esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study comprehensively analyses the morphological, structural, and thermoluminescent (TL) properties of Mn-doped ZnZrO\({}_{3}\) nanoparticles. An optimal Mn-doping concentration of 2-3% enhanced crystallinity, minimizes defects, and improves luminescence efficiency. ZnZrO\({}_{3}\):Mn nanocomposites exhibited green (500-502 nm) and red (604-612 nm) emissions, attributable to oxygen vacancies and Mn\({}^{2+}\) substitutions. XRD confirmed high crystallinity, which further enhances luminescence. Beta (\(\beta\)) irradiation response peaks at 349 K and 473 K increased in intensity with higher doses, indicating stable trapping levels. Increased irradiation doses enhanced TL response due to higher trap densities. Variable heating rate (VHR) studies revealed peak shifts to higher temperatures and increased intensity at elevated rates, with these attributable to thermal lag. These results affirm the material's potential for use in TL dosimetry, highlighting systematic dose-dependent trapping and energy-release mechanisms. TL intensity increased with increasing heating rate, with a higher peak temperature due to thermal activation. Fading analysis showed an intensity decline over time due to carrier de-trapping. Thermal cleaning improved luminescence by eliminating shallow traps. The dose\(-\)response relationship was linear at low doses but deviated at higher doses due to saturation effects. TL intensity decreased with further heating rate increases, emphasizing the need for optimized conditions. These findings confirm ZnZrO\({}_{3}\):Mn's stability, sensitivity, and suitability for dosimetric applications requiring precise luminescence control.  
This research received no external funding.  
No applicable.  
Not applicable.  
All the data will be available on request by the corresponding authors.  
The authors also acknowledge the physics department at Walter Sisulu University for helping with the research technique systems used to characterize the materials employed in this work.  
The authors declare no conflicts of interest.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Signal Conditioning and Final Processing  
The final stage of PPM signal processing requires careful signal conditioning to ensure optimal performance characteristics. This involves the systematic removal of reference pulse components that could introduce interference or bias into the demodulation process. Additionally, the introduction of appropriate DC offset compensation ensures proper signal level management throughout the processing chain. The implementation code for this section can be found in Code S3 of the Supplementary Materials.  
Figure 15 illustrates the complete signal conditioning process and its effectiveness in producing a clean, de-modulable PPM signal. The results demonstrate successful reference pulse subtraction and DC offset addition, culminating in accurate signal recovery that closely matches the original information content. This multi-stage processing approach validates the practical feasibility of PPM implementation in real-world communication systems.
# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 4 Experimental Results
### Estimation of Respiratory Rate in a Free-Motion Environment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scene \#** & **Method** & \begin{tabular}{c} **MAE** \\ **(bpm)** \\ \end{tabular} & \begin{tabular}{c} **MOD** \\ **(bpm)** \\ \end{tabular} & \begin{tabular}{c} **LOA** \\ **(bpm)** \\ \end{tabular} & \
\begin{tabular}{c} **ICC** \\ \end{tabular} &
\begin{tabular}{c} **Mean of** \\ \end{tabular} \\ \hline
**\#3** & Ours & \(0.10\pm 0.15\) & \(0.01\) & \(\pm 0.37\) & \(0.999\) & \(0.91\pm 0.03\) \\
**\#3** & Optical flow [62] & \(2.84\pm 3.45\) & \(2.34\) & \(\pm 7.32\) & \(0.521\) & \(0.44\pm 0.28\) \\
**\#3** & Intensity [57] & \(8.33\pm 9.22\) & \(-5.01\) & \(\pm 23.70\) & \(0.209\) & \(0.21\pm 0.15\) \\
**\#4** & Ours & \(0.12\pm 0.15\) & \(-0.00\) & \(\pm 0.41\) & \(0.999\) & \(0.92\pm 0.04\) \\
**\#4** & Optical flow [62] & \(5.57\pm 3.33\) & \(4.63\) & \(\pm 8.96\) & \(0.192\) & \(0.26\pm 0.20\) \\
**\#4** & Intensity [57] & \(9.45\pm 8.37\) & \(-7.13\) & \(\pm 24.69\) & \(0.237\) & \(0.22\pm 0.15\) \\ Total & Ours & \(0.11\pm 0.15\) & \(0.00\) & \(\pm 0.39\) & \(0.999\) & \(0.92\pm 0.04\) \\ Total & Optical flow [62] & \(4.20\pm 3.64\) & \(3.48\) & \(+8.48\) & \(0.202\) & \(0.35\pm 0.26\) \\ Total & Intensity [57] & \(8.89\pm 8.81\) & \(-6.07\) & \(\pm 24.29\) & \(0.134\) & \(0.22\pm 0.15\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Respiration measurements in environments with motion noise. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scene \#** & **Method** & \(p\)**-Value** \\ \hline
**\#1** & Ours–optical flow [62] & \(0.000\) \\ \hline
**\#1** & Ours–intensity [57] & \(0.000\) \\ \hline
**\#2** & Ours–optical flow [62] & \(0.000\) \\ \hline
**\#2** & Ours–intensity [57] & \(0.000\) \\ \hline
**Total** & Ours–optical flow [62] & \(0.000\) \\ \hline Total & Ours–intensity [57] & \(0.000\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of error statistics in environment with motion noise. more robust to motion noise than conventional techniques. Hence, the results show that the proposed method can be applied to real-world environments where noise can occur with much higher reliability than conventional methods.
## 3 Results
### Comparative Analysis of the Microbial Structure  
Water and sediment samples for microbial characterisation were taken in early spring coinciding with a period of a well-defined oxygen stratification pattern. The amplicon-based next generation sequencing analysis of the 16S rRNA V3-V4 region, carried out in water and sediment samples, provides a comprehensive screening of bacterial population. After denoising, from a total of 379,326 high-quality sequences representing the bacterial community, an average of 126,442 \(\pm\) 9509 sequences was obtained per sample. Afterward, with a clustering threshold of 97% similarity level, a total of 682 non-singletons and unique features (OTUs) were identified with an average of 458 \(\pm\) 132 OTUs per sample (Table 2). Regarding archaeal communities, the dataset reveals poorly enriched and characterised lineages. The sequences clustered in 8 OTU, at 97% similarity level, with very few reads per OTU. A minimum significative number of sequences must be clustered to be considered a valid OTU for classification purposes. For this reason, only the Bacteria domain was considered for community analysis.  
The diversity of prokaryotic population was compared between samples by rarefaction curve analysis of the estimated OTUs. Rarefaction curves showed a plateau stage in all samples, achieving a good coverage of the sequencing analysis of the entire community (Supplemental Figure S1). OTU-based alpha diversity measurement, as Shannon diversity index, decreased in depth at the water column, reaching the highest value in the epilimnion (W1) and the lowest value in the hypolimnion (W4). Prokaryotic diversity in sediments was similar to epilimnion diversity (Table 2).  
by _Rhodobacteraceae_ and _Burkholderiaceae_, respectively, contributed mostly to the spatial distribution of Lake Alboraj W1 sample (upper water sample, 1 m). OTUs clustering with members of _Hydrogenophilaceae_ and _Saccharomycesrilliaceae_ families, within _Gammaproteobacteria_iclass, led the spatial distribution of W4 (deepest water sample, 4 m). _Oxynphotobacteria_ members, within Cyanobacteria, also comprise a representative relative abundance in both water samples depth. Finally, sequences clustering with _Pseudomonasaceae_, _Burkholderiaceae_ (both Gammaproteobacteria), _Propionibacteriaceae_ (Actinobacteria) families and the _Bacillus_ class contributed mostly to the spatial distribution of Lake Alboraj sediment (S) sample.  
Figure 2: Relative abundance of major bacterial phyla from Lake Alboraj water and sediment. Only phyla with a relative abundance >0.5% of total bacterial population were included.
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Classification Algorithms
#### 4.2.1 Gradient Boosting  
Gradient Boosting is an ensemble method in machine learning that builds a robust predictive model by iteratively adding weak estimators, typically decision trees. Each new estimator is trained to correct errors from previous predictions by optimizing the loss function using the gradient. This iterative approach gradually improves the model's accuracy by capturing complex relationships between variables [19].  
This model is particularly suitable for detecting and analyzing geological formations because it manages heterogeneous and complex data sets. It can efficiently process data from multiple sources, such as satellite images, seismic measurements, and drilling data, while capturing non-linear interactions between these variables. Its exceptional flexibility makes exploring complex links between geological parameters possible, making this method efficient in classification and regression tasks [20].
# A Matrix Effect Correction Method for Portable X-ray Fluorescence Data
## 3 Results and Discussion
### The Correction of the Matrix Effect  
The correction indicators should be determined before using the new method for data calibration. The major elements that had the highest correlation with the element to be corrected and are not or just slightly influenced by other elements could be selected as correction indicators [10]. The results of the correlation analysis showed the major elements with the highest correlation coefficients with the elements to be corrected (Table 2). The number of trace elements mostly related to Al in the pXRF analysis was the largest, reaching 11 and accounting for almost half of all the elements. The following major elements were Ti and Fe, which affected six and four elements, respectively. The elements Mn, Si, K and Ca had little effect on the other elements, and the number of elements affected by any one of them was generally less than three. The significant correlation was either reflected in similar chemical properties or in similar positions in the periodic table.  
The testing values and reference values were imported into IBM SPSS Statistics 25 to calculate the coefficients of \(\alpha_{i}\), \(\alpha_{j}\) and \(u_{i}\) for each detected element using Equations (3)-(5). Then, the correction indicators as an independent variable were substituted, along with the coefficients of \(\alpha_{i}\), \(\alpha_{j}\) and \(u_{i}\), into Equation (2) to form multiple linear regression equations for each element, except the indicator elements that were slightly influenced by the matrix effect (Table 3). The reference value and the testing value were used as an independent variable and dependent variable, respectively. For comparison, the  
\begin{table}
\begin{tabular}{c c} \hline
**Correction Indicator** & **Trace Elements to Be Tested** \\ \hline Al & Cu, Se, Rb, Mo, Sb, Sn, W, Tl, As, Bi, U \\ Ti & P, Cr, Y, Zr, Nb, Ta \\ Fe & Mg, V, Co, Ni \\ Mn & Cd, Zn \\ Si & S, Pb \\ K & Ba \\ Ca & Sr \\ \hline \end{tabular}
\end{table}
Table 2: Correction indicators for elements to be tested by pXRF analysis.  
Figure 1: Scatter plots of testing values vs. reference values of the detected elements. The unit of Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe, Cu, Zn, As and Pb was \(10^{-2}\), and that of V, Cr, Co, Ni, Se, Rb, Sr, Y, Zr, Nb, Mo, Cd, Sn, Sb, Ba, Ta, W, TI, Bi and U was \(10^{-6}\).  
performed on the pXRF analysis results of the CRMs, and the testing and reference values of each element were used to establish regression equations (Table 3). Subsequently, both the multiple and simple linear regression equations were used to calculate the regression values for each element. The corresponding scatter plots of the regression values vs. reference values are shown in Figure 2.
## 2 Sample Preparation Methods
### Solid-Phase Extraction
The SPE technique has been applied to the analysis of PhACs that belong to several therapeutical classes (Table 1) in river waters [21; 46; 108], groundwaters [109], drinking waters [10; 110], marine waters, and wastewaters [23; 25; 27; 46; 108], both wastewater influent and effluent. For example, [PERSON] et al. (108) compared the selectivity and capacity of Oasis HLB, Oasis MAX, Oasis WAX, and a commercially available molecularly imprinted polymer (MIP) specific for non-steroidal anti-inflammatory drugs (NSAIDs) on the extraction of a group of 15 PhACs from wastewater. These four different commercial sorbents were tested to check which is more effective in preconcentrating and selectively extracting acidic PhACs from environmental waters. Although the recoveries that were obtained for the 15 PhACs by Oasis HLB with ultra-pure water and wastewater were similar (71-103%), this sorbent did not enable selective washing of real samples. Regarding the two mixed-mode sorbents, the washing step was able to eliminate all of the basic compounds, in contrast to ultra-pure-water experiments. The recoveries range between 60-100% (Oasis MAX) and 14-105% (Oasis WAX). The recoveries of PhACs in wastewater with MIP were slightly lower (45-102%) than those that were obtained with ultra-pure water, but this sorbent allowed for the selective extraction of acidic analytes from wastewater samples [107]. This technique still has several limitations, most of them being associated with the cartridge packing material, regardless of SPE's advantages. Consequently, not only sorbents of new forms (fibers, bars) appeared, but, above all, alternative modes of extraction. In this last approach, the dispersive solid phase extraction (DSPE) and the magnetic solid phase extraction (MSPE) stand out.
## 4 Hardware Description  
The sensor nodes were developed under hardware platforms based on ESP32 (ESPRESSIF SYSTEMS, Co., Ltd., Shanghai, China) that provide embedded Wi-Fi and BLE interfaces besides several input/output pins for reading and control of the sensors. ESP32 is capable of functioning reliably in industrial environments, with an operating temperature ranging from -40 \({}^{\circ}\)C to +125 \({}^{\circ}\)C. It is engineered for mobile devices, wearable electronics and IoT applications, ESP32 achieves ultra-low power consumption with a combination of several types of proprietary software. ESP32 also includes state-of-the-art features, such as fine-grained clock gating, various power modes and dynamic power scaling. It includes built-in antenna switches, RF balun, power amplifier, low-noise receiver amplifier, filters and power management modules [54]. WiPy (Pycom Ltd., Eindhoven, The Netherlands) is a microPython-enabled ESP32-based development board with a full suite of free services [55]. WiPy 3.0 is an updated enterprise grade IoT development platform. The tiny MicroPython enables Wi-Fi and a Bluetooth IoT development platform with a ESP32 chipset and dual processor [56] (see Figure 2). Pycom provides software libraries to control the device and the interfaces that can be used.  
Both the sensor and gateway nodes are designed using this low-cost hardware module.
## 4 Experiment
### Performance Evaluation Metrics  
The models used in the experiment were evaluated with four evaluation metrics: accuracy, F1-score, precision, and recall. These parameters are defined as:  
\[\text{Precision}=\frac{\textit{True Positive}}{(\textit{True Positive}\,+\, \textit{False Positive})} \tag{7}\]  
\[\text{Recall}=\frac{\textit{True Positive}}{(\textit{True Positive}\,+\, \textit{False Negative})} \tag{8}\]  
\[\text{F1 Score}=\frac{2*(\textit{Precision}*\textit{Recall})}{(\textit{ Precision}+\textit{Recall})} \tag{9}\]  
In Equations (7) and (8), true positive (TP) is the number of data classified as positive among the data tagged as positive, and true negative is the number of data classified as negative among the data tagged as negative. False negative (FN) is the number of data classified as negative but were tagged in the dataset as positive, and false positive (FP) is the number of data classified as positive but were tagged in the dataset as negative. Recall is the proportion of documents classified as positive by the model among all actual positively tagged data. Precision is the percentage of documents positively classified by the model as positive, and F1-score is the average of recall and precision.
# Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data
## 2 Materials and Methods
With small datasets, it is useful to use multiple networks in an ensemble approach  
While training the single model, we found that the training accuracy began to plateau around 99% after ten epochs. If training continues past this point, the model begins to over-fit to the training data and not generalize well on the test data. Thus, we decided to train for only ten epochs, saving the model weights after each epoch to use for voting. We then conducted a second experiment using the weights of nine of these intermediate models to vote on the class of each frame in the video. We found an increase in performance over the single model; however, overall, there were still many missing abnormal segments. KID videos 1 and 3 had significantly less abnormal frames than KID video 2, an imbalance which we identified as a potential problem. Our solution was to augment the abnormal frames in these two videos using rotation. KID video 2 has 5050 abnormal frames; therefore, for KID video 1, we rotated each of the abnormal frames by 30 degrees resulting in 12 rotations, and in KID video 3, we rotated each of the abnormal frames by 72 degrees resulting in 5 rotations. These rotations increased the number of abnormal frames from 429 and 988 to 5148 and 4940 for videos 1 and 3 respectively. In our final experiment, the rotated abnormal images were only used in training the model, not in testing. An example of this rotation can be seen in Figure 4. This augmentation, combined with the use of intermediate model weights to vote on predictions, provided us with our best results. Figure 4: Rotation augmentation examples from KID video 3. Figure 3: Network Architecture.
# Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area
## 3 Effects of Different Parameters on Sound Pressure Level
### Analysis of the Influence of Track Type on Sound Pressure Levels  
Given the different types of tracks that make up the surface metro network, the following analysis is of particular importance, since the intention is to analyze the influence of track characteristics on noise levels. It is expected that the Type I track (grassed track), due to its construction and acoustic absorption characteristics, will present the lowest levels of sound pressure [13]. On the contrary, in the Type III track (slab track), it is expected that the noise levels are much higher given the inability of the track to absorb part of the sound energy. The records presented here correspond to the passage of vehicles of the _Eurotram_ type, whose speed of circulation is, in this case, around 40 km/h. As we can see in Figure 12, the sound pressure levels vary significantly for the different types of tracks, particularly at higher frequencies. The grassed track (Type I), as mentioned, due to its intrinsic characteristics of sound absorption, presents sound pressure levels below 70 dBA unlike what happens to the other two types of track. For Type I and Type II tracks the frequency spectrum is very similar, except for the sound pressure levels which are slightly higher in the ballasted track in the whole frequency range. There is an increase in noise levels up to 1000 Hz and then a decrease in the higher frequency bands. In the case of  
Figure 11: Comparison between different speeds for the _Eurotram_ vehicle operating on a Type I track: (a) Microphone 1; (b) Microphone 2.  
Figure 10: Comparison between different speeds for the _Eurotram_ vehicle operating on a Type II track: (a) Microphone 1; (b) Microphone 2.  
track Type III (slab track), the sound pressure levels in a frequency band extending from 2000 Hz to 12.5 kHz often exceed 60 dBA. The large difference between the three types of tracks under analysis lies precisely in the frequency range mentioned. This trend is justified by the acoustic characteristics of the tracks, as previously discussed. The slab track is not able to absorb part of the sound energy that is generated and allows it to reach the receivers. In the frequency spectrums, it is clear the difference between the various types of the track about the \(L_{A\omega_{I}T}\) indicator this difference is also quite notorious, differing from each other by about 4 dBA. For the Type I track, the indicator value is 77 dBA, for the ballasted track (Type II), the indicator value is 83, and for the Type III track, the \(L_{A\omega_{I}T}\) value is 87 dBA.
## 4 Experiment Result
The addition of the EC-BER algorithm drastically improved the reliability of the system resulting in minimal to zero errors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Turbo Iteration** & **1** & **3** & **5** \\
**Packet Number** & **1** & **3** & **5** \\ \hline
1 & \(10^{-0.94}\) & \(10^{-1.09}\) & 0 \\ \hline
2 & \(10^{-0.94}\) & 0 & 0 \\ \hline
3 & \(10^{-0.43}\) & \(10^{-0.46}\) & \(10^{-0.56}\) \\ \hline
4 & 0 & 0 & 0 \\ \hline
5 & \(10^{-0.69}\) & \(10^{-0.77}\) & \(10^{-0.82}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis according to the number of Turbo iterations. (\(N_{c}=32\), \(N_{b}=4\)). Figure 7: Channel characteristic: (**a**) Channel impulse response and (**b**) Doppler spread.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 2 Neural Networks Training for SAR Imagery
### Unsupervised Learning Techniques  
Among the most popular unsupervised learning techniques for images, there are exist autoencoders [29], sparse coding networks [30], and Generative Adversarial Networks (GANs) [31]. Additionally, algorithms such as the Boltzmann Machine or Deep Belief Networks can be adapted for image processing.  
Figure 4: Image of a building (**a**) from close to nadir angle, (**b**) from constant Look-down/elevation angle and three azimuth angles, (**c**) from constant azimuth with different look-down angles (1 is the highest, 3 is the lowest).