esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Compressive Sensing Algorithm
### Compressive Sensed Signal Reconstruction  
The primary concept CS aims to reconstruct the original signal from a minimal number of measurements. To recover the original signal from the compressed domain, the signal \(x\) must have a sparse representation in any specific domain. The signal \(x\) can be modeled as \(x=\Psi s\) the acquisition process can be modelled as  
\[\mathrm{y}=\Phi x \tag{2}\]  
where as \(\mathrm{y}\) is incomplete measurement of \(x\) and \(\Phi\) is the sensing matrix. Naturally, most of the signals are sparse, i.e., they have few non-zero elements or have a sparse representation in another domain. Here, \(s\) being the sparse representation of \(x\) and \(\Psi\) the sparsifying matrix, because it maps the signal into a domain where its representation is sparse. \(\Psi\) may be a transformation matrix (e.g., DWT, DCT) mapping to a sparse domain can be expressed mathematically as:  
\[x=\Psi s \tag{3}\]  
Considering \(s\) being the sparse representation of \(x\), Equation (3) is obtained by combining Equations (2) and (3)  
\[\mathrm{y}=\Phi\Psi s \tag{4}\]  
By solving an optimization problem and using \(\mathrm{y}\), \(s\) can be retrieved. This can be found out using the \(l_{0}\), \(l_{1}\), and \(l_{2}\) norms, though \(l_{1}\)-norm yields a precise result, as it is a  
Figure 4: Sparsifying matrix. Figure 5: Communication framework using CS. nonprogrammable hard issue; it is rarely used and the \(l_{2}\)-norm is not recommended, as it creates significant errors. Since \(l_{1}\) norm has less error, it is the most suitable and commonly used norm for this optimization problem [53; 54; 55; 56; 57; 58]. It is denoted as in the following equation:  
\[\underbrace{min}_{s}\frac{1}{2}\|y-\Phi\mathbf{\Psi s}\|_{2}^{2}+\tau\|s\|_{1} \tag{5}\]  
The CS reconstruction algorithms are classified into various categories, some of the most commonly used algorithms with their sub category are as listed in Table 1.  
Each approach has certain pros and inherent cons, convex optimization can successfully reconstruct original signal form a smaller number of measurements. However, computational complexity is more [65; 66; 67; 68]. Greedy algorithms are normally fast and time complexity is less, so it requires a matrix-inverse operation in each iteration, which requires more expensive hardware [69; 70; 71; 72; 73; 74]. Combinational algorithms can quickly reconstruct data, but they require unusually structured samples, which could be difficult to obtain in practice [75; 76; 77; 78; 79; 80; 81]. The Non-Convex algorithm requires lesser measurements to recover a signal, performs better even under weaker RIP for larger signal and is difficult to implement, while the complexity is similar to that of convex optimization technique [82; 83; 84; 85]. The Bregman algorithm is fast and gives a more sparse output, but computational cost is very high [86; 87; 88]. Figure 5 illustrates an overall communication framework using CS.
# Revealing Coupled Periodicities in Sunspot Time Series Using Bispectrum--An Inverse Problem
## 2 Sunspot SILSO Data and Planetary Orbits
### Planetary Orbital and Synodic Periods  
In order to explain the periodicities that appeared in the spectrum of the sunspot time series, it was necessary to know exactly the orbital periods of the planets as well as the synodic periods of most pairs of planets, especially those that were close to the Sun and provided harmonics longer (smaller periods) than the eleven-year circle (-4000 days). The orbital periods of the planets are described by Kepler's laws and can be found in any astronomical handbook. Additionally, the synodic periods can also be found in astronomical handbooks and can be evaluated by the following relationship:  
\[\text{S}/\text{P}_{1}-\text{S}/\text{P}_{2}=1 \tag{1}\]  
where P\({}_{1}\) is the period of one planet, P\({}_{2}\) is the (longer) period of the second planet moving around the Sun, and S is the synodic period of the two planets. The synodic period of two planets is considered the time required for the centers of the two planets to be in alignment with the center of the Sun for two consecutive times. Table 1 presents the orbital and synodic periods, the values of which are at most of the order of the eleven-year circle. In the third column in Table 1 is the position of the corresponding frequency (the inverse of the planet or synodic periods) along the power spectrum as it is depicted in the Figures in Section 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**\#**} & **Planets’ Periods** & **Orbital or Synodic** & **Position in the 32,000 Harmonics** \\  & **Planets’ Synodic Periods** & **Periods: P** & **Spectrum, Including Position 1** \\  & & & the DC (**Position 32,000/P**) \\ \hline
1 & Uranus & 30,589 & 2.04 \\ \hline
2 & Saturn & 10,747 & 3.96 \\ \hline
3 & Jupiter–Saturn & 7254 & 5.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbital and synodic periods of the planets in days. As synodic period of two planets is considered the time required for the centers of the two planets to be in alignment with the center of the Sun for two consecutive times.  
Figure 2: The 11-years cycle of sunspot data having been processed with a median filter length of 11 days. The horizontal axis represents days.
## 4 Limitations in the Application of MOFs  
Despite their many advantages, MOF materials also have several disadvantages. Some are related to the synthesis of these materials and include: a long reaction time, high temperature requirement [144], reaction solvent [145] or problems with obtaining a large amount of product [146]. Additionally, MOFs may be characterized by low chemical and thermal stability and sensitivity to contamination and cyclability issues, hindering their practical applications [147]. MOFs may also face disadvantages like mass transfer limitations, especially through crystallite surfaces, which may affect their uptake rates and performance in adsorptive separations and catalysis [148]. The powdered state of MOFs often results in difficulties with transfer and separation, as well as with recovery procedures, which reduces their adsorption properties [149]. Another important aspect is the instability of many MOFs in air, water, and acid-base solutions, which limits their further commercial application and means it is not possible to realize large-scale production [150]. There is also a concern about MOF toxicity in H2O, as the washing out of adsorbents can lead to contamination which is greater than the initial pollution [149; 151]. The increasing cost of organic ligands used to prepare MOFs is another drawback [152]. All these limitations may reduce the applicability of MOF materials in various sample preparation techniques.
## 4 Satellite Self-Organizing Network Simulation Platform Based on OMNeT++
### Network Domain  
In OMNeT++, the distribution of the network is set in the *.ned and *.ini files, where the number, mobility, and distribution of nodes can be set. In this paper, the distribution of satellite nodes in the network simulates a leader-follower satellite formation flying scenario, with one leader node and four follower nodes in a formation. The follower nodes follow the leader node in orbiting flight, as shown in Figure 2. A total of 20 formations are set up, with a total of 80 satellites. The distance between the master and slave nodes is 100 km, and the distance between the master nodes is between 600 km and 700 km [19]. During the simulation process, the topology between the master satellites remains relatively stable.  
Figure 2: Node distribution in simulation.
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 4 Experiments
### Overall Performance Study  
Table 7 compares performance results of baseline method with the Class-balanced Hierarchical Refinement (CHR) method, proposed in [21], and the dual branch network, proposed in this paper, on SIXray10 and SIXray100 subsets.  
The analysis of Table 7 leads to the conclusion that compared to baseline and CHR, the method proposed in this paper achieves optimal performance of all classes. To show the difference of performance between the three methods more intuitively, according to Table 7, we compiled a bar chart, as shown in Figure 4.  
Considering overall performance (mAP), compared to the CHR method, our method gains more improvement, with 5.90 points on SIXray10 and 21.31 points on SIXray100. Compared with the baseline method, the value achieves 8.27 points on SIXray10 and 23.68 points on SIXray100. It is worth mentioning that the improvement on SIXray100 is far more than that on SIXray10. This implies that, for the dataset with a higher level of class imbalance, our method gains more on performance. It would benefit from special treatment for class imbalance by the adopted focal loss function.  
Considering average precision (AP) on a single class, improvements on various classes exist in enormous variation. As for classes wrench and scissors, on which baseline performs  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **AP\%** & **Baseline** & **CHR [21]** & **Ours** \\ \hline \multirow{6}{*}{SIXray10} & Gun & 89.71 & 87.16 & **90.66** \\  & Knife & 85.46 & 87.17 & **89.99** \\ \cline{1-1}  & Wrench & 62.48 & 64.31 & **79.90** \\ \cline{1-1}  & Pliers & 83.50 & 85.79 & **89.14** \\ \cline{1-1}  & Scissors & 52.99 & 61.58 & **65.79** \\ \cline{1-1}  & mean (mAP) & 74.83 & 77.20 & **83.10** \\ \hline \multirow{6}{*}{SIXray100} & Gun & 83.06 & 81.96 & **84.84** \\  & Knife & 78.75 & 77.70 & **86.98** \\ \cline{1-1}  & Wrench & 30.49 & 36.85 & **70.00** \\ \cline{1-1}  & Pliers & 55.24 & 64.56 & **81.92** \\ \cline{1-1}  & Scissors & 16.14 & 14.49 & **58.36** \\ \cline{1-1}  & mean (mAP) & 52.74 & 55.11 & **76.42** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Result of multi-label classification performance.  
Figure 4: The performance gained by the proposed method shows enormous variation between classes.  
worse, our method outperforms baseline by 17.42 and 12.80 points on the SIXray10 subset, and by 39.51 and 42.22 points on the SIXray100 subset, respectively. It plays a primary role in the improvement of overall performance. Based on this, it appears that our method makes a better alleviation to objects which suffer greater prediction bias in the training stage, as samples of class scissors are the least in the SIXray dataset. As for class wrench, without consideration of the distribution issue that the dataset itself has, it can be thought that the deep feature fusion module enhances receptive ability of the model to objects that belong to it.
## 5 Conclusions  
A methodology has been proposed for measuring the vibration frequencies and the amplitude of the oscillations of tall structures by joint use of TLS and GB-RAR. The methodology exploits the possibility of using a TLS in line scan mode, thus extracting a large number of scan lines per second. The use of lines interpolating the individual scanned points makes it possible to obtain results with a precision better than that of the instrument. For a given point on the structure, the stack of TLS lines is used to derive the temporal profile of displacements and from this the spectrum of vibration frequencies. It has been observed the TLS estimate of vibration frequencies is limited by the precision of displacement measurements. For oscillations whose amplitude is much smaller than the TLS precision, the proposed methodology based on TLS data cannot estimate the vibration frequency. This problem  
Figure 16: The scheme of rotating mirror of VZ1000 TLS (**a**). Ground distance from TLS of a point 65 m high on the tower base during the acquisition with scan speed 60 Hz (**b**). Power Spectrum obtained by FFT for the ground distance from TLS (**c**). Enlargement of Figure 16c (**d**).  
is overcome by the joint use of co-located GB-RAR. The higher precision of GB-RAR measurements of tower displacements provides vibration frequencies corresponding to smaller oscillation amplitudes.  
Results provided by the proposed methodology based on the joint use of co-located TLS and GB-RAR systems have been compared to the output of a numerical analysis of tower displacements, modelling the tower as subject to a wind load derived from the anemometric data.  
The comparison of results obtained by the numerical analysis and measurements leads to the following conclusions: (a) the proposed processing technique based on the geometrical modelling of the structure can enhance the precision of distance measurements below the 5 mm declared for the TLS instrument; (b) the joint use of co-located TLS and GB-RAR can provide richer information on the dynamical behavior of the structure, with the sub-centimeter spatial resolution of TLS, and on the whole spectral characterization of vibrations, including those having an amplitude much smaller than the TLS precision, thanks to the sub-millimeter precision of GB-RAR measurements.  
The proposed methodology can be a useful and cheap support for non-contact inspection and monitoring of tall structures.  
Conceptualization, S.A.; methodology, S.A.; software, S.A. and G.N.; investigation, S.A. and G.N.; writing--original draft preparation, S.A. and G.N.; writing--review and editing, S.A. and G.N. All authors have read and agreed to the published version of this manuscript.  
This research received no external funding.  
Authors acknowledge EON Italia Spa for support during data acquisition. The authors would like to thank the entire DIAN S.R.L. team for the acquisition and processing of GB-RAR data.  
The authors declare no conflict of interest.
## 3 Adaptive Control Method  
Although active control termination with the transfer function method is applied to the measurement acoustic parameters of sample, some defects are unavoidable for this method. The accuracy degree of the measurement result is mainly affected by two factors: the consistency between hydrophones and the accuracy degree of the transfer function measured between primary source and error signal and between secondary source and error signal. In addition, researchers need to measure the transfer function again once the test frequency changes, which causes the process of measuring sample properties to be overly complex. In this study, we adopt an adaptive algorithm (least mean squares, LMS) to control active termination, which can adjust in time the output of the secondary source automatically in accordance with the error signal. In this way, researchers do not need to measure the transfer function, such that we do not need to consider the measurement error caused by an inaccurate transfer function. The operation process is simplified greatly, reducing the experiment implementation time.
# A Model for Working Environment Monitoring in Smart Manufacturing
###### Abstract  
The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.  
cyber-physical systems; working environment; MQTT; IoT; open-source hardware; open-source-software; smart manufacturing; convolutional neural networks +
Footnote †: journal: Applied sciences
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 4 Evaluation, Result and Analysis  
### Evaluation Environment Setup  
The proposed method was implemented using Python 3.10.9 and PyTorch 2.0.1 with CUDA 11.8. All experiments were performed on a Linux Ubuntu 20.04.6 LTS server with a 24-core Intel(R) Core(TM) i9-10920X CPU (3.50 GHz) and NVIDIA GeForce RTX 4090 GPU (24 GB memory). We set the optimal parameters for the model through various experiments. We set the learning rate to 2 \(\times\) 10\({}^{-5}\), the batch size to 16, and the dropout ratio to 0.1 and used AdamW as the optimization tool. Each dataset is divided into the training set and the testing set according to the ratio of 7:3. We randomly selected 500 samples from each task (6 categories, 16 applications in total) and entered them into the dataset; however, if the number of samples for some applications (e.g., Gmail, SFTP within an application classification) was less than 500, we selected all samples for that application.
## 3 Purification Routes Applied to Lignocellulosic Acid Hydrolysates
### Cross-Flow Membrane Filtration  
Membrane filtration was extensively studied in lignocellulosic biorefineries using alkaline fractionation processes, for instance for the purification of spent liquor in a sulfite pulp mill to remove lignocellulosiconates [91], but this technology was used in a more limited extent to purify lignocellulosic dilute acid hydrolysates. Monosaccharides from acid hydrolysates cannot be purified in one membrane filtration step. Membranes with a molecular weight cut-off (MWCO) of 10-50 kDa can be used to separate lignin or proteins in the reentate from the monomeric sugars, sulfuric acid, and the other impurities (acetic acid, furans) in the permeate [71; 92]. Membranes with MWCO of 150-300 Da can retain monomeric sugars (glucose, xylose, arabinose), while acetic acid and furans pass through the membranes [93; 94].  
Organic flat sheet membranes of 10, 20, and 50 kDa were efficient to totally retain macromolecules (lignin and proteins) from a wheat bran acid extract, unlike ceramic tubular membranes with lower MWCO (8 and 15 kDa), where the retention of macromolecules was not total [71]. In another study, several successive concentrations by a volumic reduction factor of 3.6 of a wheat bran acid extract were run on a 10 kDa organic membrane (polysulfone filtration layer with polypropylene support material), and the flux was on average about 10 L/h/m\({}^{2}\) showing good reproducibility [92]. Afterward, diafiltration with 2.5 diavolumes was required in order to maximize sugar recovery in the permeate (99% recovery). Filtration of rice straw dilute acid hydrolysate adjusted to pH 3 on an organic spiral wound membrane with an MWCO of 150-300 Da led to total retention of glucose and very high retention of xylose and arabinose (over 94%), while acetic acid and furans totally passed through the membrane [93]. The separation performance decreased when the operating temperature was increased from 25 to 40 \({}^{\circ}\)C.  
During the filtration of lignocellulosic hydrolysates, fouling occurred on the membrane, changing the initial properties of the membrane (membrane permeability and selectivity), and membrane cleaning was necessary to recover its initial properties. Membrane cleaning with 0.01 N of sodium hydroxide and rinsing with water was enough to recover the initial water flux after the filtration of rice straw dilute acid hydrolysates [93].
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
###### Abstract  
The security and privacy of a system are urgent issues in achieving secure and efficient learning-based systems. Recent studies have shown that these systems are susceptible to subtle adversarial perturbations applied to inputs. Although these perturbations are difficult for humans to detect, they can easily mislead deep learning classifiers. Noise injection, as a defense mechanism, can offer a provable defense against adversarial attacks by reducing sensitivity to subtle input changes. However, these methods face issues of computational complexity and limited adaptability. We propose a multilayer filter defense model, drawing inspiration from filter-based image denoising techniques. This model inserts a filtering layer after the input layer and before the convolutional layer, and incorporates noise injection techniques during the training process. This model substantially enhances the resilience of image classification systems to adversarial attacks. We also investigated the impact of various filter combinations, filter area sizes, standard deviations, and filter layers on the effectiveness of defense. The experimental results indicate that, across the MNIST, CIFAR10, and CIFAR100 datasets, the multilayer filter defense model achieves the highest average accuracy when employing a double-layer Gaussian filter (filter area size of \(3\times 3\), standard deviation of 1). We compared our method with two filter-based defense models, and the experimental results demonstrated that our method attained an average accuracy of 71.9%, effectively enhancing the robustness of the image recognition classifier against adversarial attacks. This method not only performs well on small-scale datasets but also exhibits robustness on large-scale datasets (_mini_ImageNet) and modern models (EfficientNet and WideResNet).  
adversarial attack; deep learning; defense method; machine learning +
Footnote †: journal: _Sci_  
0000-0001-48819
## 4 Numerical Methods for the Solution
### Comparison with Experiment  
A small tank experiment is taken for comparison. The cable is fully immersed and allowed to reach the initial static configuration at the beginning. The experimental cable is initially suspended vertically and statically in the tank. The flow velocity is 1.543 m/s (3 kn), pulling a ball weighing 8.9 N in the water. The cable is 3.66 m long, and its diameter is 3.05 mm. The simulation time is 12 s.  
The comparisons indicate that the simulated results are in fairly good agreement with the experiment (Figure 6). The slight discrepancy may be attributed to the inaccurate drag coefficients or certain non-modeled effects such as the bending stiffness of the cable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline Seawater density (kg/m\({}^{3}\)) & 1025 & Cable length (m) & 150 \\ Drag coefficient in transverse direction \(C_{n}\) & 1.2 & Cross-sectional area (cm\({}^{2}\)) & 12.56 \\ Drag coefficient in longitudinal direction \(C_{t}\) & 0.024 & Segment length (m) & 15 \\ Relative working depth (m) & 100 & Number of links & 10 \\ Diameter (m) & 0.04 & Segment mass (kg) & 18.3 \\ Young’s modulus (N/m\({}^{2}\)) & \(1.0\times 10^{9}\) & Mass in the water (kg/m) & 0 \\ Mass in the air (kg/m) & 1.22 & Minimum fracture strain (kN) & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the lumped parameter method (LPM).  
Figure 5: Flow chart of tethered ROV motion simulation.