esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study investigated the effectiveness offered by an mmWave-mMIMO-NOMA-SWIPT system armed with a sub-connected architecture as well as in multi-user situations. The MKM user grouping technique has been used by the authors in this instance, followed by the hybrid precoder design. The optimization of both the power allocation and the power splitting has been carried out, and the convergence of the proposed system hasbeen demonstrated. This paper presents and illustrates the SE and EE, as well as the computational complexity and performance of the SSOR-CRZF-based hybrid precoder. Comparing the proposed SSOR-CRZF to traditional precoders, for instance, ZF, MRT, RZF, Kalman, and SSOR, a significant performance boost is achieved along with an immense reduction in complexity. As part of future works, the authors would like to explore the proposed SSOR-CRZF hybrid precoding scheme in terms of optimization of the resource allocation, and the authors would also like to design a more sophisticated hybrid precoding design algorithm to further improve the performance.  
Conceptualization, S.N.S., H.Q.T., and D.K.; methodology, S.N.S. and S.N.; software, S.N.S.; validation, S.N.S., A.L.I., and D.K.; formal analysis, S.N.S. and A.L.I.; investigation, S.N.S.; resources, S.N.S. and A.L.I.; data curation, S.N.S. and H.Q.T.; writing--original draft preparation, S.N.S.; writing--review and editing, S.N.S., D.K., H.Q.T., and S.N.; visualization, S.N.S.; supervision, D.K. and S.N.; project administration, S.N.S. and A.L.I.; funding acquisition, S.N.S. and A.L.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Lightweight Multi-Source Fast Android Malware Detection Model
## 3 A Lightweight Multi-Source Fast Android Malware Detection Model
### Ensemble Model and Base Models  
Based on the above, we propose a model to detect Android malware by Dex file header information, power spectrum density information of dex file structure entropy, and permission and intent information in the AndroidManifest.xml file. The structure of the model is shown in Figure 3, this model is divided into four base models and one ensemble learning model.  
Figure 3: The structure of the lightweight multi-source fast Android malware detection model. This model takes the classes.dex and AndroidManifest.xml files in the APK sample as input, followed by feature selection and feature extraction of these two files. The extracted features are predicted by 4 different base models, these base models are integrated by adaptive soft voting method, and finally the probability of the sample being malicious is output.  
Figure 2: The structure of a Dalvik executable.  
Specifically, we used the following base models.
* Base model 1 : Abbreviated as \(MLP(H)\). It extract and parse the header information of the classes.dex file, encode it as header features, and use the multilayer perceptron for prediction;
* Base model 2: Abbreviated as \(MLP(P)\). It calculate the entropy of the classes.dex file, extract the power spectral density of the entropy signal as features by the maximum entropy method, and use the multilayer perceptron for prediction as well;
* Base model 3: Abbreviated as \(ASCNN(I)\). perform the prediction on the AndroidManifest.xml file is decoded and parsed. Permission and intent keywords are extracted and encoded as features by bag-of-words model. Since the dimensionality is too high, we use adaptive shrinkage convolutional neural network for dimensionality reduction and then prediction by multilayer perceptron;
* Base model 4: Abbreviated as \(ASCNN(C)\). Since theoretically using more base models for ensemble learning will give better results. To improve the ensemble learning, we additionally added a base model that concatenate the features extracted from the DEX file header and the permission and intent features and uses an adaptive shrinkage convolutional neural network and a multilayer perceptron for prediction.  
Finally, we use the adaptive soft voting method to perform ensemble learning and predict the final results.
## 1 Introduction
Building on known equations and employing regression techniques, this method links ship speed and resistance by incorporating the geometry of the ship, channel depth, and the relationship between ship speed and resistance. [PERSON] et al. [10] utilized LVM-W and modified ITTC-78 methods in still water and conventional wave resistance tests to predict resistance variations in container ships. [PERSON] et al. [11] experimented with a physical model of a 1000-t dry bulk cargo ship in a restricted Class III channel. By applying the Zwankov formula and refining its residual resistance coefficient, they formulated an accurate resistance calculation formula tailored for these challenging navigation conditions. [PERSON] [12] segmented resistance into water friction, water pressure, and surface slope resistance for computation. He refined the Zwankov formula's resistance component using real ship test data, thereby developing a method for calculating navigational resistance. [PERSON] [13] developed a physical model to calculate ship resistance, taking into account factors such as speed, length, and draft for a 1000-t ship navigating through a channel. This model, based on the Froude flow resistance classification method, yielded promising results. A literature analysis reveals that while numerical simulation methods can flexibly modify channel width and depth, various ship parameters, navigation speed, and offer significant material and equipment cost savings, they suffer from substantial calculation accuracy errors and lengthy cycles when dealing with very small navigation tunnel section coefficients due to complex coupling effects. The physical model method, while highly practical, excels at obtaining system data and parameters through experimental measurements, enabling a more precise depiction of system dynamics and offering intuitive, verifiable results. However, the simulation model is complex, costly, takes a long time, and is challenging to modify and restructure. In most existing studies, navigation tunnels serve primarily as intermediate channels in non-flow environments, typically employing self-propelled navigation modes with relatively large section sizes. The current literature lacks sufficient research to support the study of navigation resistance characteristics in ship-towing navigation tunnels under flow conditions. Consequently, this paper targets the top-towing navigation mode in tunnels, conducting simulation experiments on ship towing navigation in very small section sizes under both non-flow and flow conditions. This further analysis of ship towing navigation resistance characteristics provides vital support for determining scale and navigation standards in towing navigation tunnels facilitating upstream and downstream connectivity in mountainous regions with sharp bends, and aids in the research and development of towing equipment.
## 2 Theoretical Analyses
### Impedance Model of the Hollow Perforated Spherical Structure with Extended Tubes (HPSET)  
Figure 1 illustrates the placement of the HPSET and melamine foam in an impedance tube. According to the well-known Maa model [28; 29], the acoustic impedance of a single extended tube can be expressed as  
\[Z=\frac{\Delta P}{\mathrm{i}t}=j\omega p_{0}(t+0.85d_{0})\left[1-\frac{2}{x \sqrt{-j}}\frac{J_{1}(x\sqrt{-j})}{J_{0}(x\sqrt{-j})}\right]^{-1}+\frac{\sqrt {2\omega p_{0}\eta}}{4} \tag{6}\]  
where \(x=d_{0}\sqrt{\omega p_{0}/(4\eta)}\) is the ratio between the perforation radius and the viscous boundary layer thickness inside the tube of the perforations (also named \"perforation constant\"), \(d_{0}\) is the inner diameter of the extended tubes, \(t\) is the maximum length of the tubes, \(\eta\) is the viscosity of the air, \(\omega\) denotes the angular frequency, \(j=\sqrt{-1}\) represents the imaginary unit, \(\rho_{0}\) is the mass density of the air, and \(J_{0},J_{1}\) are Bessel's functions of zero and first order.  
The normalized impedance of the spherical resonating cavity is \(Z_{D}=-j\cot\left(\frac{\omega}{c}\frac{(V-V_{\mathrm{tubes}})}{S}\right)\), then the normalized impedance of HPSET absorber is given as  
\[Z_{HPSET}=\frac{Z}{\varphi_{p}\rho_{0}c}+Z_{D}=r_{p}+j\omega m_{p}-j\cot\left( \frac{\omega}{c}\frac{(V-V_{\mathrm{tubes}})}{S}\right) \tag{7}\]  
with  
\[r_{p}=\frac{32\eta t}{\varphi_{p}\rho_{0}cd_{0}}\left[\left(1+\frac{x^{2}}{32 }\right)^{1/2}+\frac{\sqrt{2}xd_{0}}{64t}\right) \tag{8}\]  
\[\omega m_{p}=\frac{\omega t}{\varphi_{p}c}\left(1+\left(9+\frac{x^{2}}{2} \right)^{-1/2}+0.85\frac{d_{0}}{t}\right) \tag{9}\]  
where \(\rho_{0}c\) denotes the characteristic impedance of the air, \(c\) is the sound speed in the air (m/s), \(\phi_{p}=NA_{0}/S\) corresponds to the perforation ratio of the HPSET (\(N\) denotes the number of extended tubes, \(A_{0}=\pi{d_{0}}^{2}/4\) denotes to the inner cross-sectional area of the extended tubes, \(S=\pi R^{2}\) is the cross-sectional area of the HPSET. \(d_{0}\) is the inner diameter of the extension tubes and \(R\) is the inner radius of the HPSET). \(V=4\pi R^{3}/3\) and \(V_{tubes}=\pi t{N}{d_{0}}^{2}/4\) are the volume of the perforated ball and the extended tubes, respectively.
## 6 Training and Validation
### Training and Validation Metrics
#### 6.2.2 Train-Validation-Test Results  
In the train-validation-test method, the dataset was split into three sets. Sixty percent of the dataset (i.e., 9689 images) was allocated to the training set, while validation and test sets were granted twenty percent (i.e., 3229 images). The training process was done on one NVIDIA GEFORCE RTX 3090, and 15,000 iterations were completed in 7 h and 50 min.  
The best-performing model was selected using the validation set during the training process. It was then validated on the test set. Table 8 shows metric results of AP\({}_{50}\), AP\({}_{75}\), and mAP on the test set.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Network Hyperparameters**} \\ \hline Input size & 480 \(\times\) 480 \\ Optimizer & SGD \\ Learning rate & 0.001 \\ Momentum & 0.949 \\ Weight decay & 0.0005 \\ Batch & 64 \\ Subdivisions & 16 \\ Data augmentation & Saturation, exposure, hue, flip horizontally, mosaic \\ \hline \hline \end{tabular}
\end{table}
Table 6: Network Hyperparameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **AP\({}_{50}\)** & **AP\({}_{75}\)** & **mAP** \\ \hline Node-OK & 0.997 & 0.980 & 0.820 \\ Missed-Weld & 0.997 & 0.970 & 0.794 \\ Node-Blocked & 0.986 & 0.960 & 0.773 \\ Spacer & 0.997 & 0.977 & 0.807 \\ Seat & 0.998 & 0.972 & 0.809 \\ Endnode & 0.996 & 0.843 & 0.692 \\
**Average** & **0.995** & **0.950** & **0.783** \\ \hline \hline \end{tabular}
\end{table}
Table 7: K-fold cross-validation metrics.  
The overall precision, recall, and F1-score were 0.92, 0.99, and 0.95, respectively, when the IoU threshold was set to 50%, and the confidence score threshold was considered 0.25.
## 1 Introduction
Thus, site-specific ground measurements, especially EC-based measurements, are always required to validate these models and obtain the best satellite-based GPP estimation models in diverse ecosystems. However, Re is rarely quantified from satellite-based products because its soil contribution (Rs) is arduous to observe by remote satellite [41; 42]. Recently, using the coupled relationships between Ra and GPP [43], and between Rm and temperature [44], satellite-based models can give us comparable estimations to Re_EC [42]. Nevertheless, another challenge of current EC-based measurements is that they still cannot directly provide partitioning of Ra and Rm components of Re. Thus, no evidence is provided for separately validating the accuracy of Ra and Rm estimations in this alpine meadow. Chamber-based Re components measurements using gradual plant exclusion can provided complete Re estimations, including Re, Rs, Ra and Rm for site-specific ecosystem [45; 46; 47; 48]. Although recent studies have found chamber-based CO\({}_{2}\) measurement generally overestimates or underestimates EC-based measurements [49; 50; 51], for many ecosystems, the ratios between Re components are similar as measured at specific sites [11; 51]. Therefore, in this study, a combination of three methods, EC-based measurements, and satellite- and chamber-based ecosystem carbon flux measurements, were developed in a typical alpine meadow on the central Tibetan Plateau during 2009 to 2011 (Figure 1). Our study objectives were: (1) to select the most instrumental satellite-based GPP estimation methods through multi-model comparison with the EC-based GPP estimations; (2) to introduce a solely satellite-based Re estimation and partition method for seasonal Ra (or Rm) contributions of Re (Ra/Re), and validate its accuracy based on the tower- and chamber-based Re components measurements; and (3) to test what controls the seasonal variation of Ra/Re, and whether seasonal response of Ra and Rm to temperature change is identical in this alpine meadow.
# A Multi-Hop Clustering Mechanism for Scalable IoT Networks
## 1 Introduction
The proposed clustering mechanism, hence, tries to minimize the number of required Internet connections while avoiding the excessive delay in the wireless network. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators and to map a partition of IoT nodes onto the selected set of coordinators under a given constraint of maximum hop count between the IoT nodes and their respective coordinators. This problem can be mapped into a set cover problem which is known as NP-hard [6] and we, thus, pursue heuristics. The proposed mechanism is particularly useful for the IoT network deployed in a very large area, like a city, where a large volume of the smart nodes are monitored or maintained outdoors, such as street lights, traffic lights, noise/pollution/traffic monitoring devices, etc. [7]. Since the smart nodes are sparsely deployed outdoor, they may be out of range of the coverage of Wi-Fi access points located in the city. Therefore, the nodes require a cellular connection for accessing their common server managed by the city. Under such a scenario, using a separate cellular link from each node to the server would result in a high price of using the cellular network as well as a very large amount of overhead for the server. Therefore, it is essential to reduce a count of the selected smart nodes that opens a cellular connection to the server by forming multi-hop clusters in which low-power short-distance radio communication, such as Bluetooth and Zigbee, is used. This paper is organized as follows: Section 2 summarizes the clustering approaches for wireless sensor networks and explains their inappropriate aspects for IoT environments. In Section 3, the proposed mechanism is explained and its complexity is analyzed. The performance of the proposed mechanism is discussed in Section 4 in two experimental settings. We finally conclude in Section 5.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques
### SI Cancellation Techniques
#### 2.2.2 Digital SI Cancellation Techniques  
In a wireless communication system, accurate estimation of desired signal strength is critical [67]. Estimating the desired signal is defined as the process of characterizing the effects of the physical channel, i.e., the transceiver's components and the wireless channel between Tx and Rx on the desired input sequence [68]. Perfect suppression of SI is impossible in practice; even after applying antenna and analog cancellation techniques, residual SI still exists. The statistical properties of SI are different from those of thermal noise, but it still acts in a similar way to noise and is also treated as noise. Digital cancellation techniques are applied at the baseband side of the transceiver and are used to suppress residual SI, i.e., low power multi-path components of SI [6; 24; 25]. They are also applied to compensate for non-linear components of the Tx and Rx front end [30]. The performance of the FD transceiver is highly degraded due to non-idealities in different stages of transceiver's architecture, i.e., Tx and Rx front-end components (low cost, low-quality components) and due to the circuitry used for active and passive cancellation.  
In this research study, after applying linearization techniques, digital SI cancellation is performed, to mitigate residual SI. Linearization techniques are used to strengthen the residual SI cancellation. Non-linear transmitter and receiver chain components degrade the transceiver's performance and affect the cancellation of residual SI or make residual SI mitigation difficult. In this section, the technique for residual SI cancellation in the digital domain will be addressed. The method used for residual SI cancellation in the digital domain is the Kalman filter. In our previous work [69], we investigated the performance and digital SI cancellation using the extended Kalman filter (EKF) and achieved SINR improvement of about 90 dBm.
## 2 Materials and Methods
### Characterization and Electrical Measurements  
The output voltages, currents, and charges of the self-powered triboelectric respiratory sensors were measured by Keithley 6514. Data were recorded using self-programmed software written in LabView. The periodic respiratory process was simulated by a linear motor (Linnot E1100, LinMot, Inc., Elkhorn, WI, USA), and the constant pressure value was monitored by a Vernier double-range force sensor (Vernier Science Education, Beaverton, OR, USA).
## 3 Experimental Set-Up
### Final Experimental Set-Up
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Sensor/Signal Label** & **Sensor Type** & **Range** \\  & & & **Accuracy** \\ \hline Flow rate & TS-F-01 & & 0 to 500 g/s \\  & TS-F-02 & & 1.2\% \(\times\) flow rate (g/s) \\ \hline Absolute pressure before & TS-P-F11 & Membrane pressure sensor & 0 to 10 MPa \\ the flowmeters & TS-P-F12 & (Siemens SITrans P DSIII) & 0.16\% \(\times\) 10 MPa \\ \hline Differential pressure over & TS-P-F01 & Membrane differential pressure sensor & 0 to 0.5 MPa \\ the flowmeters & TS-P-F02 & (Siemens SITrans P DSIII) & 0.065\% \(\times\) 0.5 MPa \\ \hline Temperature before flowmeter & TS-T-F01 & & 300 to 1000 \({}^{\circ}\)C \\  & TS-T-F02 & & 0.004 \(\times\) temperature (in \({}^{\circ}\)C) \\ \hline  & TS-T-013 & & \\  & TS-T-018 & & \\  & TS-T-002 & & \\ Inlet and outlet temperature on & TS-T-003 & & 300 to 1000 \({}^{\circ}\)C \\ channels & TS-T-004 & & 0.004 \(\times\) temperature (in \({}^{\circ}\)C) \\  & TS-T-007 & & \\  & TS-T-008 & & \\  & TS-T-009 & & \\ \hline  & TS-P-001 & & \\  & TS-P-002 & & \\ Differential pressure over & TS-P-P003 & Membrane differential pressure sensor & 0 to 0.5 MPa \\ the channels & TS-P-004 & (Siemens SITrans P DSIII) & 0.065\% \(\times\) 0.5 MPa \\  & TS-P-005 & & \\  & TS-P-006 & & \\ \hline  & TS-P-011 & & \\ Absolute pressure at the outlet of & TS-P-013 & Membrane pressure sensor & 0 to 10 MPa \\ the channels & TS-P-014 & DMP 320 from BD Sensors GmbH & 0.16\% \(\times\) 10 MPa \\  & TS-P-015 & & \\  & TS-P-016 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of helium parameters monitored for the FWMU. on an area of 580 \(\times\) 160 mm on top of channels 3, 4, 7, and 8, four water-cooled plates create a mask on top of the mock-up. In addition, an infrared camera (IR camera) recorded the temperature of the heated surface. The IR camera was installed outside the vacuum chamber, and the FWMU image was transmitted toward the camera lens using two mirrors.
## 3 Methodology
### PSInSAR Full Stack Processing
#### 3.2.4 Deformation and Elevation Model Construction  
To ensure our reference point shows minimal displacement, we included an estimation of the APS to correct for any atmospheric delays that might affect the accuracy of our estimates. The deformation and elevation models are developed through an iterative process aimed at minimizing the discrepancies between observed and modeled phase values. This iterative refinement is aimed at minimizing the sum of the squared discrepancies between the observed phase values and those predicted by the model.  
To ensure the accuracy of these models, we conducted extensive parameter testing. We found that the range of the linear velocity to \(\pm\)50 mm and allowing for residual topographic error estimation of \(\pm\)200 m provided the best balance. These parameters were particularly effective in our study area, accommodating the complex dynamics introduced by high-rise buildings and other structural variabilities. This setting maintains high temporal coherence in APS estimation, reinforcing the reliability of our subsequent analyses and findings.  
As we already estimated the AS-Index and selected the PS point as a reference point with AS-Index higher than 0.85 in all the sub-stacks, now we also confirm that this reference point shows minimal displacement and velocity variations. This point also features minimal differences in height and residual height values relative to other PS points, ensuring it serves as a robust datum for height estimations across the dataset. After evaluation, we used this PS point as our reference point and estimated the height of other PS points relative to it.
###### Abstract  
Digital images are transferred with ease through the network. Many users are using the images without the knowledge of the owners. Therefore, a novel watermarking scheme is proposed to ensure copyright protection and authentication of images using cryptography techniques. Here, a quick response (QR) image is generated for a watermark image that contains public and private keys prepared using a cryptosystem. Later, this QR image is scrambled using a chaotic logistic map. The public and private keys are used to cipher and decipher the data. Next, the scrambled QR watermark is embedded into a color image using a single-level discrete wavelet transform followed by singular value decomposition using the key value. Finally, the inverse process is applied to extract the watermark. The proposed method is validated using various image processing attacks. The results are then compared with state-of-the-art watermarking schemes. The experimental results show that the scheme provides good results in terms of robustness and imperceptibility.  
digital watermarking: invisible watermark; QR code; RSA; singular value decomposition; discrete wavelet transform Article
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 3 Method  
There are three criteria that should be satisfied by any edge detector: Low error rate, good localization, and a single response to the edge [12]. Edge detections by different algorithms including Canny, LoG (Laplacian of Gaussian), Sobel, Prewitt, and Roberts have been compared, and the results obtained from the Canny algorithm were found to be most suitable under almost all scenarios [17; 18]. Algorithms such as LoG, Sobel, Prewitt, and Roberts are sensitive to the noise. The increased noise in the images could eventually degrade the magnitude of the edges and the associated accuracy of its delineation. The Canny algorithm, on the other hand, improves the signal of edge points with respect to noise ratio by using non-maximum suppression, and provides better edge detection with the help of the threshold method [17; 18]. The algorithm introduced in this study is an improvement to the Canny edge detector [13]. The three steps of the improved algorithm are: image preprocessing, automated edge detection method (with a modified element), and post-processing.