esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials
### Anode
Perovskite materials based on strontium titanantes, lanthanum chromite and fluorite materials based on ceria with very low or negligible percentage of metals are the alternative potential candidates for Ni-YSZ conducting anode [35; 72; 78; 79; 80]. The excellent performance is reported in literature with La\({}_{0.75}\)Sr\({}_{0.25}\)Cr\({}_{0.5}\)Mn\({}_{0.5}\)O\({}_{3}\) anode at 900 \({}^{\circ}\)C using humidified hydrogen fuel [81]. No coking is also observed when La\({}_{0.8}\)Sr\({}_{0.2}\)Cr\({}_{0.97}\)V\({}_{0.03}\)O\({}_{3}\) anode is operated continuously 100 h using dry methane fuel [82]. Some cermet anodes also exhibit both electronic and ionic conduction known as mixed ionic electronic conduction (MIEC) and increase the reaction zone [10; 72; 79; 35]. To improve the SOFC performance, development of new materials is in progress such as significant performance (power density \(>\) 800 mW/cm\({}^{2}\)) has been shown by double Perovskite anode Sr\({}_{2}\)Mg\({}_{1-x}\)Mn\({}_{x}\)MoO\({}_{6-\delta}\) (SMMO) in LSGM supported cells [83]. It is also reported that above material has shown a power density of 338 mW/cm\({}^{2}\) with no significant carbon deposition when methane is used as fuel at 800 \({}^{\circ}\)C. Also, a power density greater than 900 mW/cm\({}^{2}\) with H\({}_{2}\) and 600 mW/cm\({}^{2}\) with methane is reported for Pr\({}_{0.4}\)Sr\({}_{0.6}\)Co\({}_{0.2}\)Fe\({}_{0.7}\)Nb\({}_{0.1}\)O\({}_{3-\delta}\), that is another promising material with K\({}_{2}\)NiF\({}_{4}\)-type structure at 800 \({}^{\circ}\)C [84]. The catalytic activity of these anodes can be brought close to that of Ni-YSZ anode but their compatibility with YSZ and CGO electrolytes still needs investigation. The electrical conductivity of cermet anodes is smaller than that of Ni-YSZ anode. This conductivity is dependent upon the fabrication methods and composition of the materials used. By using composite of ceramic and metals the conductivity of low conductive ceramic anodes can be enhanced. The bilayered designs (porous conducting layer on anode) can also enhance current collection and conduction. Table 3 summarizes the best alternative anode materials for Ni-YSZ along with their maximum electronic conductivity and polarization resistance. Detailed information about the high temperature anode materials can be found in the literature [12; 35; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84].
## 3 IAS Capabilities  
The functioning of the IAS is based on the principle of the sequential implementation of the following stages of data processing:  
- Data creation;  
- Data modification;  
- The creation of documents and reports.  
These functions are implemented within the framework of the analytical block, which provides users with a set of tools for:  
- The predictive calculation of the temperature of the materials of the test sections, depending on the specified change in the power of energy release in them;  
- The predictive calculation of the required power of energy release in materials depending on a given change in their temperature;  
- The formation of arrays of experimental information in digital format and graphical form;  
- The comparison of experiments and their data among themselves;  
- The formation of protocols of the experiments with the possibility of choosing specific data and methods for their processing.  
An image of the structure of the analytical block is shown in Figure 3.  
An example of calculating the diagram of the change in the temperature of the materials of the test section depending on the power of energy release in them, performed by means of the developed IAS, is shown in Figure 4.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Work chamber height, mm & 700 \\ \hline Inner diameter of working chamber, mm & 600 \\ \hline Temperature sensors & thermocoouples, pyrometers \\ \hline Generator frequency, kHz & 66 \\ \hline Mass of charged burden, g & 100–200 \\ \hline Work chamber medium & vacuum, argon \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical characteristics of the “VCG-135” test bench.  
Figure 3: Analytical block of IAS.  
The regulation of the temperature shown in Figure 4 occurs due to the power, and vice versa, of the IAS, based on the values of the temperature we need, which allows us to accurately determine the power expended.  
As part of the procedure for comparing experiments and their data, the analytical block of the IAS provides an opportunity to compare the results of studying the macro- and microstructure of materials, the results of energy-dispersive spectral analysis (EDS), scanning the electron microscope (SEM), and X-ray structural analysis. An example of comparing SEM analysis data is shown in Figure 5.  
IAS allows for the displaying of research as the SEM results, as shown in Figure 5, obtained under identical experimental conditions, but with a different corium composition, and for considering the differences that occur in the material.  
One of the main functions of the IAS is the \"Ready Protocol\" function, which allows you to display and print only the data that the user needs.  
Figure 4: Heating diagram using IAS.  
Figure 5: Results of SEM analysis in IAS.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 1 Introduction
### What Is a Diatom and How Can We See It?
The recording of diatom images has evolved since they were observed for the first time in 1703. Since then, drawing, photomicroscopy, and digital microscopy have provided a time-durable record of observations. Currently, besides the direct observation by a microscope, images are captured by digital cameras, with many advantages over previous methods [7; 14]:  
* Quick and simple capture, storage, and reproduction;
* Easy and reproducible ways to edit and enhance images by computer based image processing algorithms such as: erasing artifacts, noise reduction, multifocus, multiexposure fusion, and so on;
* Feasible enrichment of digital images with valuable information such as scale bars and other metadata;
* Processing of digital images by high level algorithms to extract features and obtain useful knowledge from the images (e.g., identification, classification).
## 3 Results and Discussion
### Physicochemical Properties of Sausages
#### 3.1.3 Surface Colour (CIE \(L^{*}a^{*}b^{*}\)) of Pork Sausages  
The fat level did not influence the surface colour of SS sausages. However, the surface redness (\(a^{*}\)) was significantly affected by the salt level (Table 4). Conversely, the lightness (\(L^{*}\)) and yellowness (\(b^{*}\)) values of IW sausages were significantly influenced by the fat and salt levels, and a significant fat \(\times\) salt interaction was observed.  
The \(a^{*}\) values of IW sausages were also influenced by fat level. In comparison to the control, the addition of SS and IW seaweeds to sausages reduced (\(p<0.05\)) the lightness (\(L^{*}\)) and redness (\(a^{*}\)) of fresh pork sausages (Table 4). However, no significant effect on surface yellowness (\(b^{*}\)) was observed. Other than the fat and salt levels having effects on the colour changes of SS and IW sausages, colour changes are also presumably due to pigments such as fucoxanthin (brown or olive green), \(\alpha\)-carotene (yellow and green), chlorophyll a (blue-green), and phaephytin a (olive-brown) present in SS and IW [40, 41]. These pigments may have a substantial impact on the sensory perception of these sausages, affecting customers' purchasing behaviour and preferences. Similarly, the incorporation of sea spaghetti and konjac into frankfurters and sea spaghetti in gel/emulsion systems decreased \(L^{*}\) and \(a^{*}\) values [11, 42].
## 3 Methodology
### Data Investigation & Pre-Processing
#### 3.2.3 Data Set Splitting  
At the last step of pre-processing, the selected data sets were partitioned into two segments. One part was for training, consuming 80 percent of the data, and another part was for an evaluation test, consuming 20 percent of the data.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
###### Abstract  
The target is often obstructed by obstacles with the dynamic tracking scene, leading to a loss of target information and a decrease in tracking accuracy or even complete failure. To address these challenges, we leverage the capabilities of Geiger-mode Avalanche Photodiode (GM-APD) LiDAR to acquire both intensity images and point cloud data for researching a target tracking method that combines the fusion of intensity images and point cloud data. Building upon Kernelized correlation filtering (KCF), we introduce Fourier descriptors based on intensity images to enhance the representational capacity of target features, thereby achieving precise target tracking using intensity images. Additionally, an adaptive factor is designed based on peak sidelobe ratio and intrinsic shape signature to accurately detect occlusions. Finally, by fusing the tracking results from Kalman filter and KCF with adaptive factors following occlusion detection, we obtain location information for the central point of the target. The proposed method is validated through simulations using the KITTI tracking dataset, yielding an average position error of 0.1182m for the central point of the target. Moreover, our approach achieves an average tracking accuracy that is 21.67% higher than that obtained by Kalman filtering algorithm and 7.94% higher than extended Kalman filtering algorithm on average.  
single target tracking; GM-APD LiDAR; adaptive fusion +
Footnote †: journal: Applied Sciences  
Article
###### Abstract  
Traditional methods for predicting surrounding rock grades ahead of tunnel faces encounter challenges: image-based approaches are susceptible to environmental interference, while parameter-based classification may disrupt construction. This study proposes an intelligent rock grade identification method by integrating longitudinal displacement profile (LDP) evolution patterns with deep learning. First, the numerical model was validated against V-D theoretical curves, and LDP evolution laws were systematically analyzed for three rock types (\(GSI=15\), 30, 50) under nine geological combinations. The results indicate that (1) homogeneous strata exhibit deformation peaks followed by declines; (2) \(GSI=15\) strata show significantly larger deformations; and (3) stratified schemes display pre-interface deformation peaks and post-interface deformation controlled by subsequent lithology. A novel hybrid neural network was developed to classify strata using LDP curves as input. The model achieved 93.25% training accuracy and 91.20% validation accuracy. Ablation experiments demonstrated their superiority over the other four models with partial module deletions, achieving improvements in test accuracy of 3.24%, 3.08%, 4.16%, and 6.48%, respectively, compared to those models. This lightweight solution effectively overcomes the limitations of manual expertise dependency in conventional models and environmental sensitivity in visual methods. By synergizing LDP evolution analysis with deep learning, this framework provides a reliable approach for real-time rock grade prediction during tunnel advancement.  
longitudinal deformation curve; numerical simulation; Squeeze-and-Excitation network; Long Short-Term Memory network; multi-head attention mechanism; convolutional neural network +
Footnote †: journal: Applied sciences
# WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks
## 7 Conclusions  
This paper proposes a novel MW-ZCC coding scheme with a low cross-correlation function for WON-OCDMA system. Codes are easy to convert into multiweight power of two codes, thus suitable for supporting a variety of QoS services in WON, including sensing, datacomms and video surveillance applications.  
The effect of a free space transmission with medium turbulence on the signal transmission and received optical power was analysed. The simulations results revealed that for a minimum allowable BER of \(10^{-3}\), \(10^{-9}\), when supporting triple-play services (sensing, datacomms and video surveillance), the proposed WON-OCDMA employing MW-ZCC  
Figure 9: Probability of error versus transmission distance in moderate turbulence for concurrently transmitting users with weights 2, 4 and 8.  
codes could carry up to 32 services simultaneously at a distance of 32 km in the presence of moderate turbulence in the atmosphere.  
The research results presented in this paper thus offer conceptual approaches for engineering of future OCDMA-based wireless optical networks supporting triple-play services.  
Conceptualisation, S.S. and M.M.; methodology, S.S., M.M., M.D. and I.G.; software, S.S. and M.M.; validation, S.S. and A.A.; formal analysis, S.S., M.D. and I.G.; investigation, S.S., A.A., M.M., M.D. and I.G.; resources, A.A. and I.G.; writing--original draft preparation, S.S. and M.M.; writing--review and editing, S.S., A.A., M.M., M.D. and I.G.; supervision, I.G.; project administration, I.G.; funding acquisition, I.G., A.A. and M.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie under Grant 734331, the Slovak Research and Development Agency under the project APVV-17-0631, and supported by Construction Scotland Innovation Centre under the Collaborative Programme of Funding Support i-Con Platform.  
Not applicable.  
We would like thank the University of Strathclyde, Robert Gordon University and Sublime Digital Ltd. for supporting this research project.  
The authors declare no conflict of interest.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 5 Results and Discussion
### Residual Energy  
Residual energy in the WBAN decreases as the number of rounds increases. Figure 5 describes the relationship between the residual energy of the network and the number of rounds.  
The simulation results show that the energy consumption of TSAER is less than that of the existing two protocols. The reason is that TSAER considers the residual energy when selecting the appropriate forwarding node and pays attention to the residual energy of  
Figure 4: Analysis of network lifetime and stability.  
Figure 5: Analysis of residual energy.  
each node in the network, which can effectively balance the network energy. M-ATTEMPT is more inclined to choose a path with fewer hops for normal data transmission, rather than selecting a path to transmit data based on the residual energy. In iM-SIMPLE, the continuous data transmission by the intermediate node causes this node to form a hot node and continues to work without any treatment measures, which not only causes more energy consumption but also burns human tissues in severe cases. Moreover, the existing two protocols do not prioritize data, and congestion may occur when there is a large load in the network, and the data retransmission cause additional energy consumption.
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 2 Materials and Methods
### Shifting Parameters of Brownian Motion of Cells  
To determine the displacement parameters of cell Brownian motion, a 10\(\times\) objective lens was used to obtain multi-frame images of the cells. When the cells were suspended in the diluent blood, the motion estimation algorithm was used to estimate the displacement of cells undergoing Brownian motion. If the shape of a living cell is close to a circle, the rotation angle is small in two frames. To accurately observe the Brownian motion of cell movement, we used an inverted biologic microscope (MI 52, Mshot, Guangzhou, China) to capture the movement of living cells in the microfluidic chip. The whole blood was diluted by phosphate-buffered saline (PBS) to 1:10,000 and then injected into the microchannel of the microfluidic chip. After the fluid was stabilized in the microchannel, it was observed under a microscope with a 10\(\times\) objective, and the video was captured using a microscope camera (MS60, Mshot, Guangzhou, China) for post-processing. We used MATLAB (Version: 2016a, Natick, MA, USA) to identify the centroid of each cell, which used a threshold segmentation algorithm for the first frame of the captured video, and to segment the image within 200 pixels centered on the location of the centroid of each cell. All subsequent frames were segmented using this centroid position. Under a 10\(\times\) objective lens, the cell size was about 40 \(\times\) 40 pixels and the Brownian motion did not exceed 70 pixels, so the selection of 200 pixels ensured that the cell would not move out of this range in a short period of time. The video capture frame rate was 14 frames per second (fps), and we used a 10 s video to estimate the distance of movement for each cell. The motion was estimated using the frequency-domain motion estimation algorithm in a 140-frame image of the same cell in the video sequence, and the cell motion vector was obtained. This frequency domain motion estimation algorithm is not accurate and is generally used for rough estimation, which can accurately estimate the whole pixel displacement.  
According to the above method, when the cells were subjected to Brownian movement, the circular blood cells did not rotate substantially but the translation was large. With an average translation distance of 30 pixels, the maximum value was 70. In a lensless system, the average displacement was three pixels, and the maximum value was seven pixels. Therefore, we proposed a two-step method for motion estimation. Specifically, we used the above-mentioned frequency domain transmission to estimate the integer pixel, and then we used the Keren method to estimate the sub-pixel motion.
## 1 Introduction
After that, some highlights and conclusions are discussed in Section 5. Finally, the potential works in the near future are presented in Section 6. The notations in this paper are summarised in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Meaning** \\ \hline \(K\) & Information length \\ \(N\) & Codeword length \\ \(R\) & Code rate \\ \(M\) & Modulation order \\ \(E\) & Frame length after modulation \\ \(E^{\prime}\) & Frame length after adding pilot symbols \\ \(\beta\) & Pilot symbol coefficient \\ \(\mathbf{h}_{XY}\) & Channel gain vector between X and Y \\ \(\mathbf{h}_{XX}\) & Self-interference channel gain vector \\ \(x[k]\) & \(k\)-th bit of signal vector \(\mathbf{x}\) in the bit domain \\ \(x[n]\) & \(n\)-th symbol of signal vector \(\mathbf{x}\) in the discrete time domain \\ \(x^{\prime}[n]\) & \(n\)-th symbol of concatenation signal vector \(\mathbf{x}\) with pilot symbols in the \\  & discrete time domain \\ \(x^{\prime}(t)\) & Signal \(x^{\prime}\) in the continuous time domain \\ \(\mathbf{\tilde{x}}\) & Estimation value of \(\mathbf{x}\) \\ \(\mathbf{\tilde{x}}\) & Residual value of \(\mathbf{x}\) \\ \(\ast\) & Convolution operator \\ \(\lambda\) & Forget factor of the RLS algorithm \\ \(i\) & Index of joint iterative iterations \\ \(j\) & Index of 5G QC-LDPC decoding iterations \\ \(\bar{n}_{v}\) & Average degree of the variable nodes \\ \(\bar{n}_{c}\) & Average degree of the check nodes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of notations.
# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
## 2 Materials and Methods  
As part of this study, we created a database of healthy and hypernasal Spanish speakers. The speakers were either children or female adults. The data were obtained using a mobile app, ASICA (see Appendix A for instructions), developed as part of this project, which allowed us to include patients from three different Spanish speaking countries (Chile, Ecuador and Spain). Note that this mobile app was developed as a response to the COVID-19 crisis and the impossibility of recording the participants in our lab. Based on these recordings we proceeded to define a hypernasal dataset and an oral dataset. For this end, two approaches might be considered. One consists in including in the hypernasal dataset only those utterances for which there was perceptual evidence of HN (i.e., item-by-item approach). Another consists in including in the hypernasal dataset all the utterances of those speakers which have been previously classified as hypernasal (i.e., speaker-by-speaker approach). Given the difficulty to annotate the full database item-by-item, we decided to use a speaker-by-speaker approach.  
The resulting datasets were used to run multiple tests that consisted in training and evaluating a HN classifier. Each test was characterized by: (1) the utterance or list of utterances used to train and evaluate the classifier (e.g., syllable sequence _ta ta ta_, the word _dedo_, both the sequence _ta ta ta_ and the word _dedo_); (2) the subset of acoustic features (e.g., MFCC4 of utterance _tatata_... ) and the classification algorithm (e.g., RF, SVM or ANN). As a first step we ran forty-four tests, each one with one utterance in the database. We assumed that as the accuracy of the automatic classifiers would be partly determined by the utterances used to train them, when accuracy was very high (e.g., \"in discriminating healthy speakers from patients by using syllable repetition\"), it would indicate that the utterance used was an optimal candidate to automatically evaluate HN. Next, we ran tests using more than one utterance to train and evaluate each classifier. For this end we created what we call optimal lists of utterances (see details in Section 2.6). Finally, we explored the feasibility of computing, for each speaker, a hypernasality score (HN Score) by combining the results of multiple tests of the same participant. Note that this last approach emulates the scoring used in many speech evaluation tasks, in which a partial score is provided per item and a global score is obtained by computing (e.g., summing) the results of the individual items in the task.
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 2 Experience Driven MPCC Design
The underlying gradients can be estimated as  
\[\frac{\partial L\big{(}\theta^{Q}\big{)}}{\partial\theta^{Q}}=\mathbb{E}_{s,a,s^ {\prime}}\frac{(T_{Q}-Q(s,a|\theta^{Q})\partial Q\big{(}s,a|\theta^{Q}\big{)}}{ \partial\theta^{Q}} \tag{3}\]  
where,  
\[T_{Q}=r+\gamma Q^{\prime}\Big{(}s^{\prime},\mu(s^{\prime}|\theta^{\mu^{\prime} })|\theta^{Q^{\prime}}\Big{)}\]  
Using Polyak averaging after each MIs, the parameters of the target network are updated from the parameters of the online network, i.e., the soft update of the target policy and Q network can be estimated as: \(\theta^{Q^{\prime}}\leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\); \(\theta^{\mu^{\prime}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}}\). The Algorithm 1 demonstrates detailed steps of the DDPG-MPCC algorithm devised. ```
Initialization of DDPG Params: critic \(Q\big{(}s,a|\theta^{Q}\big{)}\), actor \(\mu(s|\theta^{\mu})\) Replay Buffer B and \(Q\big{(}s,a|\theta^{Q^{\prime}}\big{)}\), \(\mu\big{(}s|\theta^{\mu^{\prime}}\big{)}\) whileEpisode\(<Max\)do for\(t=1,2,\ldots\)MIdo  Run Ornstein-Uhlenbeck Process for exploration  Determine \(a_{t}\) using policy network and exploration  Apply action \(a_{t}\) Compute system reward \(R_{t}\)  Determine next state \(s_{t+1}\)  Save transitions \((s_{t},a_{t},R_{t},s_{t+1})\) in Replay B endfor  Retrieve \(\mathcal{N}\) transition from B \((s_{i},a_{i},R_{i},s_{i+1})\) \(\mathrm{Y_{i}=R_{i}+\gamma Q^{\prime}(s_{i+1},\mu^{\prime}(s_{i+1}|\theta^{ \mu^{\prime}})|\theta^{Q^{\prime}})}\)  Renew Critic: min \(\Big{(}L=1/N\sum_{i}(y_{i}-Q\big{(}s_{i},a_{i}|\theta^{Q}\big{)})^{2}\Big{)}\)  Renew Actor Policy using updated policy gradient  Renew Target Networks (using hyperparameter, \(\tau\)): \[ \begin{cases}\theta^{Q^{\prime}}\leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{ \prime}}\\ \theta^{\mu^{\prime}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}} \end{cases} \] endwhile
```  
**Algorithm 1** Pseudocode of DDPG-MPCC Algorithm