esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Ventricle Segmentation Results  
Figure 2 and Table 2 display the performance of the nnU-Net and SynthSeg models on the internal and external test sets in terms of the DSC, HD95, surface DSC, and APL. The nnU-Net demonstrated significantly better performance on the internal test set compared to the external test set on all four metrics reported as median [range] (DSC = 0.93 [0.86-0.95] vs. 0.84 [0.69-0.89], \(p<0.001\); HD95 = 0.9 [0.7-2.5] vs. 2.1 [1.6-5.8] mm \(p<0.001\); Surface  
DSC= 0.97 [0.90-0.98] vs. 0.75 [0.63-0.84], \(p\) < 0.001; APL = 876 [407-1298] mm vs. 3653 [2612-5667] mm, \(p\) < 0.001).  
Similarly, the SynthSeg model demonstrated significantly better performance on the internal test set compared to the external test set in terms of the surface DSC and APL (surface DSC = 0.84 [0.70-0.89] vs. 0.73 [0.55-0.78], \(p\) < 0.001); APL = 2809 [2311-3622] mm vs. 4562 [3373-5280] mm, \(p\) < 0.001).  
Within the internal dataset, the nnU-Net outperformed the SynthSeg model significantly on all four metrics (DSC = 0.93 [0.86-0.95] vs. 0.85 [0.67-0.91], \(p\) < 0.001; HD95 = 0.9 [0.7-2.5] mm vs. 2.2 [1.7-4.8] mm, \(p\) < 0.001; Surface DSC = 0.97 [0.90-0.98] vs. 0.84 [0.70-0.89], \(p\) < 0.001; APL = 876 [407-1298] mm vs. 2809 [2311-3622] mm, \(p\) < 0.001). In contrast, no significant differences between metrics were observed when comparing the models on the external test set.  
Figure 2: Comparison of segmentation performance of the nnU-Net and SynthSeg on the internal test set and external test set. Red cross markers represent outliers (>1.5 interquartile range). DSC: Dice Similarity Coefficient; HD95: Hausdorff distance 95 th percentile; APL: added path length.
## 2 Proposed Order Reduction Methodology
### Moment Matching Method for 3-D Transient Field Problems  
Moment matching is a projection-based method. In a moment-matching method, a subspace basis \(V\in\Re^{n\times r}\) is first determined to approximate the residing manifold of the state vector, with the Petrov-Galerkin projection then used to construct the ROM. To preserve the passivity of the high-fidelity model, \(\mathbf{W}=\mathbf{V}\)[14] is selected, yielding a reduced-order model:  
\[\widetilde{\mathbf{B}}\frac{d\widetilde{\mathbf{T}}}{dt}+\widetilde{\mathbf{A}}\widetilde {\mathbf{T}}(t)=\widetilde{\mathbf{P}}\mathbf{J}(t) \tag{3}\]  
where, \(\widetilde{\mathbf{B}}=\mathbf{V}^{T}\mathbf{B}\mathbf{V}\), \(\widetilde{\mathbf{A}}=\mathbf{V}^  
where, \(\hat{\mathbf{P}}(s_{0})=(\mathbf{B}s_{0}+\mathbf{A})^{-1}\mathbf{P}\), \(\hat{\mathbf{A}}(s_{0})=\mathbf{B}s_{0}+\mathbf{A}\). In this paper, \(((\hat{\mathbf{A}}(s_{0}))^{-1}\mathbf{A})^{i}\hat{\mathbf{P}}(s_{0})\) is the \(i\)th order moment vector.  
From [14], one has  
\[\mathbf{H}_{r}(s)-\mathbf{H}(s)=(s-s_{0})^{q} \tag{7}\]  
where, \(\mathbf{H}_{r}(s)\) is the transfer function of the reduced model. It implies that a \(s_{0}\) will guarantee that the reduced-order model achieves a relatively high accuracy of a specified frequency, with a larger \(q\) yielding a more accurate \(\mathbf{H}_{r}(s)\).  
From (7), it is obvious that \(\mathbf{H}_{r}(s)\) depends also on \(s_{0}\). For a fixed \(q\), the error of \(\mathbf{H}_{r}\) at \(s^{\prime}\) is inversely proportional to the distance of \(s^{\prime}\) and \(s_{0}\). In other words, if \(s_{0}\) is put at zero, then \(\mathbf{H}_{r}(s)\) is probably not accurate at high frequencies, which is explained in detail in Figure 1.
# Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions
## 2 Proposed Framework
### Classification  
Based on learning algorithms, Classification classifies the messages of the generated stream. The classification module can aggregate different classification solutions that exist in the literature or are proprietary. The module consumes these solutions as web services that are available and comply with an input and output format. The solutions of the module to be evaluated classify the generated message streams into different categories. The same message stream can be classified by more than one classification solution, which allows comparing the performance of \(n\) solutions with the same message stream generated in the simulation. In this framework, classification solutions that include a text processing component can be evaluated. To train these classifiers, 90% of a set of messages, such as the messages in the SpamAssassin corpus, is used. The main value of this module is not the classification algorithms it uses, but the possibility of integrating existing or developing classification algorithms into the framework through the use of web services and their joint evaluation. This approach allows the execution load of sorting and classification algorithmsto be distributed across different computing nodes. These nodes process the messages within the stream sent to them and then send the sorting results back to the framework.
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 3 Design of a Wireless Monitoring System  
This section describes the design of a wireless monitoring system, including the architecture, network configuration and topology, design of the frames and the ad hoc experimental sensor board. The proposed monitoring system is aimed at monitoring industrial processes by obtaining several measures from diverse sensors distributed in distances in the range of several meters. The presented monitoring system was tested for validation purposes indoors by means of several validation tests carried out at the university facilities. In addition, the system was tested with an aeronautical reconfigurable tooling prototype at industrial premises. The system is aimed at monitoring big machinery.  
The operational requirements for such a monitoring system are as follows:  
* **Sampling frequency:** The monitoring system should be able to monitor real-time sensor data acquired from distributed places at frequencies ranging from 10 to 40 Hz (100-25 ms).
* **Types of sensors:** Different kinds of sensors should be connected: Analog sensors, in the range 0-3.3 V. In the future, digital sensors connected by means of the SPI bus could be also considered.
* **Operating distance:** Since the monitoring system is aimed at monitoring big machinery indoors in industrial environments, it should at least reach distances of up to 10-15 m.  
* **Amount of sensors:** In the range of teens of sensors should be connected to the monitoring system. The number of sensors may change according to the sampling frequency used.
* **WiFi compatible:** WiFi technology is frequently used in industrial scenarios for different, noncritical applications. The monitoring system should be able to avoid or mitigate the loss of packages due to the interferences caused by this technology.
* **Bounded time:** The monitoring system should be able to minimize jitter in order to provide precise measures.
* **Allow both wired and wireless connections:** The monitoring system must be able to allow wired connections, for critical sensors, as well as wireless connections, in order to reduce cabling and measure difficult to reach places.
* **Able to work in industrial environments:** The monitoring system must blend technologies which are commonly accepted in industrial scenarios.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 3 Results
### High Correlations between AOD and \(D_{f}\)  
The AOD had a high correlation with the diffusion fraction (D\({}_{\rm f}\)) (Figure 4; R\({}^{2}\) = 0.57, \(p\) < 0.01), and the influence of AOD on D\({}_{\rm f}\) can be expressed by the distribution of the Michaelis-Menten equation (Equation (5): D\({}_{\rm f}\) = V\({}_{\rm max}\)\(\times\) AOD/(K\({}_{\rm m}\) + AOD), where AOD is set to range from 0.1 to 1). Among them, V\({}_{\rm max}\) represents the threshold of D\({}_{\rm f}\) that could be affected by AOD (Figure 4), that is to say, AOD will not have an impact on D\({}_{\rm f}\) once D\({}_{\rm f}\) exceeds this value (0.6). In addition, K\({}_{\rm m}\) represents the AOD value (0.14) when D\({}_{\rm f}\) is half V\({}_{\rm max}\) (i.e., 0.3) (Figure 4). According to this equation, we can know the increase velocity of diffusivity under different aerosol loading. When the AOD loading was low (from 0 to 0.14), D\({}_{\rm f}\) increases rapidly (from 0 to 0.3), which was a first-order reaction. As the value of AOD increases (from 0.14 to 1), the growth rate of D\({}_{\rm f}\) starts to slow down (from 0.3 to 0.6),  
Figure 3: The spatial distribution of the trends in mean of (**a**) AOD, (**b**) PARdir (W m\({}^{-2}\)), (**c**) PARdiff (W m\({}^{-2}\)), and (**d**) PARtotal (W m\({}^{-2}\)) from 2000 to 2014. The map insets in (**a–d**) show the significance level of the trends of AOD, PARdiff, PARdir, and PARtotal, respectively, with the red area indicating significant trends (\(p\)-value < 0.05) and the black area indicating insignificant trends. The temporal variation of annual AOD, PARdir, PARdiff and PARtotal that were spatially averaged over the globe are illustrated in (**e–h**), respectively; the red lines are the fitting lines.  
which was a second-order reaction. Overall, most of the AOD values were low, indicating a large scatter at low AOD.
## 2 Design Consideration
### Reciever AFE Architecture  
The AFE architecture presented in Figure 9 includes a differential \(R_{\mathrm{m}}\) (100 \(\Omega\)) termination, a two-stage CTLE and TIA, and a feedback resistor (\(R_{\mathrm{f}}\)) and inductance reactance (\(L_{\mathrm{f}}\)). The 100 \(\Omega\) resistor was adopted to match the differential input of the circuit with that of the equipment. The AFE topology along the \(g_{\mathrm{m}}\) TIA with the CTLE compensates for the channel loss at a 15 GHz Nyquist frequency and adjusts the output amplitude without bandwidth deterioration [1; 4; 7]. The transfer function of the AFE circuit is as follows:  
\[\mathrm{A_{\mathrm{e,DC}}}=-\frac{\mathrm{g_{m}R_{D}}}{1+\mathrm{g_{m}R_{s}}} \frac{1-\mathrm{g_{mTIA}}\mathrm{R_{F}}}{1+\mathrm{g_{mTIA}},\mathrm{R_{D}}} \tag{15}\]  
\[\mathrm{A_{\mathrm{e,DC}}}=\mathrm{A_{\mathrm{e,DC}}}\frac{\Big{(}1+\frac{S}{ \omega_{z1}}\Big{)}\Big{(}1+\frac{S}{\omega_{z2}}\Big{)}}{\Big{(}1+\frac{S}{ \omega_{z1}}\Big{)}\Big{(}1+\frac{S}{\omega_{z2}}\Big{)}((\frac{S}{\omega_{z }})^{2}+\frac{2\mathrm{S}}{\omega_{z}}+1)} \tag{16}\]  
where \(\omega_{z1}
## 3 Frequency Equation  
Since the equation in Appendix A has a nontrivial solution, and \(C_{i}\) are generally non-zero, the determinant of the coefficients matrix should be equal to zero.  
\[\text{det}\big{[}a_{ij}\big{]}=0 \tag{14}\]  
Using MATLAB, with its ability to handle symbolic calculations, we obtained the frequency equation (Appendix B) of the free-free beam with tip masses, including their rotary inertias.  
At this point, we took into account several cases to validate the obtained frequency equation:  
Case-1: The free-free beam with two unequal concentrated tip masses. For this case, we obtain \(\alpha_{1}=\alpha_{2}=d_{1}=d_{2}=0\). So the obtained frequency equation is reduced to:  
\[1-\cos\beta\cosh\beta+\beta(\Omega_{1}+\Omega_{2})(\cosh\beta\,\sin\beta-\cos \beta\sinh\beta)+2\Omega_{1}\Omega_{2}\beta^{2}\sin\beta\sinh\beta=0 \tag{15}\]  
which is in precise compliance with that obtained by [PERSON] et al. [29].  
Case-2: The free-free beam with two equal concentrated tip masses. For this case, we assume \(M_{1}=M_{2}=M\), \(\Omega_{1}=\Omega_{2}=\Omega\), \(\alpha_{1}=\alpha_{2}=d_{1}=d_{2}=0\). Thus, the obtained frequency equation is reduced to:  
\[1-\cos\beta\cosh\beta+2\beta\Omega(\cosh\beta\,\sin\beta-\cos\beta\sinh\beta) +2\Omega^{2}\beta^{2}\sin\beta\sinh\beta=0 \tag{16}\]  
which exactly coincides with the equation obtained by [PERSON] and [PERSON] [28].  
Case-3: The free-free beam with one concentrated tip mass. For this case, we obtain \(\Omega_{1}=M_{1}=\alpha_{1}=\alpha_{2}=d_{1}=d_{2}=0\), \(M_{2}\
eq 0\). Thus, the obtained frequency equation is reduced to:  
\[1-\cos\beta\cosh\beta+\beta\Omega_{2}(\cosh\beta\,\sin\beta-\cos\beta\sinh \beta)=0 \tag{17}\]  
Case-4: The free-free beam with no tip masses. For this case, we obtain \(\Omega_{1}=\Omega_{2}=\alpha_{1}=\alpha_{2}=d_{1}=d_{2}=0\), so the obtained frequency equation is turned into the well-known cantilever beam frequency equation:  
\[1-\cos\beta\cosh\beta=0 \tag{18}\]  
The frequency equation obtained in this study is also valid for cases where the position of the gravity center of the end mass is \(\zeta<1\), as seen in Figure 1b. This would facilitate the designer during the positioning of the end mass in the design of a tip-mass-attached structure, such as an energy harvester or dynamic vibration absorber. In cases where the gravity center position of the end mass increases such that \(\zeta>1\), namely when the \(G_{2}\) point is shifted along the \(+\)x direction, the end mass will adversely affect the dynamic behavior of the beam. In this case, the beam may behave as a stepped beam. If the gravity center of the end mass is shifted to be \(\zeta<1\) as seen in Figure 1b, the end mass will not negatively affect the beam dynamics and will also offer convenience to the designer in terms of construction.
## 3 Preliminaries, Models, and Definitions
### Definitions
#### 3.3.2 Network Connectivity  
Network connectivity is another important criterion parameter for evaluating the quality of service in sensor networks. This parameter is a prerequisite for the effective application of sensor  
Figure 1: Underwater 3D space model.  
networks. It is defined as the ratio of the number of nodes connected to the sink node by a one-hop or multi-hop path to the total number of nodes in the network. The number of nodes connected to the sink node can be calculated by breadth-first search, in which the sink node is the root node.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 4 Simulations
### Results
To determine RMSE, we used the results obtained via the impulse response function (A1) as reference values; the results are summarized in Table 3. The results for SAR scene cuts in the range and azimuth directions demonstrate that the extended sinc interpolator provides the most accurate results at the Nyquist rate with an error around 0.7%. Note that linear and cubic interpolators provide good accuracy with deviation from the reference results around 3% and 1.26%, respectively, in range, and around 1.18% and 0.79% in azimuth, respectively. In the case of the nearest neighbor interpolator, the upsampling procedure is required. Figure 5c,d depict reconstructed cuts \(h(\xi_{n},\rho_{n})\) plotted and compared similarly as in Figure 5a,b, but for the twice increased sampling rate, i.e., for \(f_{\rm s}=2f_{\rm max}\). The reconstruction accuracy based on the nearest neighbor interpolation improved significantly by increasing the sampling rate, with the RMSE reduced from 24% to 6.4%; see Table 3. However, the reconstruction result does not agree with the results provided by linear interpolator in the range direction. It contains sidelobe distortions in azimuth SAR-scene cut, as shown in Figure 5c,d. Furthermore, the SAR-scene cuts \(h(0,\rho_{n})\) and \(h(\xi_{n},0)\) that are reconstructed \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interpolation** & **Sampling Rate** & \(f_{\rm max}\) & \(2f_{\rm max}\) \\ \hline Nearest neighbor & \(-7.395\) & \(-13.37\) \\ Linear & \(-10.756\) & \(-12.555\) \\ Cubic & \(-14.372\) & \(-13.586\) \\ Sinc & \(-13.335\) & \(-13.331\) \\ \hline Analytical & & \(-13.265\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Peak-sidelobe Ratios (in dB). Figure 5: Evaluation of interpolation methods based on reconstructed SAR scenes of a point target \(h(\xi_{n},\rho_{n})\) for the sampling rate: (**a,b**) \(f_{\rm s}=f_{\rm max}=0.33\,\)THz; (**c,d**) \(f_{\rm s}=2f_{\rm max}=0.66\,\)THz. with the nearest neighbor and linear approaches still have a lower range and azimuthal resolution, which can be improved with a further increase of the sampling rate. It has also been investigated that by increasing the sampling rate, the SAR-scene cuts obtained with cubic interpolator have the same spatial resolution as the result based on the impulse response function (A1). Furthermore, the reconstructed scene \(h\) based on cubic interpolation deviates from the analytical result around 2.4% in terms of PSLR and less than 0.8% in terms of RMSE; see PSLRs in Table 2 and RMSE in Table 3, respectively. Hence, it can be concluded that the incorporation of cubic and sinc interpolators into the GBP algorithm provides the most accurate reconstruction of SAR scenes, which motivates further investigations with application to real data.
## 3 Development of the Proposed Computational Unit
### Application of the Proposed Computational Unit for Medical Image Compression
\begin{matrix}16&11&10&16&24&40&51&61\\ 12&12&14&19&26&58&60&55\\ 14&13&16&24&40&57&69&56\\ 14&17&22&29&51&87&80&62\\ 18&22&37&56&68&109&103&77\\ 24&35&33&64&81&104&113&92\\ 49&64&78&87&103&121&120&101\\ 72&92&95&98&112&100&103&99\end{matrix} \]  
A quality factor of 50 was utilized for the quantization process. Each matrix element should be multiplied by \((100-\mathrm{q})/50\) for higher qualities; the multiplication factor is \(50/\mathrm{q}\) for lower qualities. The high-frequency components are eliminated without compromising the image quality. Higher numbers are located at the bottom right of the quantization table, whereas data with high-frequency and low-frequency values are located at the top left. This makes the high-frequency components to be rounded off to zero. Once the DCT coefficients are quantized, a zig-zag scanning pattern, as depicted in Figure 15, is followed to maintain the redundancy of zeroes due to the presence of several zeroes at the bottom left corner. The high-frequency components are reduced to zero once quantization is fulfilled, due to which the zig-zag pattern of scanning is pursued. The list of integer values obtained after zig-zag scanning generates the sequence of symbols. Run Length Encoding (RLE) is the next step since there are many zeroes from which each number, along with the number of times it is repeated, is obtained. The quantized coefficients are sampled using the zig-zag scan pattern from the lowest to highest frequencies to generate longer runs. The data stream obtained has a length of 64 bytes, which has a high probability of zeros toward the end. The RLE of the stream obtained is highly efficient. After this, Huffman Coding must be carried out, where the numbers with higher frequency are denoted with lesser bits than the other numbers. RLE and Huffman Coding are performed to reduce the space required without removing any data. The data obtained are then converted back to binary format using the gray-to-binary code converter, after which the compressed image is obtained [38; 39; 40; 41; 42]. The reverse process is applied to obtain the reconstructed image using the decoding process illustrated in Figure 16. The Huffman-coded data are decoded, and the run-length encoded data are expanded into an \(8\times 8\) matrix to restore the matrix of quantized  
Figure 15: Zig-zag scan pattern. coefficients. These are multiplied by the values from the quantization matrix Q (u,v). Next, the Inverse Discrete Cosine Transform (IDCT), which is the reverse of the DCT, is carried out on the values obtained in a similar manner using the computational unit for the computations involved. At this instant, data loss becomes evident as the DCT coefficients that were set to zero due to rounding remain zero after decompression.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Inference Attack Methods  
An inference attack, alternatively referred to as a model extraction or model stealing attack, is an adversarial attack launched during the deployment or production phase. Inference attack is a technique used by attackers to obtain sensitive information about an ML model or its training data. In a black box scenario, the attacker does not possess access to the inner workings of the model and only has access to its input and output interfaces. The attacker may use various techniques to extract information about the model such as query-based attacks, membership inference attacks, and model inversion attacks.  
[PERSON] et al. [69] introduced Knockoff Nets as a model-stealing technique that can extract the features of a completely trained model through a two-step process. In the first step, the attacker collects model-generated predictions through a series of input data queries. Subsequently, the collected data-prediction pairs are utilized to construct a substitute model referred to as a \"knock-off\" model. Likewise, [PERSON] et al. [70] proposed a method that involves creating an adversarial model that faithfully reproduces the architecture and weights of the target oracle model. The method is called the Functionally Equivalent Extraction (FEE) attack and prioritizes accuracy and fidelity objectives for model extraction. [PERSON] et al. [71] introduced the Hop Skip Jump Attack, a decision-based attack that estimates the decision boundary of an ML model. The goal of this attack is to cross the estimated boundary deliberately to cause a misclassification.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 1 Introduction  
Satellite communication systems are an important component of next-generation mobile communication networks. Through the introduction of satellite communication means, the global coverage of mobile communication systems can be enhanced, thereby expanding applications in such areas as automatic flight piloting in remote areas [1; 2; 3], maritime logistics monitoring [4; 5], global environmental resource monitoring [6; 7], and disaster early warning [8; 9].  
Satellite communication systems have a significantly increased beam coverage compared to terrestrial communication networks. As long as it is within the beam coverage, it is possible to interfere with satellite communication signals. In addition, satellite communication systems have high-gain characteristics, with the S-band synchronous-orbit communication satellite antenna aperture reaching 15 m [10]. The wide-area coverage and high-gain characteristics of satellite communication systems make the risk and probability of interference significantly higher, especially for wide-beam antenna systems used for signaling or IOT message transmission [11].  
In order to improve the anti-jamming performance of satellite communication systems to meet the transmission of highly reliable information, such as signaling transmission, autopilot, far-area flight, disaster warning, etc., the spectrum is usually extended in exchange for a certain anti-jamming gain. The higher the spectrum expansion ratio, the greater the anti-jamming gain. Under the premise of an unchanged transmission bandwidth, the lower the information rate, the stronger the anti-jamming ability. Therefore, in the above scenario, the information rates of each user's signaling secret key, driving instructions, warnings, etc., are generally reduced to the kbps scale for transmission [12; 13].  
According to [PERSON]'s theorem, there is some room for information capacity improvement before the communication link reaches maximum efficiency after spectrum expansion [14].  
Spread spectrum is divided into frequency-hopping spread spectrum and direct spread spectrum. There is a contradiction between the anti-interference capability of information transmission and the need for an information transmission rate. To alleviate the contradiction between the two, this paper proposes a capacity enhancement method for the anti-jamming communication system for FH, which aims to increase the information transmission capacity of the anti-jamming communication system by modulating the incremental information in the frequency-hopping frequency point dimension without guaranteeing the anti-jamming capability of the original system or increasing communication resources, such as the power and bandwidth.