esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Models Overview
#### 3.7.7 Long Short-Term Memory (LSTM)  
Long Short-Term Memory (LSTM) networks [38] are a specialized form of Recurrent Neural Networks (RNNs) designed to handle sequential data and capture long-term dependencies. This makes LSTMs highly suitable for HAR, where temporal patterns are crucial.  
In this study, a multi-layer LSTM network was implemented to model the sequential patterns in sensor data, optimizing key parameters such as the number of LSTM units and learning rate for effective classification performance. The core of the LSTM consists of gates--forget, input, and output--that control information flow, allowing the network to manage both short-term and long-term dependencies in the data. These gates interact with the cell state and the network's memory, to retain relevant information and discard what's unnecessary over time.  
As depicted in Figure 7, the forget gate discards irrelevant information, the input gate allows new data to be added, and the output gate determines what is passed to the next layer. These mechanisms enable LSTM to retain useful patterns for activity recognition, selectively managing both short-term and long-term dependencies.  
The LSTM's core equations are expressed as:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}), \tag{21}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}), \tag{22}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}), \tag{23}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}), \tag{24}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}), \tag{25}\]  
where \(f_{t}\), \(i_{t}\), and \(o_{t}\) represent the activations of the forget, input, and output gates, respectively, while \(c_{t}\) represents the cell state update. These gates use the sigmoid function \(\sigma\), while cell updates rely on element-wise operations \(\odot\), and the previous hidden state \(h_{t-1}\) and input \(x_{t}\) influence the gating mechanisms.  
Figure 6: Architecture of a Gated Recurrent Unit (GRU) Network used in Activity Recognition. Adapted from [37], showing the flow through the reset and update gates, facilitating efficient sequential data processing.  
The LSTM's hyperparameters were optimized using a nested cross-validation process, as shown in Table 13. For the two-sensor, non-overlapping configuration, two LSTM layers with 32 units and a dropout rate of 0.30 performed best, while the 50%-overlapping setup used one LSTM layer with 128 units. The four-sensor non-overlapping configuration similarly required one LSTM layer, whereas the 50%-overlapping setup needed a more complex architecture with three layers.  
The LSTM's ability to capture both short-term and long-term dependencies was key to improving classification accuracy across different sensor configurations. By fine-tuning the hyperparameters, the model was able to effectively differentiate between various human activities, achieving robust performance through careful management of sequential data.
## 3 Results and Discussion
### Verification of the Fast Calculation Model with PSF Convolution
#### 3.3.3 Results Verification of Uplink Transmission Scenario Results
Figure 14 shows the change curve comparison between the fast calculation results and the 4D wave optics simulation results of \(Enc_{axis}\) and \(Enc_{peak}\) with facula expansion multiple \(\beta\) under S8, S9, and S10 scenarios. Similarly, when the thermal effect is weak, the fast calculation results are in good agreement with the 4D wave optics simulation results. With the increase of the thermal distortion parameter, the difference in \(Enc_{axis}\) increases, but the difference in \(Enc_{peak}\) is not significant. Figure 15 gives the relative error between the fast calculation results and the 4D wave optics simulation results for \(Enc_{axis}\) and \(Enc_{peak}\). For \(\beta=5,10\), the errors of \(Enc_{axis}\) are within \(-60\)\(\sim\)\(-20\%\), and the errors of \(Enc_{peak}\) are within \(-10\)-\(5\%\). For \(\beta=15\), the errors in \(Enc_{axis}\) and \(Enc_{peak}\) are all negligible. Figure 13: 4D wave optics simulation results **(a-c)** and the far-field intensity distribution for fast calculation results **(d–f)** with S8, S9, and S10 scenario pairs presented top to bottom, respectively.
# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
## 3 Materials and Methods
### Software Functionality
#### 3.2.1 Transmitter Process  
As shown in Figure 3, bits are generated and the information maps a vector of bits. These bits are modulated by utilizing the modulation technique quadrature phase-shift keying (QPSK). The applied signal can be either integers or bits. The width must be an integer multiple of the incoming bits per symbol when setting the 'Input type' parameter to 'Bit.' The De-mux block divides vector signals into smaller vectors or scalars, and these are utilized for the series to parallel transformation. After that, nulls and DC subcarriers are added to create a 64-point OFDM signal, to transform complex frequency domain signals into time-domain by utilizing the IFFT block, and to implement orthogonality between the subcarriers. At the end, the cyclic prefix (CP) is inserted to prevent inter-symbol interference (ISI) and then it is sent to up-sample and DAC in the USRP hardware block.  
Figure 3: Transmitter operation for generating Orthogonal Frequency Division Multiplexing (OFDM) signal.  
Figure 2: Simulink model.
## 3 Data
### Climate data  
10-day maximum, mean and minimum temperature, air humidity and the cloud cover of sky data for 2004 at nine climate stations located in the study area were obtained from the National Hydro-Meteorological Centre of Kazakhstan. Gridded maps of 10-day values for each variable were constructed using the interpolation method kriging with external drift (KED) where a digital elevation model, scaled in meters, was used as an external drift. In order to assess the accuracy of the data preparation, we randomly removed three weather stations from the interpolation for one of the 10-day from the recorded values of mean temperature. Average error resulted in a value less than 6%. It means that the KED approach worked very effectively.
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### QoS Metrics
#### 3.3.3 Packet Loss Rate  
The vehicle packet loss rate is the ratio between the number of lost packets and the total of packets sent or received during a period of time. For a given vehicle \(u\), the packets can be either received correctly or received but dropped. Packets can be sent correctly or dropped while sending.  
Many reasons cause packets drop such as errors, discarding, packet's lifetime expiration, congestion or drop from the queue. The packet loss rate \(PL(u)\) of a vehicle \(u\) is calculated using Equation (7) proposed in [50]:  
\[PL(u)=\frac{(PKTs_{rd}+PKTs_{sd})}{(PKTs_{rc}+PKTs_{sc}+PKTs_{rd})} \tag{7}\]  
where \(PKTs_{rc}\) is the number of packets received correctly. \(PKTs_{rd}\) is the number of packets received but dropped. \(PKTs_{sc}\) is the number of packets sent correctly and \(PKTs_{sd}\) is the number of packets sent but dropped.
## 3 Results and Discussion
### XRD Analysis  
The XRD spectrum of the fabricated Cu-SiC-Gr and Cu-WC-Gr hybrid composites are shown in Figures 7 and 8, respectively. The phase structure examination of the hybrid composites was conducted at a diffraction angle of 20\({}^{\circ}\) to 90\({}^{\circ}\) by the x-ray diffraction. Major diffraction peaks of Cu were found in the base alloy as well as the composites. The peaks corresponding to the 20 angle of 34.4\({}^{\circ}\), 50.5\({}^{\circ}\), and 74.1\({}^{\circ}\) refer to the copper peaks, and the lattice planes are (1 1 1), (2 0 0), and (2 2 0), respectively. Similarly, clean peaks of SiC were found in Figure 5, whereas the peaks of WC can also be observed in Figure 6. The very small peak of graphite can be visible in both the hybrid composites due to the small intensity as compared with the Cu peaks. The XRD pattern shows that there are no intermediate reactions between Cu with SiC or WC and graphite that can be confirmed from the spectrum, which shows a successful casting process. The absence of any undesirable intermetallic reaction products in the composites was a good sign for the hybrid composites, which otherwise would have adversely affected the properties. [PERSON] et al. [22] fabricated nano-MMCs of Cu-reinforced in-situ TiC and carbon particles, and they reported only diffraction peaks related to TiC and Cu without any other intermetallic compounds. Moreover, the careful analysis revealed that the intensity of the peaks in the case of the composites was changed with respect to the base alloy, which can be  
Figure 6: FESEM micrograph and EDX analysis of Cu-SiC-Gr hybrid composites with WC variation of (**a**) 3, (**b**) 6, and (**c**) 9 wt.%.  
associated with the reinforcement content for both the hybrid composites. Further, the major diffraction peaks of the copper elements in the hybrid composites were shifted towards a higher two-theta angle compared to that of the base matrix, which also facilitated the incorporation of the ceramic reinforcements.
## 3 Results
The results show that the proposed model improves the utilization of temporal, spectral, and spatial information of EEG signals, and has a better effect on the classification of epileptic EEG signals. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Preictal** & **Interictal** \\ \hline Subject No. & 21 & 21 \\ No. of channels & 18 & 18 \\ No. of spectrograms & 312,552 & 329,437 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The summary of the number of spectrograms for preictal and interictal periods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Patient**} & \multirow{2}{*}{ \begin{tabular}{c} **No. of** \\ **Seizures** \\ \end{tabular}  } & \multicolumn{3}{c}{**GTN [FOOTNOTE:]Footnote 1: GTN, gated transformer networks; 2 Sen, sensitivity; 3 Spec, specificity; 4 Pre, precision. [ENDFOOTNOTE]**} & \multicolumn{3}{c}{**Our Model**} \\ \cline{3-7}  & & **Sen 2 (\%)** & **Spec 3 (\%)** & **Pre 4 (\%)** & **Sen 2 (\%)** & **Spec 3 (\%)** & **Pre 4 (\%)** \\ \hline Chb01 & 7 & 98.62 & 98.99 & 98.96 & 100 & 99.8  
The classification results were subsequently post-processed to obtain the prediction results from our seizure prediction model. The FPR classified for our network alone and after post-processing is shown in Table 4. It can be seen that after post-processing, our FPR decreased, on average, from 0.19/h to 0.047/h. This effectively eliminated sporadic errors from the process of model classification and improved the performance of our epileptic seizure prediction system so as to better complete the prediction task. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Patient**} & \multirow{2}{*}{**No. of Seizures**} & \multicolumn{2}{c}{**Without**} & **After** \\  & & **Post-Processing** & **Post-Processing** \\ \cline{3-4}  & & **FPR 1 (h\({}^{-1}\))** & **FPR 1 (h\({}^{-1}\))** \\ \hline Chb01 & 7 & 0 & 0 \\ Chb02 & 3 & 0.07 & 0.01 \\ Chb03 & 7 & 0.3 & 0.07 \\ Chb04 & 4 & 0.9 & 0.22 \\ Chb05 & 5 & 0.02 & 0 \\ Chb06 & 10 & 0.52 & 0.13 \\ Chb07 & 3 & 0.3 & 0.07 \\ Chb08 & 5 & 0.02 & 0 \\ Chb09 & 4 & 0.3 & 0.08 \\ Chb10 & 7 & 0.1 & 0.02 \\ Chb11 & 3 & 0.1 & 0.02 \\ Chb14 & 6 & 0 & 0 \\ Chb15 & 13 & 0.05 & 0 \\ Chb16 & 5 & 0.1 & 0.03 \\ Chb17 & 3 & 0.07 & 0.02 \\ Chb18 & 5 & 0.02 & 0 \\ Chb19 & 3 & 0.3 & 0.08 \\ Chb20 & 6 & 0.1 & 0.03 \\ Chb21 & 4 & 0.45 & 0.11 \\ Chb22 & 3 & 0.1 & 0.03 \\ Chb23 & 5 & 0.3 & 0.07 \\ Average & - & 0.19 & 0.047 \\ \hline \hline \end{tabular} \({}^{\dagger}\) FPR: False Prediction Rate.  
\end{table} 
Table 4: The comparison between the performance of the GTN and our model. Figure 6: The accuracy and the \(F1\) score of the GTN and our model for 21 patients.
## 3 Results
### Unconfined Compressive Strength
#### 3.1.3 Effect of Curing Age at Ambient Temperature  
Figure 5 presents the unconfined compressive strength after 7, 14, 28, 56 and 90 days of curing GGBFS activated with 15 M NaOH solution at a liquid to solid ratio of 15%. It is observed that the curing period plays a vital role in UCS development. A linear relationship is found to exist between the development of unconfined compressive strength and the curing period. From 7 to 90 days curing period there is a 27% increase in UCS. [PERSON] et al. [39] reported similar findings. This is possibly due to increasing the curing period extent of dissolution of aluminosilicate species and an acceleration of the formation of a hardened specimen particularly in the initial stage of the alkaline activation process. There was a balance between Si, Al and Na contents that allowed unreacted GGBFS particles to react with the alkaline solution with increasing curing period [40]. The stable reaction products formed with extended curing resulted in denser microstructure with better mechanical performance and improved UCS [40; 41]. In addition, the SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar ratio may be within the required threshold causing the higher amounts of soluble silica (Si-O) and Na2O. It is noticeable a large portion compressive strength is gained within 56 days. After 56 days, the statistical ANOVA test shown in Table 3 reveals that the UCS gained within 90 days is insignificantly different compared to the UCS gained within 56 days. This may have resulted in rapid generation of reaction products with time allowed for the unreacted GGBFS to further react with the NaOH solution attributed to an almost complete alkaline activation process at 90 days curing period.  
Figure 4: Effect of liquid-solid ratio on UCS.
## 5 Challenges and Future Directions
### Data and Sensor Capabilities  
To support efficient anomaly detection, urban mapping, and other environmental applications, researchers should focus on implementing new sensing devices, specialized cameras, multi-criteria decision algorithms, multispectral imagery, edge/fog computing coexistence [225], and remote sensing and positioning mechanisms for UAVs. Advancing drone-based deforestation mapping, for instance, requires integrating emerging technologies, such as multi-sensor systems, hyper/multispectral cameras, distributed computing architectures, and high-precision geospatial tracking. These capabilities can enable the advanced detection of various tree species, biodiversity richness, and urban ecosystem characteristics by aerial UAV platforms. Another promising area for UAV applications in climate change research is data filtering. This technique can be implemented in UAVs to prevent redundant data, limit duplicates, block illegal access, and filter false locations. UAVs can be integrated with emerging intelligent reflecting surfaces (IRSs) to enhance the positioning, localization, and sensing (PLS) performances of IRS-assisted UAV systems in diverse scenarios while maintaining computational complexity and overall system capabilities [226]. Embedding data filtering in UAVs can help avoid redundant data and false location information. Integrating UAVs with intelligent reflecting surfaces can also boost navigation and sensing abilities in complex urban environments without increasing system complexity.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 4 Materials and Methods
### Modeling Choices
#### 4.1.2 Signal Progression through Obstacles  
To model indoor signal propagation, the ITU model, proposed by the International Telecommunication Union, described in [108], was modified through the addition of the \((w\times c)\) term to account for attenuation caused by the internal walls/obstacles to the following form:  
\[PL[dB]=20 log_{10}(f)+28 log_{10}(d)-28+(w\times c) \tag{5}\]  
where \(w=4.4349\) is the wall attenuation factor (for brick wall) [109], \(c\) is the wall count encountered, \(f=2400\) MHz is the frequency channel of communication for standard Wi-Fi, and \(d\) is the distance, in meters, from the beacon source [108]. Figure 3 shows the signal propagation with a corner-located beacon.  
In real-life situations, a radio wave is usually able to propagate through obstacles (depending on the thickness and material of the obstacle), but the signal strength significantly reduces due to a phenomenon called _path loss_, for which we use the ITU model to model an indoor beacon propagation. This path loss can, in effect, be simulated by artificially lengthening a link across a wall. Accordingly, Equation (5) is rewritten to calculate the re-interpreted distance between the agents as:  
\[Distance(m)=10^{(\frac{PL+28-20\omega_{210}(f)}{20})} \tag{6}\]  
This value is then fed into the _Cost Matrix_ (introduced in a simplified form in Section 3.4.1). Due to this, the _Cost Matrix_ represents the new weight of the edge between two nodes and the Dijkstra algorithm, as we already described, is run to find the shortest path from the target back to the source, with the possibility that the signal will penetrate the walls. A visual representation of how walls can affect signal strength is shown in Figure 3; the impact of allowing sufficiently strong signals into the path even though the signal goes through a wall can be seen in Figure 10, where the top version does not include signals that pass through walls, while the bottom does.
## 3 Results and Discussions
### Color  
The final color of a product launched on the market is an important attribute because it conditions the choice of the consumer. One of the most widely used tools to define the color of a beverage is using the coordinates of CIELab space. Figure 2 shows the resulting color of the different PFVBs. As can be seen, these presented the final colors according to the composition of the raw materials of each PFVB. In particular, in the case of the PFVB1 and PFVB2 beverages, the resulting colors were yellowish and orange, respectively, since the CIELab space coordinates were set to negative a* and positive b* values in the case of PFVB1 and positive a* and b* values for PFVB2. The colors of these probiotic beverages may be due to carotenoids and phenolic compounds, present in pineapple, apple, grape, carrot, orange, mango, and ginger [57; 58; 59; 60; 61; 62; 63]. In the case of the PFVB4 beverage, it was also included in the same quadrant of CIELab space of PFVB1, but it showed a greenish color, defined by higher values of the a* and b* coordinates. This color could be a consequence of the chlorophylls in spinach [64] and cucumber [65] and the phenolic pigments and carotenoids in the fruits and vegetables used in this beverage. On the other hand, the color of the PFVB3 beverage was red with bluish tones (positive a* and negative b* coordinates of CIELab space). This color is probably due to anthocyanins present in red fruits [66] and carotenoids and betalains from beet [67]. Regarding lightness, all of the vegetable probiotic beverages exhibited L values exceeding 65. This places them in the upper region of the CIELab color space, suggesting that the resulting beverages are light and bright from a sensory point of view.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **PFVB1** & **PFVB2** & **PFVB3** & **PFVB4** \\ \hline Sodium (g) & 0.012 \(\pm\) 0.0 & 0.044 \(\pm\) 0.0 & 0.001 \(\pm\) 0.0 & 0.033 \(\pm\) 0.0 \\ Protein (g) & 0.810 \(\pm\) 0.021 & 0.704 \(\pm\) 0.011 & 1.02 \(\pm\) 0.04 & 1.04 \(\pm\) 0.02 \\ Sugar (g) & 1.60 \(\pm\) 0.11 & 4.23 \(\pm\) 0.15 & 1.81 \(\pm\) 0.03 & 1.41 \(\pm\) 0.02 \\ Carbohydrates (g) & 10.0 \(\pm\) 0.4 & 10.4 \(\pm\) 0.2 & 12.0 \(\pm\) 0.2 & 11.4 \(\pm\) 0.3 \\ Fat (g) & 3.31 \(\pm\) 0.34 & 0.222 \(\pm\) 0.001 & 0.103 \(\pm\) 0.001 & 0.502 \(\pm\) 0.014 \\ Saturated fatty acid (g) & -0.01 & -0.01 & \textless{}0.01 & \textless{}0.01 \\ Total energy (kJ) & 310 \(\pm\) 6 & 196 \(\pm\) 4 & 213 \(\pm\) 1 & 222 \(\pm\) 6 \\ Total energy (kcal) & 74 \(\pm\) 3 & 47 \(\pm\) 1 & 51 \(\pm\) 3 & 52 \(\pm\) 2 \\ \hline \end{tabular}
\end{table}
Table 3: Nutritional information of 100 g of the different PFVBs.
###### Abstract  
The global maritime digitalization reflects on navigation and paperless vessels with Paper Navigational Charts (PNC) nowadays superseded by Electronic Chart Display and Information System (ECDIS). Considering the system implementation and its acceptance as a sole navigational means, opinions of navigators differ. Although the ECDIS mandatory implementation ended in 2018, some navigators have been still favouring PNCs, pointing out their advantages over ECDIS navigation. These standpoints may have an impact on the safety of navigation in terms of acceptance, interpretation, and understanding of the system as well as on conflict of standpoints of decisive navigational ranks, the latter reason being found as one of the real problems. The presented study has focused on a specific period, soon after the transitional period completion, aiming to determine the views of traditional navigation advocates, their arguments in the present maritime navigation paperless era and to identify potential problems emerging from the conflict of two navigational means. The research has induced two independent, internationally distributed questionnaires, dedicated to navigational ranks. The first survey has referred to the period from 2012 to 2018, marking the transition to ECDIS navigation. The second survey was conducted after the implementation period completion date. The answers were analysed and discussed from the navigational ranks' perspective, considering their competitiveness and the level of ECDIS education. The research results have indicated and confirmed that PNCs could not entirely be ruled out, at least at this stage. Besides definitive questionnaire answers, the findings have been supported with categorised comments as interpreted from the first survey questionnaire results. The paper aims to present the future of the PNCs, including possibilities of fusion with modern means. The proposed suggestions have been directed towards the benefits of maritime navigation safety, referring especially to disagreement between navigational ranks in terms of particular means acceptance.  
maritime navigation; electronic chart display and information system; paper navigational charts; electronic navigational charts; ECDIS EHO
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 2 The Instantaneous (Shannon) Spectral Entropy
### Shannon Spectral Entropy  
The general term spectral entropy (SE) refers to any measure of the uniformity of a signal spectral power distribution. The definition of Shannon SE originates from the works of [PERSON] [21], based on the measure of uncertainty proposed by [PERSON] in [36]. Specifically, the SSE formula can be considered as the limit form of the generalised Renyi entropy for \(\alpha\to 1\)[37]. Specifically, for a given probability distribution in the frequency domain \(P(f)\), the SSE can be defined as  
\[\text{SSE}=-\sum_{f}^{B}P(f)\log_{10}P(f), \tag{1}\]  
where \(B\) is the total number of discrete frequencies, i.e., the bins of the distribution. Please note that here, the base 10 logarithm was applied; however, any other base can be used, without major conceptual differences. Equation (1) can be normalised by dividing it by \(\log_{10}B\); nevertheless, for the sake of this research, the standard (non-normalised) definition of Equation (1) has been preferred.  
For a discretised power spectrum \(|H(f)|^{2}\), where \(H\) indicates the Discrete Fourier Transform of a general signal \(h(t)\), the probability distribution can be written as  
\[P(f)=\frac{|H(f)|^{2}}{\sum_{f}^{B}|H(f)|^{2}}. \tag{2}\]  
This can be then extended to include the time dependency.