U\Delta\beta}{\sqrt{\Delta^{2}+\left(y_{e}+\chi\right)^{2}}} \tag{28}\] \[=\frac{U\left(\Delta\beta-y_{e}-\chi\right))}{\sqrt{\Delta^{2}+ \left(y_{e}+\chi\right)^{2}}},\]  
where \(\beta\) is well known according to \(u\), \(v\), i.e., \(\beta=\arctan(v/u)\). Consequently, \(\chi=\Delta\beta\) is derived and is substituted into Equation (28):  
\[\dot{y}_{e}=-\frac{Uy_{e}}{\sqrt{\Delta^{2}+\left(y_{e}+\lambda\right)^{2}}}. \tag{29}\]  
Hence, the proposed 3 DOF adaptive proportional LOS guidance law can be expressed by Equation (30):  
\[\left\{\begin{array}{l}\Delta^{\prime}_{\min/\max}=\Delta_{\min/\max}- \Delta_{\min/\max}\operatorname{sat}\left(\frac{c}{c_{\max}}\right),\\ \Delta=\Delta^{\prime}_{\min}+\left(\Delta^{\prime}_{\max}-\Delta^{\prime}_{ \min}\right)e^{-\frac{\sqrt{2}}{u}},\\ \psi_{d}=\gamma_{T}+\arctan\left(-\frac{y_{e}}{\Delta}-\beta\right).\end{array}\right. \tag{30}\]
## 5 Discussion  
The implementation of the different technologies presented in this study facilitates the simulation of 5G digital twins. The network simulator (GNS3) allows the combination of NFV components and data transmission across a virtual network. The deterministic radio simulator provides a coverage map of the warehouse scenario. This data can be exported as a comma-separated value file (CSV file), generating a 5G radio coverage dataset with cartesian coordinates and power values. The use of dynamic network components is required for sending data. In this regard, Ubuntu-Docker KVM is quite helpful. This component allows extra modules in a given scripting language (Python3). The TCP protocol enables client and server connection by denoting a specific port (21880). The selected transmission mode for this study is simplex. This communication mode is unidirectional between client to server. The transmission of files is individual. Wireshark is the software used to monitor data transmission.  
Data security is a constant challenge in data communication networks. The packet analyzer allows the capture of packages across the network. Capturing data is known as packet sniffing, and it could represent a security risk when dealing with more sensitive data. The AES algorithm has been adopted in this study to hedge this risk.  
This study compares the difference between encrypted and non-encrypted files across the network. RTT time between files is slightly different, ranging from 1.2 to 3 milliseconds. Throughput measures the amount of data transmitted from client to server in a given period. Therefore, when the data package increases, the throughput also tends to increase. In this case, the file size increases an extra 34 percent after encryption. The encrypted file takes an extra 0.005 s to achieve its maximum throughput, which is 90,000 bits/s higher than its counterpart. The extra 0.005 s represent a 25 percent increase in time. In other words, the extra size of the file significantly influences time and throughput.  
Packet capture exposes the vulnerability of non-encrypted files. The raw data can be decoded and easily read by an attacker. In contrast, data encrypted with the AES algorithm can be captured but not easily decoded.
## 4 The Research Results  
The data on bus downtime in monthly intervals were arranged according to the downtime categories (reasons for unavailability) of vehicles. Using Formula (2), the \(AT\) availability factor of each make of vehicles was calculated in the entire observed period of operation (144 months). Using Formula (4), the \(UT_{k}\) categorized unavailability rate was calculated for the following categories: NB, ON, BCZ, NW, WS, and NN.
# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 1 Introduction
The CRF model method [16] can customize the feature function, which express not only the dependence between observations, but also the complex dependence between the current observation and multiple states before and after. This can efficiently overcome the problems faced by HMM model. However, its disadvantage is that the sequence features require to be manually extracted. So, this paper based on CRF, with little input considered visual characteristics, and do some ways to solve our problem. That is different from other research. This paper proposes a gaze points classification method based on CRF and visual characteristics to extract the meaning of gaze points for a more accurate manipulation of servo platform, while the servo platform visually aim the target. Considering the saliency of images and task-related attention in human eye gaze tasks, the visual features of gaze images and gaze points are described, the LSTM model [18] is introduced to merge the two described feature relationships, and the CRF model is used to mark and classify the eye-tracking gaze points. The proposed method aims at classifying the gaze points from the perspective of visual characteristics and improving the accuracy of eye-tracking interaction. In the application of eye-tracking, the potential of eye-tracking interaction is fully utilized, and the isolated eye gaze point is removed. The main contributions of this paper are summarized as follows. 1. A novel hybrid classification model is proposed, introducing visual characteristics about gaze scene image and gaze point;
2. The method achieves automatic and efficient analytical processing of gaze points in eye-tracking interaction. This remainder of this paper is organized as follows. Section 2, describes the gaze point classification method of conditional random field based on visual characteristics. Section 3, presents the experimental process and data. Section 4, shows the experimental results and the comparison with other related methods. Finally, the conclusions and future work are drawn in Section 5.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 3 Integrated Management Framework
### Scheduler Module Technical Implementation
#### 3.2.1 Data Model and Problem Structure  
The scheduling problem follows a hierarchical structure with three planning categories: (1) the planning solution containing the complete problem and solution space, (2) planning entities representing individual tasks to be scheduled, and (3) planning variables defining the assignable values (satellites, targets, and start times). Each task is characterized by resource requirements (memory and energy), task type, duration, and priority weight. The model incorporates tasks including authentication requests, data uploads/downloads, and 3 GPP-specific procedures.
## 3 ICT Sub-Systems of the Smart Grid
### Smart Grid applications  
From an application perspective, there are three major domains for ICT integration into the power grid [4], as shown in Figure 2. Supervisory control and data acquisition (SCADA) is already widely used for remote monitoring and controlling of higher levels of the distribution grid [5]. This area is also well covered by advanced standards, such as IEC 61850 for substation automation. The active distribution grids are the core of modern smart grids and enable the integration of distributed energy sources mainly into the mid-level voltage grid. The primary task of communication in this respect is to collect critical network parameters and to actively influence the distributed energy generation, so as to maintain the overall power quality. On the lower grid levels, smart metering is the most relevant use case for ICT. Originally intended to enable the remote retrieval of meter values, smart metering also includes load profiling, local power-quality monitoring and the remote switching of loads [23]. This requires a permanent connection to a communication network, even though real-time requirements are not as stringent as in SCADA control applications, e.g., the overall load balancing on the grid.
# I-Shaped Metamaterial Using SRR for Multi-Band Wireless Communication
## 1 Introduction
[28] for use at operating frequency of 4.5 GHz. The MeTM's structure, however, was only been verified for single-negative (SNG) properties with negative permittivity. An inverse double-L-shaped structured MeTM was presented by [PERSON] et al. [29]. The MeTMs was etched on Roger RT5880 dielectric substrate material. A triple-band frequency operation was obtained using different array arrangement in C-, X-, and K\({}_{\text{u}}\)- bands. A \"C- shaped\" split circular resonator with dimensions of 12 \(\times\) 12 \(\times\) 1.635 mm\({}^{3}\) was developed on a Roger RT6010 dielectric substrate material with thickness 0.035 mm by [PERSON] et al. [30]. A 30 \(\times\) 30 mm\({}^{2}\) split H-shaped MeTM structure was proposed by [PERSON] et al. [31]. The effective negative permittivity, permeability and refractive index was verified over the frequency band of 2.74, 7.122, 10.855 and 14.337 GHz, respectively. An inverse double C-shaped SSRR MeTM was proposed by [PERSON] et al. [32]. This MeTM had a multi-resonant frequency operating at a frequency range of 2.47-12.60 GHz. In this research work, a new ISMeTM structure is presented. The originality of this research work comes from the proposed shape of the MeTM unit cell structure and the range of the operating frequency obtained, which distinguishes it from the multiband MeTMs in the literature. The I-shape is designed using three SSRRs on the outmost part that are etched onto the FR-4 dielectric substrate material with a thickness of 1.6 mm. The dimensions of the proposed MeTM are 10 \(\times\) 10 \(\times\) 1.6 mm\({}^{3}\). The structure produces the dips in the S21 parameter at 6.31, 7.79, 9.98, 10.82, 11.86, 13.36 and at 15.5 GHz in the C/X/Kuplands. In addition, the ISMeTM cell, the 1 \(\times\) 2, 2 \(\times\) 2 and the 2 \(\times\) 4 array structures with dimensions 10 \(\times\) 20 mm\({}^{2}\), 20 \(\times\) 20 mm\({}^{2}\) and 20 \(\times\) 40 mm\({}^{2}\), respectively, are analyzed to verify the similarities with the established effective parameters. The equivalent circuit for the unit cell is designed using the ADS software. As a result, the proposed ISMeTM cell is suitable for multiband microwave applications.
## 2 Materials and Methods
### Results of the Software Operation
#### 2.7.7 Example of the Software Use  
This subsection presents an example of the software use for the first measurement campaign implemented on the road along a three-kilometre-long route (tram loop) in Gdansk Brzezeno. In order for it to be implemented, the program user must perform the following operations:  
1. Prepare the GeoJSON file in a GIS software, containing the planned route of the inspection vehicle;
2. Acquire the GeoTiFF file covering the campaign area;
3. Acquire the almanae file that is current as of 6 March 2021;
4. Enter the date of campaign commencement: 10:00:00 p.m. UTC+01:00, 6 March 2021;
5. Enter the campaign duration: 02:00:00;
6. Enter the velocity of the inspection vehicle: 30 km/h (standard inspection velocity at which the mobile satellite measurements have been carried out to date);
7. Enter the sample period: 30 sec (this parameter specifies the time resolution at which the calculations will be made by the software, and the possible minimal duration of the inspection vehicle's movement);
8. Enter the min. elevation: 10\({}^{\circ}\);
9. Enter the max DOP: 10 (based on the publication by [49; 50; 51] these are the min. conditions under which GNSS measurements can be carried out);
10. Select all GNSS systems that are tracked by the navigation receiver. The use of all available global navigation satellite systems is crucial for achieving the highest efficiency;
11. Select the DOP: PDOP;
12. Select the Almanac ephemeris data source, and indicate the folder in which the file is found;
13. In the \"Trajectory\" field, indicate the file with the linear object's geometry created;
14. In the \"Digital surface model\" field, indicate the file with the DSM;
15. Click the \"Process\" button.