esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Dynamic Time Warping to Determine the Shear Wave Velocity from the Down-Hole Test
## 6 Discussion  
The results show that the determination of the difference in the times of arrival of a wave at two geophones during a down-hole test is possible using the DTW method. However, the proper preconditioning of seismic signals is necessary before attempting to match them. For this reason, a comprehensive data preparation procedure is proposed that includes noise and trend removal, amplitude normalization, and the extraction of the informative part of the signal. Such data cleaning allows more accurate results to be obtained. Despite the appropriate preparation of signals, some of them still exhibit a low quality, which renders further analysis unfeasible. It is suggested that the signals' quality should be evaluated based on their similarity. Only highly similar signals can enable the precise determination of wave arrival time differences. Due to the possible shifts, extensions, or contractions in the signals, the DTW algorithm is used in this regard.  
The proposed methods were tested on real data, and the estimated speed values were compared with the speed determined from the cross-hole test, which served as reference data. The results showed a large influence of signal quality on the accuracy of the results. Removing about 9% of very low-quality data reduced the RMSE by an average of 37%. The proposed method of determining the signal quality may also allow researchers to select a more precise result obtained from left and right shear wave measurements, as well as the ability to repeat the test in the same place.  
Conceptualization, P.S. ([PERSON]) and N.D.-M.; methodology, N.D.-M., W.K., M.S. and P.S. ([PERSON]); software, N.D.-M. and W.K.; validation, W.K., M.S. and S.A.; formal analysis, W.K. and M.S.; investigation, P.S. ([PERSON]) and P.S. ([PERSON]); resources, N.D.-M. and S.A.; data curation, W.K.; writing--original draft preparation, N.D.-M., W.K.; writing--review and editing, P.S. ([PERSON]); visualization, N.D.-M. and W.K.; supervision, S.A., [PERSON] ([PERSON]) and [PERSON] ([PERSON]); project administration, S.A. and P.S. ([PERSON]); funding acquisition, S.A. and P.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work has received funding from EIT RawMaterials GmbH under Framework Partnership Agreement No 21123 (project Sec4 TD).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Side** & **All** & **Q\textgreater{}0.5** & **Q\textgreater{}0.7** \\ \hline \multirow{3}{*}{1} & left & 64.52 (100\%) & 58.29 (92\%) & 59.90 (73\%) \\  & right & 102.74 (100\%) & 50.80 (90\%) & 47.63 (83\%) \\ \hline \multirow{3}{*}{2} & left & 117.69 (100\%) & 46.42 (82\%) & 42.21 (64\%) \\  & right & 75.22 (100\%) & 69.66 (86\%) & 63.04 (64\%) \\ \hline \multirow{3}{*}{3} & left & 253.94 (100\%) & 131.80 (95\%) & 124.30 (77\%) \\  & right & 79.05 (100\%) & 79.05 (100\%) & 78.30 (89\%) \\ \hline \multicolumn{3}{c}{Average} & 115.53 (100\%) & 72.67 (91\%) & 69.23 (75\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of RMSE values for individual tests.
## 3 Hybrid Method  
We propose a high throughput system that can correct \(t\) bit errors over different BCH codes \((n,k,t)\), i.e., error correction over different finite field dimensions, using hardware design and GPU kernel routines. Figure 2 depicts the architectural block diagram for our proposed hybrid method. The flash memory interface is a physical interface to a flash memory device, and the host interface is a standard bus interface, which communicates with the GPU. The GPU could either reside inside the host interface (system on chip) or external to the host system. It is important to note that the GPU system in the system is used for dual purposes, i.e., for the graphical display and error correction. Furthermore, in our proposed method, the GPUs are only deployed when there is an error detected in the page, and the GPUs are not used for pages without error. It is assumed that the host system exercises a memory copy routine to transfer data whenever there is an interaction between the host system and the GPU system. The syndrome generation, proposed in [14], is split into two modules: the Syndrome Residual Unit (SRU) and the syndrome kernel. Then, we propose to implement the modules SRU and the FIFO (shaded area) in hardware and to use GPU kernel routines for the modules' syndrome calculator, key-equation solver, and an error corrector.
# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 8 Conclusions  
In this paper, we have proposed and showcased a semantic ONTO-SAFE framework that acts as a supporting tool in the decision-making process to prevent a wildfire event. The framework can significantly support decision-making issues by providing valuable information to relevant stakeholders (first responders, decision makers, organization managers, etc.) to detect possible wildfire events or the appropriate conditions to conduct the prescribed burning process. Moreover, the study demonstrated how the suggested ontology meets the needs of end users by presenting an ontology-based reasoning module for wildfire event management. In addition, the evaluation of the ONTO-SAFE framework shows that it can be optimized for large-scale implementation. Future work includes a) the extension of the proposed framework to similar situations corresponding to other forest regions in order to demonstrate the semantic reasoning techniques, b) the enrichment of our reasoning services to optimize the response time in order to support the decision-making process more effectively, and c) the integration of machine learning algorithms in order to enhance the framework's effectiveness in handling forest fire situations.  
Conceptualization, P.M., S.K. and Z.V.; methodology, P.M., Z.V. and G.M.; software, P.M.; validation, P.M., Z.V. and G.M.; formal analysis, P.M.; investigation, P.M. and G.M.; writing--original draft preparation, P.M., S.K. and Z.V.; writing--review and editing, G.M., S.V. and I.K.; visualization, P.M.; supervision, S.K., S.V. and I.K.; project administration, S.V. and I.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the EC under contracts H2020-869353 SAFERS and H2020-101021957 NIGHTINGALE.  
Not applicable.  
The authors would like to thank [PERSON] from the Pau Costa Foundation (PCF), who shared with them the results from their experiments that have been conducted regarding the prescribed burning plans in the Canary Islands and Extremadura areas in order to use them for testing the suggested framework.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AL & Attributive Language \\ BUI & Buildup Index \\ CAP & Common Alerting Protocol \\ DC & Drought Code \\ DL & Description Logic \\ DMC & Duff Moisture Code \\ FFMC & Fine Fuel Moisture Code \\ FWI & Fire Weather Index \\ ISI & Initial Spread Index \\ KBP & Knowledge Base Population \\ KBR & Knowledge Base Repository \\ KBS & Knowledge Base Service \\ OWL & Web Ontology Language \\ RDF & Resource Description Framework \\ SPARQL & Simple Protocol and RDF Query Language \\ URI & Uniform Resource Identifier \\ WWW & World Wide Web \\ W3C & World Wide Web Consortium \\ \end{tabular}
## 2 Materials and Methods
### Feature Selection and Machine Learning Methods
#### 2.6.3 Performance Evaluation  
We implemented a comprehensive performance evaluation framework using 5-fold cross-validation to ensure that the performance estimates were robust to any particulartrain/test split. The primary evaluation metric was the area under the receiver-operating characteristic curve (AUC), which quantifies a model's ability to discriminate between cognitively normal and impaired individuals across all classification thresholds. We also reported the accuracy, balanced accuracy, sensitivity, specificity, precision, and F1-score to provide a detailed assessment. For each metric, 95% confidence intervals were estimated through bootstrap resampling (10,000 iterations) to assess statistical robustness. Confusion matrices were examined to characterize the classification outcomes in different patient subgroups.  
To address disease heterogeneity, we adopted three complementary strategies. First, we quantified the distribution of the disease types (MS, NMOSD, and others) across the cognitive status groups and, where appropriate, included disease type as a covariate in our models. Second, we carried out subgroup analyses by disease type, developing separate classifiers for MS and NMOSD patients to identify both shared and disease-specific handwriting biomarkers. Finally, we performed sensitivity analyses by stratifying the validation folds by disease type to confirm that our findings were not driven by uneven disease distributions between cognitive groups.
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 4 Experimental Characterization
### Silicon-on-Insulator Device  
The heaters exhibited an average I\({}_{\pi}\) and P\({}_{\pi}\) (i.e., the current and the power needed to impress a \(\pi\) phase shift) of 14.2 mA and 55 mW, respectively, with 275 \(\Omega\) average resistance. Each MZI has a loss of approximately 0.7 dB and an average extinction ratio (ER) of 26.8 dB, both evaluated near 1550 nm. Figure 5a reports the normalized transmission along the longest path (from I\({}_{1}\) to O\({}_{1}\) in Figure 3) accounting for the grating coupler (GC) spectral response: a 3 dB bandwidth of 35 nm can be appreciated, while the insertion loss (IL) is about 4.5 dB at 1550 nm along the same path crossing 6 MZIs, with an additional 6.5 dB loss per GC. Figure 5b,c show the normalized transmission of MZI 6 in cross state and MZI 9 in bar state at 1550 nm, respectively, the best and the worst ones from an ER standpoint, with extinctions of 42.5 and 22.9 dB.  
The impact on the bit resolution of input analog signals crossing the silicon-on-insulator linear optical processor has been then assessed. Figure 6 reports the ENOB reduction as a function of laser power with respect to the reference signal at the maximum power; the reference curve, as well as the the best and the worst device paths (i.e., \(I_{4}\to O_{4}\) and \(I_{2}\to O_{1}\), respectively) have been plotted. An ENOB degradation in the range 2.3-3.3 can be appreciated for the best path with respect to the reference, while the variation due to different paths is <1. Looking at the sources of ENOB reduction, they are mainly due to optical losses that directly degrade the SNR. Considering a 1 ENOB loss every 6.02 dB, the GCs alone are responsible for a loss more than two equivalent bits due to significant coupling losses. On the other hand, the analyzed spectra exhibited negligible distortions with a THD degradation < 1 dB.
# Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy
## 3 Peak Location Algorithms  
One common peak location algorithm uses differences and second differences to locate each local maximum [30]. The first derivative changes sign from positive to zero to negative at a peak; therefore, the second derivative for automatic full-energy peak location was proposed by [30]. However, real spectra have significant fluctuations because of the discrete and statistical nature of the data. Thus, a discrete analog of the second derivative (the second difference) is typically used.  
Each local maximum is a candidate peak, and simple screening methods can eliminate some local maxima from consideration. Two common screening methods are to check whether the local maximum is sufficiently larger than the nearby background level, which is larger than an absolute threshold, or the peak width is larger than a threshold.  
No matter what peak detection and location methods are used, as seen in Figure 1, it is unlikely that all birth energies will lead to a detected peak. Whenever a peak detection algorithm fails to resolve overlapping peaks, some information will still be extracted from the resulting combined peaks. Therefore, library comparison RIIDs [1; 2; 3; 4; 5; 6; 7; 8; 9; 10] have the potential to work better with customized libraries with a single peak energy and peak area assigned to a composite peak. For example, the libraries in [31] only save information regarding the combined peak. It is still necessary to assess whether a peak detection algorithm can resolve overlapping peaks. The minimum energy gap (minimum resolvable energy, MRE) that a peak detection algorithm can resolve can be estimated using real and/or simulated spectra. The MRE will be different for different peak detection algorithms and detector response functions, which depends on the relative amplitude of the adjacent peaks [8]. The Bayesian RIID in [8] used the photopeak libraries in [31] generated by the method in [8]; almost certainly the simulated spectra would be meaningfully different from corresponding real spectra; the impacts of simulation quality on RIID development and assessment will be addressed in future work.  
As an example, Figure 3 plots example raw data and a fit to one peak and a fit to two peaks. The fit with one peak leads to a different estimated peak center (centroid) and net peak area than does the fit with two peaks.
## 3 Results and Discussion
### Effects of \(p_{0}\) and \(\alpha\)  
The value of \(p_{0}\) is the value of pressure at the upper channel wall, but this also affects the pressure distribution across the channel, as seen in Equation (22). In Equation (29), \(p_{0}\) appears in the denominator of the exponent that defines viscosity.  
Parameter \(\alpha\) is a viscosity control parameter which, when varied on its own, adjusts the rate at which the viscosity changes with pressure. As seen in Equation (29), an increase in \(\alpha\) results in an increase in the exponent that defines viscosity as a function of pressure. Since the value of the viscosity on the upper channel wall is set via \(\mu_{0}\), increasing the value of \(\alpha\) results in increasing the viscosity in the lower portion of the channel.  
The effects of increasing \(p_{0}\) on viscosity distribution are therefore opposite to the effects of increasing \(\alpha\).  
The results for the variation of \(p_{0}\) and \(\alpha\) can be seen in Figures 5 and 6, respectively, while all other parameters take on the values identified in the Reference Case given in Table 1. Increasing \(p_{0}\) translates to a higher value of pressure on the upper channel wall due to the pressure boundary condition there. At the lower channel wall, \(y=0\), Equation (22) gives \(p(0)=p_{0}+cos\vartheta\), which, for a given \(\vartheta\), increases with increasing \(p_{0}\). Therefore, the pressure distribution across the channel decreases from \(p(0)=p_{0}+cos\vartheta\) to \(p(1)=p_{0}\). Clearly, the higher the value of \(p_{0}\), the lower the ratio \(P=\frac{P}{p_{0}}\) that appears in Equation (29). This implies that the viscosity across the channel decreases and offers less resistance to the flow. The end result is that flow velocity increases with increasing \(p_{0}\).  
The effect of low \(p_{0}\) (high \(\alpha\)) is to cause the viscosity to vary more rapidly throughout the channel, thereby decreasing the flow's velocity in the lower, highly viscous regions of the domain. Furthermore, this rapid variation in viscosity across the channel causes significant variation in the shear force across a differential fluid element, as seen in the plot of \(\Big{(}\frac{du}{dy}\Big{)}\Big{(}\frac{du}{dy}\Big{)}\) in Figure 5, and a corresponding change in \(\mu\Big{(}\frac{du}{dy^{2}}\Big{)}\), with the latter developing a significant region of reversal of direction caused by the region of inflection in the velocity profile.  
As \(\alpha\to 0^{+},\ \mu(y)\rightarrow\mu_{0},\ \ \text{as can be seen from Equation (29). When the viscosity is constant across the channel, the velocity profile is near symmetric about the longitudinal centreline of the channel. As \(\alpha\) increases, the viscosity distribution across the channel decreases with increasing \(y\), and the flow experiences more resistance in the lower parts of the channel. Therefore, velocity decreases with decreasing \(y\), accompanied with greater deviations from the symmetry in the velocity profile. This behaviour is demonstrated in Figure 6, which shows the effects that can be observed when \(\alpha\) increases from 0.01 to 10.
# DTV Essential Hidden Area Decoder for DTV Broadcasting
## 4 Experimental Results  
To analyze DEHA for conventional HDTV broadcasting, the proposed DEHA decoder is implemented on a general DTV decoder by adding a DEHA decoder use of block average and modifying the overlay block to data and the check pattern image of DEHA, as shown in Figure 12.  
The proposed stream was created by modifying the DEHA generation part in MSSG Software. The proposed stream was configured similarly to the actual broadcasting system using a well-known universal DTV stream generator, channel coder, and Vestigial Sideband (VSB) modulator. The proposed decoder applies the register value corresponding to the display size as large as the DEHA size in the existing DTV decoder so that pixels can be output, and the DEHA is overlaid on the active region using a Field-programmable gate array (FPGA). DEHA Overlay Mode can be set to DEHA Text, DEHA Image, QR code, and Magnified DEHA, as shown in Figure 10.  
There are 240 blocks in the DEHA for an HDTV broadcasting system. DEHA values of only the first 20 block value are overlaid in a three-digit decimal on the bottom of a decoding image. In DEHA analysis using the proposed decoder, a general video encoder copied the last line as the DEHA, as shown in Figure 1, and almost all real-time video encoders use a black bar as the DEHA in Figure 12.  
Experiments for DEHA utilization (Figure 13) were conducted under a variety of sequences, as shown in Table 3.  
All sequences via SVT were selected from VQEG's original footage and standard HDTV test materials recommended in ITU-R BT.1210 [19; 20].  
Figure 11: A 21 \(\times\) 21 QR code using error recovery function for MPEG CBR lossy compression.  
Figure 12: The proposed DEHA decoder using overlay mode.  
Program-related technical metadata are prescribed for DEHA utilization in the previous section (Table 2).  
The sequences have been designed to represent a demanding, but not unduly so, multi-genre TV program, hence it can be suggested that this set of sequences can act as a reference for the peak bit rate (video elementary stream) that is needed for secondary distribution (also known as transmission) of most TV programs to reach 'good reception condition' (also known as broadcast quality).  
The above list is ordered according to the coding difficulty, with the most difficult sequence at the top of the list. If the whole program is encoded at a bit rate where a sequence classified as 'difficult' does not have a bigger quality drop than 30% in a DSCQS test, it can be assumed that 'good reception condition' (also known as 'Broadcast Quality') according to ITU-R BT.1122-1 can be obtained for the whole program. The same appears for sequences classified as 'easy' (12% quality drop allowed).
## 3 Materials and Methods
### Chemical-Physical Tests and Non-Destructive Analytical Technique  
The upcoming sections outline the results from the chemical and physical tests. We discuss the results of the geotechnical analyses on soil samples and the characterization of the green materials used. To determine the chemical composition of the green materials, energy-dispersive X-ray spectroscopy (EDS) was employed using a Phenom Pro X microscope (Boston, MA, USA). This equipment enabled both Scanning Electron Microscopy (SEM) for morphological analysis and energy-dispersive spectroscopy (EDS) for compositional analysis. The powder samples were first dried at 105 \({}^{\circ}\)C until a constant weight was achieved, then ground in a ball mill to sizes smaller than 100 \(\upmu\)m. Each component was pressed into a 1 cm diameter disk, selecting an area of approximately 1 mm\({}^{2}\).  
For the mineralogical analysis, a Rigaku Powder Miniflex 600 diffractometer (Tokyo, Japan) was used, equipped with Cu K\(\alpha\) radiation and a 20 range from 5\({}^{\circ}\) to 80\({}^{\circ}\), scanning at 0.05\({}^{\circ}\) 20/s. Mineral phase identification was performed using Rigaku PDXL2 software version 2.0.  
Thermal analysis was carried out with a Mettler Toledo TGA/DSC 2 STAR system, capable of performing thermogravimetric analysis (TGA) in the 20 \({}^{\circ}\)C to 1200 \({}^{\circ}\)C temperature range, with a heating rate of 10 \({}^{\circ}\)C/min under an air flow. To assess particle size distribution, a Malvern Instruments Mastersizer 2000 laser scattering analyzer (Worcestershire, UK) was employed, covering a particle sizes range of 0.02 to 2000 \(\upmu\)m. The pH of MSWI-FA and washed fly ash (W-FA) was measured using a Thermo Orion 420A+ pH meter (Boston, MA, USA), with a mixture of MSWI-FA and deionized water in a 1:2.5 (_m/v_) ratio.  
Heavy metal content was analyzed using atomic absorption spectroscopy (AAS) with a Varian SpectrAA 55B spectrometer, equipped with a flame (acetylene/air) and a deuterium lamp for background correction.  
Finally, the chloride and sulfate content in MSWI-FA and W-FA samples was determined through a leaching test in deionized water for 24 h, followed by ion chromatography to analyze the leachate solution.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique  
Remote sensing technology aims to monitor and capture the earth's information without making direct contact and destroying it. The utilization of the electromagnetic spectrum, ranging from visible to microwave for measuring the earth's properties, is the main idea behind remote sensing technology. Since the target's reactions to various wavelength regions differ, we can exploit them to identify vegetation, water, soil, and other features [35]. Combining the target's reaction with the shape, texture, and pattern information of weeds and crops, we can discriminate them and improve SSWM using remote sensing algorithms.  
The image processing workflow to detect weed in paddies can generally be divided into five stages: image data collection, pre-processing, feature extraction and selection, training, image classification and validation [36].