esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Recent Temporal Modeling Approaches (2020-2025)  
To further contextualize the performance of the proposed MHMM, a detailed comparative summary of recent temporal modeling approaches applied to human state evaluation is presented in Table 6. This table highlights their data modalities, methodologies, reported accuracies, and main limitations, providing a broader perspective on the current landscape of research.  
While recent temporal modeling approaches demonstrate promising results across diverse domains (from gaming and driving to pilot monitoring) they often rely on unimodal data, lack longitudinal proficiency tracking, or omit subjective context. As summarized in Table 6, many models either prioritize short-term states (e.g., fatigue, vigilance) or are validated on narrow, domain-specific tasks. In contrast, the proposed MHMM integrates physiological, behavioral, and subjective modalities within a three-state interpretable framework, achieving high accuracy (92.5%) and aligning with cognitive science theories. Despite the current limitation of relying on simulation-based validation, the MHMM offers strong potential for real-time, generalizable proficiency tracking in complex industrial environments. Its modular design and multimodal integration position it as a promising foundation for future adaptive and human-centered AI systems in Industry 5.0.
## 4 Experiments and Evaluation
### Real Scenario
#### 4.1.4 Analysis of Location of Additional Sources Procedure  
In the case of the scenario of basement underperformance, this procedure was applied by querying the Brick model for all of its components that are measured by the basement light meter, yielding the successful discovery of the green wall and outdoor lighting.
## 2 Methods
### Bayesian image restoration  
The central task in Bayesian image restoration is to select the best statistical estimator \(\tilde{\mathbf{f}}\) of an image \(\mathbf{f}\), assuming that both the image \(\mathbf{f}\) and the data \(\mathbf{g}\) are outcomes of random vectors given in the form of probability distribution. The incomplete knowledge about the errors in measurements and modeling are included in a noise random vector \(\mathbf{e}\), also expressed as probability distribution. Likewise, a prior probability has to be defined that reflects the uncertainty or partial knowledge on the image \(\mathbf{f}\). In the linear case \(\mathbf{g}=\mathbf{R}\mathbf{f}+\mathbf{e}\), where the \(N\)-dimensional image vector \(\mathbf{f}\) consists of the pixel values of a latent image, the \(M\)-dimensional data vector \(\mathbf{g}\) consists of the pixel values of an observed image supposed to be a degraded version of \(\mathbf{f}\), the matrix \(\mathbf{R}\) is the PSF of the imaging system, and the errors \(\mathbf{e}\) are assumed additive and zero-mean Gaussian. As such, \(\mathbf{e}=k\mathbf{\sigma}\), which entails:  
\[\mathbf{g}=\mathbf{R}\mathbf{f}+k\mathbf{\sigma} \tag{26}\]  
where \(\mathbf{\sigma}\) is the noise standard deviation and \(k\) is drawn from the standard normal distribution:  
\[P(k)=(2\pi)^{-\frac{1}{2}}\exp\left(-\frac{k^{2}}{2}\right) \tag{27}\]  
Likelihood  
After \(M\) measurements, the likelihood of the data \(\mathbf{g}\) results:  
\[P\left(\mathbf{g}|\mathbf{f},\mathbf{C},H\right)=\frac{1}{(2\pi)^{\frac{M}{2}}\cdot \det^{\frac{1}{2}}\mathbf{C}}\cdot\exp\left[-\frac{1}{2}(\mathbf{g}-\mathbf{R}\bm {f})^{T}\mathbf{C}^{-1}(\mathbf{g}-\mathbf{R}\mathbf{f})\right] \tag{28}\]  
where \(\mathbf{C}\) is the covariance matrix of the error vector \(\mathbf{e}\). If the pixels are uncorrelated and each pixel has the standard deviation \(\sigma_{m},\,m=1,2,...,M\), then the symmetric full rank covariance matrix \(\mathbf{C}\) becomes diagonal with elements \(C_{mm}=\sigma_{m}^{2},\,m=1,2,...,M\), that is, \(\mathbf{C}=\sigma_{m}^{2}\mathbf{I}\). Hence the probability of the data \(\mathbf{g}\) given the image \(\mathbf{f}\) can be written as:  
\[P\left(\mathbf{g}|\mathbf{f},\mathbf{C},H\right)=\frac{1}{(2\pi)^{\frac{M}{2}}\cdot \prod_{m=1}^{M}\sigma_{m}}\cdot\exp\left[-\frac{1}{2}\sum_{m=1}^{M}\frac{ \left(g_{m}-\sum_{n=1}^{N}R_{mn}f_{n}\right)^{2}}{\sigma_{m}^{2}}\right] \tag{29}\]  
The argument of the exponential function amounts to \(-\frac{1}{2}\chi^{2}(\mathbf{f})\) according to eq. (8), which gives a more compact relationship:  
\[P\left(\mathbf{g}|\mathbf{f},\mathbf{C},H\right)=\frac{1}{(2\pi)^{\frac{M}{2}}\cdot \prod_{m=1}^{M}\sigma_{m}}\cdot\exp\left[-\frac{1}{2}\chi^{2}(\mathbf{f})\right] \tag{30}\]  
The Poisson distribution is often better modeling the fluctuations in data acquisition rather than the Gaussian distribution assumed here. Nevertheless, the choice depends on the statistical characteristics of the noise assumed _a priori_ known.
## 3 Results and Discussion
The main cause of the increase in charge transport resistance (R\({}_{\text{ct}}\)) was that the lithium, which was plated with incessant concentration polarization, continuously reacted with the electrolyte under overvoltage to form SEI and consumed the electrolyte and lithium ions at the same time [15]. Furthermore, as the cycles repeated, the electrolyte resistance (R\({}_{\text{S}}\)) gradually increased, and the overall curve shape was shifting to the right, which is believed to be a combination of electrolyte decomposition, aging, and electrode and separator deterioration due to repeated charging and discharging [15; 16]. In contrast, in the case of the battery to which 6C pulse current charging was applied, the results show nominally small increases of the electrolyte resistance (R\({}_{\text{s}}\)) and the charge transport resistance all the way up to 400 cycles, and these results clearly prove that the high C-rate pulse current charging method facilitated nice and smooth lithium insertion and diffusion into the graphite anode [17] under the testing condition. In other words, even though the high current level of 16.8 A was applied to the battery for 0.5 s, it was less damaged because there was a resting period of 0.5 s for relaxed lithium insertion and diffusion into the anode. Similar observations were reported by other researchers as well, while the viewpoints were somewhat different among each other. In the study by [PERSON], [PERSON] et al. [12], SEM images of cathodes and anodes from all tested cells were taken, and the comparison showed that the cathodes were all in similar conditions regardless of charging profiles, whether conventional CC-CV or pulse charging, while the anodes showed significantly less SEI formation for the case of the pulse charging profiles. These results are nicely in line with the current study of 1 Hz pulse charging strategy, which specifically targeted minimal SEI formation during the charging. In addition, [PERSON], [PERSON] et al. [18] compared multi-stage constant current and constant voltage (MCC-CV), conventional CC-CV, and pulse charging profiles. They concluded that the battery deterioration was severe for the case of CC-CV, while the rate of capacity decrease was minor for the cases of MCC-CV and the pulse charging and somewhat similar between them. However, they also reported that the lower rate of impedance rise was a clear characteristic of the pulse charging case compared to the case of MCC-CV, which indicated the better anti-aging performance of the pulse charging strategy. Similar results has also been reported in other studies [18; 19; 20; 21].
# Possibilities of Powering Military Equipment Based on Renewable Energy Sources
## 2 Literature Review
### The Use of Photovoltaic Cells
The system includes the following: a power distribution unit (Hub), two battery charging stations for Motorola and Harris radios, a GPS DAGR charging cable, power sources in the form of Zinc-Air batteries with a voltage of 12 V and a capacity of 24 Ah (whose main advantage is the ability to work in a temperature range between \(-20\)\({}^{\circ}\)C and \(+60\)\({}^{\circ}\)C, as well as providing resistance to mechanical damage including bullet holes) [19]. Instead of Zinc-Air batteries, it is possible to use lithium batteries or a flexible PV panel [19]. In Poland, research on the use of PV panels in military applications has been initiated at the Military Institute of Engineer Technology (WITI--Military Institute of Engineer Technology, Obornicka 136 Str., 50-961, Wroclaw, Poland) [1; 8]. As a part of this research, PV panels prototypes were designed and manufactured. There were portable for a single soldier and transportable for powering devices (e.g., communications, electronics) that are equipped with combat vehicles [31]. Greece has also been involved in the development of power systems, and its troops are equipped with a photovoltaic power plant by the Intracom Defense Electronic-IDE company. It is an autonomous, hybrid power system consisting of six PV panels with a total power of 3 kWp placed in transport boxes and a generator with a power of 20 kW. The set was designed to power Greek military units located in places with no access to power grids [27; 28].
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 3 RMCSat Mission
### Mission Concept  
Solar density flux measurements are captured by RMCSat during each orbit as the Sun passes into the boresight angle of the primary payload antenna. The microwave frequencies of 2695 MHz, 2785 MHz, 2815 MHz are simultaneously recorded by the primary and secondary payload antennas. In order to maximize the data collection opportunities for educational and research purposes, the antennas continue to operate throughout the orbit. The low power requirements of the payload allow data collection to continue in eclipse.  
Figure 7: (**a**) The satellite alignment for a noon crossing orbit. The direction of satellite travel is on the page. (**b**) Simulation of the effect of a 15\({}^{\circ}\) tilt required to maintain solar alignment for an SSO one hour offset from noon. The dashed line represents local noon, the green beam is indicative of the effect on a secondary antenna.  
The data collected during the 15 daily orbits are stored and then downlinked to a ground station in Kingston, Ontario, Canada during one daylight contact each day. The ground station is operated by RMC where the mission team is located. A concept diagram of the mission is given in Figure 8.  
The design orbit for the mission is a noon-midnight SSO at 550 km with a 98.0\({}^{\circ}\) inclination and a 95.6 min orbital period. The noon-midnight aspect allows for minimal atmospheric attenuation when making solar observations, embedded operational instrument calibration for solar measurements, and a fixed nadir orientation to Earth of the satellite itself. Power calculations in Section 4 indicate that the limited solar exposure of such an orbit is sufficient to power the mission adequately. The proposed payload operation cycle shown in Table 4 is based on a descending noon-midnight SSO. The cycle commences at the equator in the eclipse.
# TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space
## 3 Proposed Framework
### System Functionality  
Before going to the detailed implementation of the proposed framework, in this section, we discuss the system functionality of the different components discussed above.  
Figure 2: A simple outline of the proposed ‘TrackInk’ framework and its major components.  
The processing unit is the main computational device. It reads the user input, satellite data, and user input controller. It uses the user input to determine whether the system should be automated or run with a manual control. If the system is in automated control mode, it will use the satellite data and sensor data to decide which object to follow and calculate the corresponding servo positions. Information about the system, e.g., geographical location and local satellite altitude and azimuth are then sent to the visualisation server from the processing unit.  
In the framework, multiple sensors are used to measure information about the system. More specifically, these sensors will be used to determine the exact geographical location of the apparatus, along with its rotation relative to Earth. These sensor readings will then be fed back to the processing unit for further use.  
An input controller is used as an interface between the user and system. This will allow the user to control the state of the system, toggling between automated and manual modes. When in manual mode, the user will be able to directly control the direction of the servos. User input is ignored when the system is in automated mode, other than switching operating modes.  
Satellite and celestial body data is essential for this system to work due to the fact that orbiting bodies do not follow a perfect orbit. This live data is acquired from a publicly accessible server hosting a database on TLE satellite data and celestial body data. This data is accessed and interpreted by the processing unit.  
Servo motors are used to actuate the system. The purpose of this component is to move the system to point at the targeted object. A position is sent from the processing to these devices to move the system to the correct position. A visualisation server is used to visualise parameters of the system. This component is used to display plots of how the system operates and changes modes of operation over time. This both provides useful information to debug the system but is also used to show how satellite positions change over time, e.g., seeing their latitudinal sinusoidal paths due to their circular orbits.
###### Abstract  
In this paper, we propose a covert underwater acoustic communication method that is robust to fading using a chirp signal combined with a frequency-hopping spread spectrum scheme. A fractional Fourier transform, which estimates the slope of the signal frequency variation, is applied to the receiver to enable a robust and reliable symbol estimation with respect to the frequency and irregular phase variations. In addition, since the recursive symbol synchronization can be implemented using a chirp signal, compression and expansion effects due to the Doppler shift can be mitigated. Simulation and lake trials were performed to verify the performance of the proposed method. The simulation was performed by two different methods.  
covert underwater acoustic communication; frequency-hopping spread spectrum; symbol synchronization; Doppler fading; fractional Fourier transform; underwater channel simulator +
Footnote †: journal: Accepted: 11 December 2018;
## 3 State-of-the-Art for WirelessHART Routing Schemes
### The Joint Routing Algorithm for Maximizing Network Lifetime  
A joint routing algorithm for maximizing network lifetime (JRMNL) was proposed in [62]. It is based on the WirelessHART graph routing topology and defines a link cost function for choosing the best next hop node in order to achieve the objective of the maximum network lifetime. Before applying the cost function, the algorithm assigns degrees to the nodes, based on their hop distance to the gateway (the degree assigned to the gateway is zero). Then, the links between nodes of the same degree are deleted, as can be seen in Figure 8.  
The cost function for the link between the \(i\)-th and \(j\)-th nodes is defined in Equation (2) and is based on the communication load factor of the node (\(B_{j1}\)), the energy consumption for that single transmission  
Figure 8: Topology adaptation for the joint routing algorithm for maximizing network lifetime (JRMNL).  
from node \(i\) to node \(j\) (\(P_{d}(i,j)\)) and the initial and residual energy of the destination (\(E_{o}\) and \(E_{kj}\), respectively). The \(\omega_{i}\) represent the collection of neighbors of the \(i\)-th node. The \(x_{1}\), \(x_{2}\) and \(x_{3}\) are the positive integers that weight the priority of each specific characteristic.  
\[L_{i\to j}=min_{l\in\omega_{i}}((B_{j1})^{x_{1}}P_{d}(i,j)^{x_{2}}( \frac{E_{0}}{E_{k}j})^{x_{3}}) \tag{2}\]  
The communication load factor is defined in [62] and is based on the average transmission interval of each node and its neighbors. The \(P_{d}(i,j)\) is defined by Equation (3), where \(\alpha\) is the path loss exponent, \(d_{ij}\) is the linear distance between nodes \(i\) and \(j\), \(R_{0}\) is the transmission rate and \(P_{0}^{s}\) is the probability of successful transmission. The noise terms are set based on complex zero mean Gaussian random variables with variance \(N_{0}\). This equation was developed by [63].  
\[P_{d}(i,j)=\frac{(2^{R_{0}}-1)N_{0}d_{ij}^{\alpha}}{-log(P_{0}^{s})} \tag{3}\]  
As can be seen, the cost function increases with \(B_{j1}\) and \(P_{d}(i,j)\), but decreases with \(E_{kj}\). This means that the algorithm's best case for a node is one for which the communication load and transmission power are minimal and the residual energy is maximal.  
The algorithm was validated with a MATLAB(r) simulation and was compared with the ELHFR (Enhanced Least-hop First Routing) [54] and MPCR (Minimum Power Cooperative Routing) [63] algorithms, obtaining a network lifetime increase of seven- and two-times, respectively. For the average energy consumption, JRMNL was about 4 dBm lower than ELHFR and 2 dBm higher than MPCR.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 4 Biomass Pretreatment to Decrease Recalcitrance
### Principal Pretreatments
#### 4.1.2 Alkaline Pretreatment  
Alkaline pretreatment allows a destruction of the lignocellulosic and decreases both the degree of polymerization and the crystallinity of the cellulose. It is effective in extracting lignin and solubilizing a part of the hemicelluloses (commonly >40% _w/w_), while causing negligible degradation of the cellulose (Table 1). During alkaline pretreatment, the first reactions are the dissolution and saponification of the intermolecular ester and ionic bonds that cross the hemicelluloses and other components, reactions that increase the porosity of the lignocellulosic biomass through the disintegration of these bonds. In addition, hydrolysis of glycosidic bonds and acetyl groups may occur, breaking down polysaccharides and enhancing subsequent enzymatic hydrolysis [186; 219]. The most used bases are sodium, potassium, calcium hydroxide and ammonium hydroxide [185; 219]. In general, alkaline pretreatment is more effective on hardwoods, arable crops and agricultural residues with low lignin content than on softwoods with high lignin content [186]. Compared to other pretreatments, it is considered of low cost and its main disadvantage is the formation of salts, which are difficult to remove, and the low yields in monomeric sugar [172; 185]. The digestibility in the solid fraction is frequently more than 70% (Table 1).
## 2 Materials and Methods
### Random Forests Classification, Validation and Variable Contribution  
The accuracy of a classifier entails the probability that it will correctly classify a random set of examples [76], in our case, burned and unburned classes. The classification of burned and unburned classes was performed using the Random Forests (RF; Breiman [77]) algorithm available in the GEE platform. The choice for RF algorithm application in this is influenced by its success and extensive use in burned area valuation studies [54]. RF uses ensemble methods with multiple tree-type classifiers to attain better predictions and it builds a perfect forest of random uncorrelated DTs to achieve the best possible result [77]. The number of trees was set to 30, while the number of predictor variables at each node was set at the square root of the input variables used in the model [78], which is 4 in our case. Certainly, some spectral bands/ratios are more important than others for the classification, and it was established that when more spectral bands are included, the accuracy is improved until a certain threshold is reached [79]. Here, we used 16 input variables, which included Sentinel-2 bands excluding aerosol and water vapor bands displayed in Table 2, NBR, NDVI, NDWI, NDWI1, and texture. The aerosol and water vapor bands were excluded because of their irrelevance to burnt area mapping [80]. We followed [PERSON] and [PERSON] [81] by incorporating a texture index in the classification because it is known to increase accuracy [82], in our case, this was computed from the standard deviation of NBR. The samples were randomly divided into 70% for training the classifier and 30% for validating datasets. The procedure was reiterated 1000 times and the proportion of correctly classified entries in the validation dataset was documented using the overall classification accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spectral Band** & **Band Name** & \multirow{2}{*}{**Wavelength**} & \multicolumn{1}{c}{**Spatial**} \\  & & & **Resolution (m)** \\ \hline B1 & Coastal aerosol & 442.3–443.9 nm & 60 \\ B2 & Blue & 492.1–496.6 nm & 10 \\ B3 & Green & 559.560 nm & 10 \\ B4 & Red & 664.5–665 nm & 10 \\ B5 & Red edge 1 & 703.8–703.9 nm & 20 \\ B6 & Red edge 2 & 739.1–740.2 nm & 20 \\ B7 & Red edge 3 & 779.7–782.5 nm & 20 \\ B8 & NIR & 833–385.1 nm & 10 \\ B8A & Near infrared narrow & 864–864.8 nm & 20 \\ B9 & Water vapor & 943.2–945 nm & 60 \\ B10 & SWIR cirrus & 1376.9–1373.5 nm & 60 \\ B11 & SWIR 1 & 1610.4–1613.7 nm & 20 \\ B12 & SWIR 2 & 2185.7–2202.4 nm & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectral bands of the Sentinel-2 imagery.  
Figure 2: Spatial distribution of samples for burned and unburned areas.  
We also performed the RF variable importance within the GEE environment to find the spectral bands and ratios that mostly contributed to the overall burned area classification. We used the decrease in node impurities measured by the Gini index for this purpose. The variable with the highest Gini index was considered the most important compared to the other input variables in the Sentinel-2 imagery.
## 5 Results and Discussion
### The Structural Model Test
Figure 4a,b depict the two models used to test moderators and Table 9 summarizes the moderating effects results (H7). Figure 4: (**a**) Main effect model; (**b**) interaction effect model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hypothesis** & **Path** & & **Std. \(\beta\)** & **t-Value** & **Result** \\ \hline H1 & Irrelevant Information Overload & & 0.297 & 4.120 & S** \\ H2 & Open Reachability & & 0.095 & 1.015 & NS \\ H3 & Engagement & & 0.323 & 5.129 & S** \\ H4 & Maintaining Self-Reputation & & 0.406 & 5.235 & S** \\ \hline H5 & SNS Fatigue & & Living Disorder & 0.411 & 7.236 & S** \\ \hline H6 & Living Disorder & \(\rightarrow\) &
\begin{tabular}{c} Reduced Intention \\ to Use SNSs \\ \end{tabular} & 0.324 & 4.322 & S** \\ \hline \hline \end{tabular} Note: *: \(p<0.05\), **: \(p<0.01\); S: Supported; NS: Not supported.  
\end{table} 
Table 8: Summary of hypothesis tests (H1 to H6). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hypothesis** & **Construct** & \
\begin{tabular}{c} **Analysis of the** \\ **Main Effect** \\ \end{tabular} &
\begin{tabular}{c} **Analysis of the** \\ **Interaction Effect** \\ \end{tabular} \\ \cline{2-5}
**(Result)** & **Std. \(\beta\)** & **_t-Value_** & **Std. \(\beta\)** & **_t-Value_** \\ \hline \multirow{5}{*}{
\begin{tabular}{c} H 7 \\ (Supported) \\ \end{tabular} } & Living Disorder & 0.410 & 6.884 ** & 0.413 & 7.006 ** \\ \cline{2-5}  & Experience of Privacy Violations & 0.219 & 3.582 ** & 0.241 & 3.668 ** \\ \cline{1-1} \cline{2-5}  & Living Disorder \(\times\) Experience of & & & & \\ \cline{1-1} \cline{2-5}  & Privacy Violations & - & & & \\ \cline{1-1} \cline{2-5}  & R\({}^{2}\) (Reduced Intention to use SNSs) & 0.238 & & & \\ \cline{1-1} \cline{2-5}  & F-value & & & & \\ \hline \hline \end{tabular} Note: *: \(p<0.05\), **: \(p<0.01\)  
\end{table} 
Table 9: Moderating effects results.