esa-sceva/satcom-chunk-collection huggingface dataset:
# Aiding Dictionary Learning Through Multi-Parametric Sparse Representation
## 3 Analysis of the Multi-Parametric Formulation Induced by the DL Problem
### Illustrative Example  
For illustration purposes, we depict a proof-of-concept example. We take \(\mathbf{T}=\begin{bmatrix}1&0&\sqrt{2}\\ 0&1&\sqrt{2}\end{bmatrix}\), \(\mathbf{\mathrm{\Delta}}=\mathbf{I}_{3}\), \(\delta=1\), thus the the space dimensions are \(m=2\), \(n=3\), \(p=3\). Having taken \((\Delta=\mathbf{I}_{3},\delta=1)\) means that \(\|\mathbf{\mathrm{\Delta}}x\|_{1}\leq\delta\) describes the three-dimensional, cross-polytope \(C_{3}=\mathrm{conv}\,(\pm\mathbf{e}_{1},\pm\mathbf{e}_{2},\pm\mathbf{e}_{3})\), as shown (semi-transparent red object) in Figure 2.  
The geometrical interpretation of solving (10) is that the unconstrained optimum (the one for which \(\mathbf{T}\xi-\mathbf{y}=0\)) is projected on the feasible domain, thus leading to the constrained optimum (i.e., the solution \(\xi^{*}\)). This is done by inflating the sub-level cost sets (those for which the cost remains constant on their boundary) until there is an intersection with the feasible domain. Since, in the particular case of (10), the quadratic cost depends on a semi-definite matrix (\(\mathbf{T}^{\top}\mathbf{T}\) is not full-rank); this means that the optimum cost lies in the null subspace of matrix \(\mathbf{T}^{\top}\mathbf{T}\) and the sub-level sets correspond to a degenerate ellipsoid (the blue-and-dotted cylinder shown in Figure 2). Note that, in (14) and its subsequent variations, we made a change of variable that increases the search space from \(\mathbb{R}^{3}\) to \(\mathbb{R}^{6}\), which is, of course, not representable in 3D.  
Continuing with the geometric interpretation: the sub-level surface is inflated and touches the feasible domain at some point on its boundary. As long as this point is described by the same combination of active constraints (this means in Figure 2 that it is described by the same combination of vertices), we have that the solution \(\alpha(\mathbf{y})\) is given as in (24) for a fixed index \(i\). Moreover, we identify the region \(\mathcal{R}^{i}\) in which \(\mathbf{y}\) lies. For this numerical example, the 16 regions \(\mathcal{R}^{i}\) obtained through the active set enumeration procedure are shown in Figure 2(a) and the associated cost value in Figure 2(b).  
We have not illustrated the actual solution \(\alpha^{*}(\mathbf{y})\) (due to its dimension). We can however note that, as expected, no more than \(m=2\) vertices (corresponding to an edge in Figure 2) are active for any of the critical regions.
## 3 Experimental Study on the Coefficient of Restitution of Wheat Grains  
### Factors Influencing Determination of the Coefficient of Restitution  
When determining the coefficient of restitution (\(e\), hereafter \(CoR\)) for cereal grains, a variety of factors can affect accuracy and repeatability. Although theoretical models offer a framework, practical measurement must account for real-world complexity. Previous work has shown that the test material itself strongly influences \(CoR\)[29]; for grains, however, the impact angle often exerts an even greater effect. In addition, higher impact velocities generally yield lower \(CoR\) values because more energy is lost to deformation [13; 30].  
Key grain-related and environmental variables are summarised below:  
* Moisture content--higher moisture levels reduce \(CoR\)[6; 31]; water softens the kernel, lowering its stiffness and elasticity;
* Variety and physical properties--size, shape and elastic modulus vary among cultivars and alter collision dynamics [32; 33]; structural features such as bran layers and endosperm composite further modulate the response [32; 34];
* Environmental conditions--temperature and humidity during growth or testing modify mechanical properties and, consequently, \(CoR\)[33; 35].
* Impact velocity, which directly affects \(CoR\), can be controlled in two common ways: (i) a calibrated spring launcher [29] or (ii) varying the drop height, a method widely used with wheat grains [23; 28]. The latter approach produces velocities of 1-5 m s\({}^{-1}\) with good repeatability while preserving the grain's initial state.  
This overview highlights the need to specify grain variety, moisture level, impact angle and velocity when reporting \(CoR\) data so that results remain comparable across studies.
## 2 Background and Related Work
### Related Works  
The most closely related works are the feature selection techniques in the data preprocessing of ML/AI-based disk failure prediction. Previously, some works trained models without feature selection [17]; however, some features are not strongly correlated with drive failures, and using them for failure prediction could have a negative impact on the model performance [11]. Thus, feature selection is widely employed in the disk failure prediction field and has crucial impacts on prediction quality. Generally, features are selected based on their relationship with failures. Some works selected features based on expertise [5]. Some works obtained features using statistical methods for modeling; for example, reverse arrangement tests, rank-sum tests, and z-scores were used in [15]. [PERSON] et al. [13] used the correlation coefficient and information gain to select features. [PERSON] et al. applied principal component analysis (PCA) to find the most related features for disk failure prediction modeling. [PERSON] et al. [8] employed J-Index [18] for feature selection, where it determined the correlation between an attribute and failure. [PERSON] et al. [9] and [PERSON] et al. [19] selected features based on [PERSON]'s correlation coefficient [20] to obtain a linear correlation between continuous data, and they also chose features that had closer correlations with failures. Moreover, some works selected features based on machine learning techniques; for example, [PERSON] et al. [21] used random forests to find features, and the features with high information gain were selected for modeling. However, most of the previous works mainly focused on improving the prediction accuracy by determining whether the selected features correlated with failures, which can obtain good prediction results, but the trade-off between prediction accuracy and modeling timeliness was not explored further. In this study, to find a better way to balance prediction accuracy and modeling timeliness, both the correlation between attributes and failures and the correlation between different attributes are considered for feature selection design, and a two-layer classification-based scheme is proposed.
# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## VMC Implementation and Experiment Design
For this purpose 12 test cases were considered:  
* \(P_{0}\)--prioritization for the initial state,
* \(P_{1}\)--CIA value change for 10% of assets,
* \(P_{2}\)--CIA value change for 20% of assets,
* \(P_{3}\)--CIA value change for 30% of assets,
* \(P_{4}\)--10% of assets marked as DELETED,
* \(P_{5}\)--20% of assets marked as DELETED,
* \(P_{6}\)--30% of assets marked as DELETED,
* \(P_{7}\)--the increase of new vulnerabilities by 10%,
* \(P_{8}\)--the increase of new vulnerabilities by 20%,
* \(P_{9}\)--the increase of new vulnerabilities by 30%,
* \(P_{10}\)--10% of vulnerabilities marked as FIXED (fixing the vulnerability),
* \(P_{11}\)--20% of vulnerabilities marked as FIXED (fixing the vulnerability),
* \(P_{12}\)--30% of vulnerabilities marked as FIXED (fixing the vulnerability). The simulations were repeated three times and afterwards the average value of the simulation time was calculated for each \(P\). Each time simulations were repeated the VMC software was restarted in order to exclude the influence of optimizations performed automatically by autonomous VMC components, which are not the subject of the presented research, e.g., each time Elasticsearch handles the same request it uses cache to speed up operation. Such optimization of course influences the measured CPU time and thus distorts the calculated results and hence should be prevented from taking place.
# Computational Design of a Broadband In-Line Coaxial-to-Rectangular Waveguide Transition
## 3 Design Examples
To ascertain the validity of the computational design, we compared the time- and frequency-domain simulations of the WR75 transition in Figure 11. The agreement between the two simulation methods is excellent. In Table 4, we compare the proposed transition design with previous works. We performed a thorough literature search for comparison and listed in Table 4 only those previous works that are worthy of comparison in this paper. For completeness, we include a commercial transition by Exceed Microwave [28], which is shown in Figure 1 along with its reflection coefficient graph. Compared with the bandwidth of 1.36:1 to 2.00:1 in the previous works, the design approach presented in this Communication achieves a 2.83:1 bandwidth, a great increase from existing results. The proposed transition uses a WR75 waveguide and operates from 8.14 GHz to 23.00 GHz, fully covering the WR90, WR62, and WR51 waveguide bands. Figure 11: Reflection and transmission coefficients of the designed WR75 transition by the time- and frequency-domain simulations.
## 4 Conclusions  
In the twisting process of the direct-twisting machine, the twisting tension is the key factor affecting the balloon shape, twisting quality, and twisting energy consumption. In the case of ignoring air resistance, the tension equation of the yarn balloon can be established according to the principle of kinematics. Through this theoretical equation, the relationship between tension and process parameters such as spindle angular velocity and yarn fineness can be roughly seen. However, the exact relationship between tension and process parameters such as yarn fineness and spindle angular speed for a specific model of yarn balloon needs to be predicted by fitting equations based on experimental data. It is an effective method to establish the fitting equation of tension change based on the test data, which can provide a more accurate reference basis for the design of twisting mechanism and process parameters of a direct-twisting machine.  
Conceptualization, S.M. and Q.X.; Methodology, S.M., M.Z. ([PERSON]), L.Y. and Q.X.; Software, M.Z. ([PERSON]) and D.Q.; Validation, S.M. and M.Z. ([PERSON]); Formal analysis, M.Z. ([PERSON]) and L.Y.; Investigation, M.Z. ([PERSON]), D.Q., M.Z. ([PERSON]) and L.Y.; Resources, S.M. and M.Z. ([PERSON]); Data curation, M.Z. ([PERSON]), D.Q. and L.Y.; Writing--original draft preparation, M.Z. ([PERSON]) and S.M.; Writing--review and editing, S.M. and M.Z. ([PERSON]); Visualization, S.M. and M.Z. ([PERSON]); Supervision, S.M.; Project administration, S.M., Q.X. and M.Z. ([PERSON]); Funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The Science and Technology Program of Hubei Province (No. 2019 AEE011) and The National Science Foundation of China of China (No. 51175385).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Twisting Configuration**} & \multicolumn{2}{c}{**Error Analysis**} \\ \hline \multirow{3}{*}{**Twisting** **Tray/mm**} & **Yarn** **Storage** & **Spindle** **Can/mm** & **Yarn** **Fineness/dtex** & **Spindle** **Angular** **Velocity/(rad/s)** & **Measured** **Value/cN** & **Predicted** **Value/cN** & **Absolute** **Error/cN** & **Relative** **Error/\%** \\ \cline{3-8}  & **Tray/mm** & & 930 & 942.48 & 1355.0 & 1329.0 & 26.0 & 1.92 \\  & & 1100 & 785.40 & 1361.0 & 1339.9 & 21.1 & 1.55 \\  & 220 & 1100 & 837.76 & 1427.4 & 1449.1 & \(-\)21.7 & \(-\)1.52 \\  & 1100 & 890.12 & 1526.6 & 1547.8 & \(-\)21.2 & \(-\)1.39 \\  & 120 & 1100 & 942.48 & 1628.8 & 1624.3 & 4.5 & 0.28 \\  & 1670 & 785.40 & 2036.2 & 2018.9 & 17.3 & 0.85 \\  & 266 & 1670 & 837.76 & 2145.8 & 2140.0 & 5.8 & 0.27 \\  & & 1670 & 890.12 & 2247.8 & 2238.8 & 9.0 & 0.40 \\  & & 2200 & 785.40 & 2268.8 & 2244.3 & 24.5 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measured and predicted twisting tension error analysis.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 5 Performance Evaluation
### Performance Metrics  
The performance evaluation is done through two security-related metrics and two network-related metrics, which are described below:  
Figure 8: Sequence diagram of the proposed lightweight data authentication mechanism.  
* _Detection ratio (%)_: It represents the ratio of the number of detected unauthenticated data packets to the total number of received unauthenticated data.
* _False positive (FP) ratio (%)_: The FP represents the number of data packets that are detected as unauthenticated while these packets are benign, to the total number of detected unauthenticated data.
* _Average end-to-end delay (s)_: It represents average time between sending the interest packet and receiving content as a result of the request for all connections.
* _Content delivery ratio (CDR) (%)_: CDR means the number of successfully fulfilled interest packets to the total number of generated interests.
* _Energy efficiency (W.s)_: It shows the average consumed energy for both strategies with and without the distributed monitoring.
* _Generated Overhead (Kb/s)_: It represents the additional trust establishment fields.  
First, we discuss the proposed trust-based data authentication strategy as compared to the traditional strategy implemented by vanilla NDN together with their generated false positive as shown in Figure 9. Afterwards, we study their ability to cope with delay-sensitive applications where the average end-to-end delay and the CDR as shown in Figure 10. Finally, we show through Figure 11, the average energy consumption and the generated overhead.  
Figure 10: Network-related performance of both Vanilla NDN and our proposal.  
Figure 9: Security-related performance of both NDN and our proposal.
## 5 Numerical Results
### Flow-Vibroacoustic Simulation Result of HSM
It can be seen that the interior wind noise was dominated by the acoustic surface pressure field as the frequency increased, although the exterior acoustic energy was less than that of the hydrodynamic one in the corresponding frequency bands. This result reveals that the transmission efficiency of acoustic excitation was much higher than that of hydrodynamic excitation. In particular, a sharp increase in the relative contribution of compressible acoustic pressure was observed to begin in the frequency band around 4 kHz. The vibration of the plate is closely related to the wavelength of the excitation pressure. The frequency at which the vibration wavelength of the plate becomes the same as the wavelength of the excitation pressure is called the coincidence frequency [31]. At the coincidence frequency, the vibration of a plate excited by sound waves acts as if it is transparent and has zero impedance. Under this condition, the sound waves incident on the plate are freely transmitted into the interior cabin. The coincidence frequency (\(f_{c}\)) of the front  
Figure 21:0 Regenerated (**a**) surface pressure (unit: Pa); (**b**) mode shape of left window. Figure 21:1 Comparison of numerical and measured interior noise levels using the injected power formula of Equation (75). windshield and side window of the HSM was 3820 Hz and 3620 Hz, respectively, obtained from the maximum value of radiation efficiency. A greater contribution of compressible pressure components observed in the frequency band around 4 kHz can be considered due to this coincidence effect.
## 2 Materials and Methods  
### Dataset Preparation  
Dormant Panax ginseng roots were obtained from the Ginseng National Research Center in Daejeon, South Korea. After storage for 1 month at 4 degrees Celsius to break dormancy, the roots were planted in small pots. The soil used in our experiment was collected from the field and was contaminated with the soil-borne pathogen (Cylindrocarbon destructans) that causes root rot disease in ginseng plants. The collected soil was divided into two halves. One-half of the soil was sterilized using an autoclave [31]. The soil was sterilized at a temperature of 121 degrees Celsius for 20 min. The sterilization was repeated three times under similar conditions to ensure the healthiness of the soil. The plants were then grown in two different soil conditions: (i) pots containing the healthy soil and (ii) pots containing the soil mixed with soil-borne pathogens (Cylindrocarbon destructans). To grow the plants, the growing conditions were set to a temperature of 20 degrees Celsius with relative humidity of 60-70%, manual watering once a week, and continuous light intensity of 15,000 Lx. In our experiment, biotic stress was the only stress applied to the ginseng plants. Among the diseased plant images, the asymptotic diseased plant images [25] exist and did not show any symptoms in leaves but in roots. Figure 1 shows the plant growth conditions in the chamber.  
RGB images of the ginseng plants were captured one day every week between the 10 th week and 16 th week using a Canon EOS 700D camera. The imaging device is shown in Figure 2. These images were stored in a computer separately for further analysis. The entire dataset of images was divided into training and testing images. There were  
Figure 1: Plant growth chamber.  
2112 healthy plant images and 1991 diseased plant images in the training dataset. The testing dataset contained 760 healthy plant images and 770 diseased plant images. Image augmentation [32; 33] was adopted in our experiment. The variations such as Rotation in the range of \(\pm\)180 degrees; Height and/or width shifting of \(\pm\)0.1 of image dimensions; Contrast variation with the factor between 0.75 and 0.95; Illuminance variations with a factor of 0.5 and 1.5 are randomly applied. Figure 3 shows a few augmented images.  
Of the training dataset, 10% were used for validation, and the remaining data in the training dataset were used for training the model. The images were carefully divided for training, validation, and testing such that plants in different stages were distributed evenly among the datasets in order to ensure that the proposed method is capable of identifying diseased plants at different stages. Once the images were collected during the 16 th week, the plants were harvested to check the ginseng roots and root images were also captured in order to cross-verify the proposed model. The size of the plant and root images was 3456 \(\times\) 5184.
# Pseudorange Measurement Method Based on AIS Signals
## 2 Principle of Pseudorange Measurement in AIS-R
### Timestamp Option  
The special symbols containing emission timing information in wireless transmission signals are called timestamps. Selecting easily monitored timestamps and designing their detection model are the basis of any TOA measurement system.  
Usually in TOA measurement systems, timestamps are in an arbitrary carrier phase or code phase. AIS is typically communications based, which is not primarily intended for positioning so it is impossible to measure the arbitrary code phase. Based on this factor, AIS uses Gaussian minimum shift keying (GMSK) modulation, where the carrier frequency changes in a linear fashion. When the current carrier phase is detected, the carrier frequency cannot be obtained, thus the launching time \(t_{T}\) cannot be determined. A correlation detection method based on a fixed message format is proposed in the literature [23], which wastes the limited AIS communication bandwidth.  
The initial transmission time of each frame signal can be determined as the AIS uses time division multiple address (TDMA) networking mode, so the launch time \(t_{p0}\) of every bit-0-phase in the frame can be calculated. The bit-0-phase corresponds to the hopping edge in the digital message sequence shown in Figure 1a. These features are easily detected by the receiver, so the bit-0-phase that contains launch time is regarded as the timestamps in the paper.  
Considering that AIS adopts the GMSK modulation method, the reproductive baseband signal in the receiver smoothly changes the Gaussian baseband waveform shown in Figure 1b, before the receiver obtains the binary bit sequence by the bit decision algorithm, which only focuses on the bit error rate and is not concerned with the launch time information of the bit-0-phase. Therefore, the research subject can only be the Gaussian baseband waveform rather than the digital message sequence. Thus, the issue of how to accurately detect the bit-0-phase in the smooth Gaussian baseband waveform first needs to be resolved.
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
### Novelty  
State-of-the-art solutions and commercial network-planning tools only support a single wireless technology and perform open-loop networking planning, that is they define AP locations based on a predefined floorplan and perform an on-site measurement campaign. Tools that use a monitoring system adjust a single parameter, that is, a monitor network interference, and automatically switch channels based on channel occupancy.  
Instead of only monitoring interference and modifying channel settings, we present a closed-loop algorithm for monitoring the packet error rate (PER), received signal strength, signal-to-noise, and latency, and reconfiguring the PHY and medium access control (MAC) parameters of the network: modulation and coding scheme (MCS), output power, bandwidth, and frequency settings. The closed-loop algorithm allows to quickly determine and solve network issues, and enables wireless machine-to-machine communication with guaranteed quality of service (QoS) in an industrial environment. The framework is not only tested via simulations, but experimental validations in an industrial lab are also performed.  
To the best of the authors' knowledge, this is the first closed-loop framework that simultaneously adjusts PHY and MAC layer parameters of multiple wireless technologies, including WiFi, ZigBee, LoRa, and SDR, and has been implemented and experimentally tested in an industrial environment using IEEE 802.11 and IEEE 802.15.4 technologies. This closed-loop framework ensures a fully automated system with limited manual maintenance, while providing QoS without a fully redundant network architecture. This makes it unnecessary to perform an on-site measurement campaign prior to network-planning, as the network automatically adjusts to the environment. Furthermore, the support for legacy and new wireless technologies makes the framework suitable for the design of networks for FoF, where different types of technologies are required.  
In Section 2, we first present the overall system architecture and then describe link monitoring on a per-packet basis, PHY network-planning, QoS monitoring, and PHY layer configuration. Section 3 presents the simulation and validation scenarios. In Section 4, we present and discuss simulation and validation results of the proposed framework. Section 5 concludes this paper.