esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis
#### 2.2.3 Machine Learning Evaluation  
The preprocessed data were used to train an ML decoding pipeline using Common Spatial Pattern (CSP [45]) features and Linear Discriminant Analysis (LDA [46]) for 2-class classification. This decoding pipeline was chosen for its low complexity and common usage in MI decoding [5]. The classification task consisted of distinguishing feet and right hand MI. For the CSP features, 4 components were used. The pipelines were evaluated by 5-fold cross-validation for each sensor subset.  
All reported metrics were obtained by averaging the classification accuracy on the test set for each of the train-test splits that were obtained from cross-validation. For each subset, a new pipeline was trained using only the sensors that are part of the subset. The mean cross-validation accuracy obtained by training on all sensors was used as the baseline against which we compare performance when training the decoding pipeline with a subset of sensors. Mean cross-validation accuracies were compared with an independent sample \(t\)-test at a significance level of 0.05. This test was chosen since the data originate from separate classifiers trained on different data subsets. The resulting accuracy distribution is normal due to the central limit theorem [47] since the compared means are themselves a result of mean cross-validation accuracy.  
The considered sensor subsets were chosen based on existing datasets, the sensors used in commercial devices, and brain regions associated with motor activity. The central regions of interest were the motor cortex, which typically shows MI activity [12], and also other brain regions associated with MI [13]. Figure 3 shows the electrode locations on the scalp for each subset according to the 10-10 system.  
The Full subset uses all 64 EEG channels, while the Half subset only uses 32 channels with reduced spatial resolution. The BCI Comp subset uses 21 of the 22 locations present in the BCI competition IV dataset 2a [36]. The excluded location of FCz could not be used because the LiveAmp device uses it as the reference. The OpenBCI 8 and OpenBCI 16 subsets correspond to the default sensor locations for the OpenBCI Ultracortex Mark IV headset ([[https://docs.openbci.com/AddOns/Headwear/MarkIV/](https://docs.openbci.com/AddOns/Headwear/MarkIV/)]([https://docs.openbci.com/AddOns/Headwear/MarkIV/](https://docs.openbci.com/AddOns/Headwear/MarkIV/)) (accessed on 26 January 2023)) in its 8-channel and 16-channel configurations, respectively. Finally, the Motor cortex (MC) subset corresponds to the 24 locations that are associated with motor activity and the MC reduced subset only uses 9 of those locations.  
Data were processed and analyzed with Python 3.9, using the MNE-Python software (v 1.0.3) library for EEG analysis [48]. Machine learning methods that decode EEG were implemented with the Scikit-Learn library [49]. The statistical analysis was performed with the Pandas library [50] for data manipulation and SciPy [51] for statistical testing. Figures were generated with the Seaborn library [52].
# The Extraction of Maximal-Sum Principal Submatrix and Its Applications
## 6 Applications
### Construct a Optimal Color Combination
#### 6.1.1 Construction of Color Distance Matrix  
The most commonly used color space in computer systems is RGB color space, whose pixel values are generally obtained through hardware devices such as image acquisition cards and CCD cameras. Throughout the entire color space \(\Omega\), all colors can be represented by three channels:  
\[\Omega=\left\{(r,g,b)|r,g,be\{0,1,\cdots,255\}\right\}\]  
Traditionally, red can be represented as \((255,0,0)\), green can be represented as \((0,255,0)\), and blue can be represented as \((0,0,255)\).  
Definition of standard color difference is based on RGB color space. In \(\Omega\), let any two colors \(x_{i}=(r_{i},g_{i},b_{i})\) and \(x_{j}=\left(r_{j},g_{j},b_{j}\right)\); the spatial distance \(d\left(x_{i},x_{j}\right)\) between colors \(x_{i}\) and \(x_{j}\) is defined by the Euclidean distance.  
\[d\left(x_{i},x_{j}\right)=\sqrt{\left(r_{i}-r_{j}\right)^{2}+\left(g_{i}-g_{j} \right)^{2}+\left(b_{i}-b_{j}\right)^{2}}\]  
Obviously, \(d\left(x_{i},x_{j}\right)=d\left(x_{j},x_{i}\right)\), and we abbreviate \(d\left(x_{i},x_{j}\right)\) as \(d_{ij}\).  
Define the distance matrix \(D\) between any two colors. Suppose \(\left|\Omega\right|=n\), which means that there are \(n\) colors used as color candidates. Let \(\forall i,j\in L=\{1,2,\cdots,n\}\), and the distance matrix between different colors can be constructed as:  
\[D=\begin{bmatrix}0&d_{12}&\cdots&d_{1n}\\ d_{21}&0&\cdots&d_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ d_{n1}&d_{n2}&\cdots&0\end{bmatrix}\]  
Matrix \(D\) is an \(n\)-order real symmetric non-negative matrix.  
The MSPS-RU algorithm is applied to the \(n\)-order distance matrix \(D\) to extract the optimal solution \(A_{1^{*}}^{(k)}\) and the optimal indicator set \(I^{*}\). \(I^{*}\) corresponds to the \(k\) selected colors. By this way, the selected colors have the maximal color distance \(d_{ij}\). Therefore, these \(k\) selected colors can form a color combination with maximal difference and readability.
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
###### Abstract  
Wave Energy Converters (WECs) rely on effective Power Take-Off (PTO) control strategies to maximize energy absorption under dynamic sea conditions. Traditional hydrodynamic modeling techniques may require computationally intensive convolution calculations, making real-time control implementation challenging. This paper presents an alternative approach by leveraging instantaneous frequency estimation to dynamically adjust PTO damping in response to varying wave frequencies. Two real-time frequency estimation methods are explored: the Hilbert Transform (HT) and Phase-Locked Loop (PLL). The Hilbert Transform method provides accurate frequency tracking but introduces a delayed response due to its dependence on causal data. Conversely, the PLL approach demonstrates strong potential in frequency tracking but requires careful gain tuning, particularly in complex sea states. Comparative evaluations across multiple test cases--including sinusoidal variations, amplitude steps, frequency step changes, and real-world JONSWAP spectrum waves--highlight the strengths and limitations of each method. The two different PTO control techniques across the various frequency estimation methods were tested under real-sea states using a state-space model of a point-absorbing Wave Energy Converter. The Capture Width Ratio (CWR) is used as a performance metric, with results showing that the HT achieves a 10.6% improvement, while the PLL estimation yields a 0.9% improvement relative to the fixed parameter control baseline. These results highlight the effectiveness of real-time frequency estimation in improving energy absorption compared to static control parameters.  
WEC control; instantaneous frequency estimation; impedance matching; control optimization; median filter Hilbert transform; phase-locked loop +
Footnote †: journal: Applied sciences  
Article
## 5 Results
### Hypothesis Testing  
SmartPLS 3.0 was chosen for Structural Equation Modeling (SEM) analysis due to its suitability for handling complex models with latent variables, small-to-moderate sample sizes, and non-normal data distributions. Unlike Covariance-based SEM (CB-SEM), which requires large sample sizes and strict assumptions about data normality, Partial Least Squares SEM (PLS-SEM) is more appropriate for exploratory research and theory development [83]. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Constructs**} & \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} \\ \cline{3-6}  & & **Loading (0-6)** & **VIF (\(\mathbf{<3}\))** & **Loading (0-6)** & **VIF (\(\mathbf{<3}\))** \\ \hline \multirow{3}{*}{Digital infrastructure capability (DIC)} & DIC1 & 0.902 & 2.514 & 0.75 & 2.856 \\  & DIC2 & 0.761 & 1.337 & 0.641 & 1.554 \\  & DIC3 & 0.832 & 2.192 & 0.634 & 2.26 \\ \hline \multirow{3}{*}{Digital analysis capability (DAC)} & DAC1 & 0.821 & 1.755 & 0.646 & 1.824 \\  & DAC2 & 0.821 & 1.632 & 0.672 & 1.848 \\  & DAC3 & 0.867 & 1.615 & 0.684 & 1.791 \\ \hline \multirow{3}{*}{Strategic support capability (SSC)} & SSC1 & 0.786 & 1.661 & 0.726 & 1.758 \\  & SSC2 & 0.799 & 1.846 & 0.752 & 2.044 \\  & SSC3 & 0.85 & 2.08 & 0.79 & 2.207 \\  & SSC4 & 0.825 & 1.741 & 0.764 & 1.908 \\ \hline \multirow{3}{*}{Supply chain resilience (SCR)} & SCR1 & 0.723 & 1.616 & 0.72 & 1.616 \\  & SCR2 & 0.863 & 2.774 & 0.862 & 2.774 \\ \cline{1-1}  & SCR3 & 0.74 & 1.718 & 0.739 & 1.718 \\ \cline{1-1}  & SCR4 & 0.746 & 1.749 & 0.748 & 1.749 \\ \cline{1-1}  & SCR5 & 0.816 & 2.003 & 0.818 & 2.003 \\ \cline{1-1}  & SCR6 & 0.795 & 2.127 & 0.795 & 2.127 \\ \hline \multirow{3}{*}{Supply chain risk management (SCRM)} & SCR1 & 0.827 & 1.941 & 0.827 & 1.941 \\ \cline{1-1}  & SCRM2 & 0.823 & 1.897 & 0.823 & 1.897 \\ \cline{1-1}  & SCRM3 & 0.805 & 1.664 & 0.805 & 1.664 \\ \cline{1-1}  & SCRm4 & 0.829 & 1.86 & 0.829 & 1.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of confirmatory factor analysis. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Construct** & **Item Numbers** & **Cronbach’s \(\mathbf{\alpha}\)** & **rho\_A** & **CR** & **AVE** \\ \hline Digital infrastructure (DIC) & 3 & 0.78 & 0.78 & 0.87 & 0.7 \\ Digital analysis capability (DAC) & 3 & 0.79 & 0.82 & 0.88 & 0.7 \\ Strategic support capability (SSC) & 4 & 0.83 & 0.84 & 0.89 & 0.67 \\  & 10 (contains) & items of DIC, & 0.89 & 0.9 & 0.91 & 0.5 \\  & DAC, and SSC) & 6 & 0.87 & 0.88 & 0.9 & 0.61 \\  & Supply chain risk management (SCRM) & 4 & 0.84 & 0.84 & 0.89 & 0.67 \\ \hline \multicolumn{5}{l}{Note: Cronbach’s \(\mathbf{\alpha}\) (criterion: \(\geq\)0.7); Composite Reliability (CR) (criterion: \(\geq\)0.7); rho\_A (criterion: \(\geq\)0.7); Average Variance Extracted (AVE) (criterion: \(\geq\)0.5).} \\ \end{tabular}
\end{table} 
Table 3: [PERSON]’s \(\mathbf{\alpha}\) coefficient.
## 4 Performance of Waste-Plastic-Modified Bitumen Binder and Mixture
### Characteristics of Plastic-Modified Bituminous Mixtures
#### 4.2.2 Blending Methods and Processing Techniques  
Ref. [55] assessed dry and wet methods using LLDPE. The dry method (0.5% by weight of aggregate) enhanced mixture stiffness and improved rutting resistance, but resulted in diminished cracking resistance, as seen by a lower CTIndex in IDEAL-CT testing. In contrast, the wet-processed mixture (1.0% LLDPE in binder) exhibited a performance comparable to that of the SBS-modified control in the Hamburg Wheel-Tracking Test regarding dynamic modulus and fatigue resistance, with minimal surface cracking after 10 million Equivalent Single-Axle Loads. Conversely, the dry mixture demonstrated 3.5% surface cracking under the same stress conditions, underscoring the brittleness impact linked to the presence of dry LLDPE.  
Ref. [56] presented the \"PlasticAid\" method, employing shredded LDPE and HDPE as a compaction aid through a modified dry method. Their research demonstrated greater compatibility, reduced rut depth, and improved cracking resistance (as measured by the CT-index), particularly with the utilization of LDPE flakes. Powdered forms exhibited less efficacy. A study by [57] evaluated a hybrid method that combined 50% reclaimed asphalt pavement (RAP) with 0.3-0.5% waste plastic granules. The mixtures maintained strength (in terms of stiffness and volumetric properties) and improved sustainability indicators without compromising workability.
## 3 Obstacles to the Implementation of LCA in the Procurement Process of Buildings
### Economic Obstacles
\begin{table}
\begin{tabular}{l c} \hline \hline
**Economic Obstacles** & **Reference** \\ \hline _Review of up to-date EPD programs for pavement materials_ & \\ _Discussion about stakeholders’ perspectives on the current EPD program with_ & \\ _material manufacturers, public government agencies, and LCA consultants_ & \\ _Use of EPDs in GPP to ensure the environmental improvement of_ & [57] \\ _materials and pavements_ & \\ \(\rightarrow\) Collecting EPDs to establish benchmarks is resource intensive and & \\ requires advanced data management & \\ \hline _Discussion with stakeholders (owner/client, designer, and contractor) about_ & \\ _EPD implementation_ & \\ _Application of EPDs during the design and construction stage using an office_ & \\ _building as case study_ & [58] \\ \(\rightarrow\) Additional cost for LCA application & \\ \(\rightarrow\) Consideration of environmental products can lead to additional cost & \\ regarding transport & \\ \hline _Semi-structured interviews about current practices and obstacles to_ & \\ _environmental requirements in construction_ & \\ _Survey about the existence of municipal policies dealing with_ & \\ _environmental issues_ & \\ \(\rightarrow\) Establishing standardized procedures is time consuming and costly & \\ \(\rightarrow\) Process of training staff is time consuming and costly & \\ \hline _Structured interviews on the assessment of environmental aspects and the_ & \\ _review of environmental requirements_ & \\ _Survey about including environmental requirements in_ & [64] \\ _procument documents_ & \\ \(\rightarrow\) High initial cost & \\ \hline _Assessment of the determinants and drawbacks of green procurement adoption_ & [65] \\ \(\rightarrow\) Lack of money to support GPP & \\ \hline _Interview series to achieve insights into application of environmental_ & \\ _preferences in construction projects_ & \\ _Survey about the application of environmental preferences in the procurement_ & [68] \\ _of construction contracts_ & \\ \(\rightarrow\) Fear of increased cost & \\ \hline _LCA is applied for two products to support procurement decision_ & [91] \\ \(\rightarrow\) Additional cost for LCA application & \\ \hline _Semi-structured interviews to develop a framework for a carbon emission_ & \\ _encompassed tender_ & [92] \\ _Framework for a carbon emission encompassed tender_ & \\ \(\rightarrow\) Developers may not be willing to bear additional cost & \\ \hline _Development of the Thai National LCI Database_ & [94] \\ \(\rightarrow\) No funding supports for LCI development & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Economic obstacles to LCA implementation in the procurement process of buildings. there are no funding supports for the implementation of early LCA. The establishment of a so-called \"(public) climate fund,\" which also addresses the pre- and post-procurement phases, could reduce these additional costs during the initial implementation of more environmentally friendly building projects.
## 2 Methodology
### Power Generation Performance Loss Factors
#### 2.2.3 PCS Standby Loss  
The PCS remains in standby mode until more than a certain amount of power is not produced in the PV array (until the operating conditions of PCS are met). This is called the automatic start/stopfunction of the PCS. In this paper, \"PCS standby loss\" is defined as the loss that occurs when the solar irradiation incident on the PV array exists but does not satisfy the conditions for operating the PCS, and thus PCS remains in standby mode due to the automatic start/stop function of the PCS. To analyze the PCS standby loss, the representative days of clear sky (3 June 2012) and overcast sky (6 June 2012) were analyzed according to the sky conditions, and the monthly ratio of unused solar irradiation was analyzed.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### Segmentation-Based Observation Units  
The areal units provided by the Finnish Forest Centre represent forest areas to be treated by sanitary cuts due to the windstorm damage. They may be larger than the real damage area. We tested whether it was possible to delineate the entire forest area into sub-areas of which a part of the sub-areas are changed due to the damage. A common practice is to use segmentation for the delineation of the area of interest. The input for segmentation should include the information of the changes. A speckle removal before segmentation could improve the quality of segmentation.
## 3 Conclusions  
To conclude, we note that TE\({}_{19,8}\) is not the only high-order mode that can be used for five-fold frequency multiplication in the free-running gyrotron operation regime. According to Figure 2a, the neighboring parasitic mode's excitation zone is shifted by 2-3% in terms of magnetic field. For lower operating modes, this shift is even larger. Correspondingly, the sub-MW-level gyrotrons operating at TE\({}_{10,5}\) or TE\({}_{12,5}\) can also be used for 5 th CH excitation.  
In order to increase the generated 5 th CH power, one would have to use MW-level gyrotrons, which possess a much denser mode spectrum. In this case, excitation zones of different modes would overlap and the parasitic modes would prevent the excitation of the 5 th CH at the optimal parameters. To avoid this, the generation region of the fundamental-harmonic operating mode can be expanded by locking the gyrotron with an external signal that suppresses parasitic oscillations (see details in [22]). According to preliminary estimations, frequency-locked MW-level gyrotrons can provide the radiation power at the level of 10-20 W in the sub-THz band. Such parameters of radiation can be of interest, for example, for the molecular spectroscopy [23], including observation of so-called forbidden transitions.  
Conceptualization, G.D.; Investigation, I.Z. ([PERSON]) and R.R.; Methodology, A.M.; Project administration, M.G.; Software, A.S.; Supervision, I.Z. ([PERSON]); Validation, M.G.; Writing--original draft, I.Z. ([PERSON]); Writing--review & editing, I.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the IAP RAS Program #0030-2021-0027.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 2: (a) Results of simulations for the 5 th harmonic multiplication. Output power vs. guiding magnetic field for competing TE\({}_{19,8}\) and TE\({}_{-17,9}\) modes at the 1 st cyclotron harmonic and for TE\({}_{95,37}\) mode at the 5 th cyclotron harmonic. Orange symbols depict the dependence of output power for the spread of electron velocities of 15%. (b) Establishment of the stationary oscillations in the process of competition between 12 modes at the fundamental cyclotron resonance.
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 2 Push-Out Test for Shear Performance Analysis
### Material Properties  
Table 2 shows the steel's material properties. For the studs of all the test specimens, the diameter is 10 mm, the modulus of elasticity is 205 GPa, the yielding strength is 340 MPa, and the ultimate tensile strength is 400 MPa. For the concrete slab, the designed concrete compressive strength is 60 MPa (named C60-grade concrete), the measured cubic concrete compressive strength is 62.1 MPa, and the modulus of elasticity is 3.46 \(\times\) 104 MPa. For the steel plates (Q345D), the measured modulus of elasticity is 210 GPa, the yielding strength is 412 MPa. For the rebar, the measured modulus of elasticity is 209 GPa, and the yielding strength is 458 MPa.
## 7 Discussion and Further Work
### Dealing with Advanced Threats  
Traditionally, mainly the inbound communications are considered the most concerning for which only preventive controls are employed. However, they may not be sufficient: first, because they may not be suitable for the increasing threat sophistication and, secondly, they mainly focus on the inbound communication [51]. For instance, traditional intrusion detection or prevention systems are based on the concept of intrusion and, in general, are used to detect and analyze inbound communications only. Thus, they lack protection against the insider threat. Furthermore, depending on their architecture, they either analyze network packets or host information. Hence, the scope and context they analyze is limited. Advanced threats, also known as advanced persistent threats (APT) [52], are considered to be highly sophisticated, possibly exploiting zero-days and can be very challenging for a complex and diverse network like the IoT. Therefore, relying on a single analysis technique or method may be insufficient to address the threat landscape faced. We should have appropriate multiple detection capabilities as a second line of defense, which should consider both the outsider and insider threat.  
An in-/out-bound activity may trigger a number of events. If we focus only on the activity type, the chances are that we may omit critical events necessary for rigorous analysis. EDAS ensures event-driven analysis where events from multiple activities, irrespective of their type, can be investigated during event correlation. Furthermore, we suggested that any CEP-compatible methods and tools can be utilized, including the traditional solutions, as they may provide various security alerts (events) as an input. Hence, combining different analysis techniques and tools, such as rule-based, profiling and resource reputation measures, statistical methods, behavior analysis, _etc._, to collectively analyze a situation may appropriately address advanced threats. Lastly, if the events handled in EDAS are adequately logged and managed, they can be utilized as key learning resources for enhancing the underlying analysis techniques.