esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Noise Effects  
This section attempts to analyze the reliability of the proposed method for noisy signals. For the experiment, a relatively stable Record 234 with a constant RR interval was contaminated at 5 s intervals by the MA and BW of the NSTDB [33], as shown in Figure 17. The third-row figure shows the R points and noise regions detected in the noisy Record 234 signal. The noise regions were detected by the noise detection method using the vertical histogram introduced in Section 2.2. In the noise region, the low signal level represents non-noise regions, while the high signal level means noise regions. Also, the R-points indicated by the triangle are the R-points contaminated by noises and are excluded from the RR interval analysis because the contaminated R-point degrades the accuracy of the RR interval analysis.  
The analysis similarity for the original Record 234 and the noisy Record 234 is shown in Figure 18. In the case of the noisy Record 234, half of the total signal was contaminated by noise. As a result, the contaminated R points were excluded from the analysis of the figure, and the overall detection  
Figure 16: Calculation time per 1000 iterations for 11 seconds of Record (**a**) 108, (**b**) 111, (**c**) 118, (**d**) 217, and (**e**) 214.  
time was reduced by half. The average beat-per-minute (BPM), average RR interval, root mean square of successive differences (RMSSD) and width and length in a Poincare plot are 92.44 bpm, 0.65 s, 0.04 s, 0.02, and 0.05 for the original Record 234 and 92.62 bpm, 0.65 s, 0.04 s, 0.03, and 0.05 for the noisy Record 234, respectively. We can see that the analysis result of the noisy Record 234 is similar to that of the original Record 234, even though it was undermined by MA and BW.
## 3 Results and Discussion
### Static Headspace Sampling
Furfural is a neutral molecule and its volatility is not affected by sample pH. As seen from Fig. 3 emission of furfural seems to be inversely proportional to the pH of cellulosic sample. This indicates that production of furfural is accelerated by acid hydrolysis of polysaccharides in cellulosic materials. Correlation between pH and emission of furfural from paper can be therefore exploited for non-destructive estimation of paper pH as well as for the indication of cellulose degradation via the acid-catalysed mechanism. Using real samples (Table 1), a satisfactory correlation between furfural emission and sample pH was obtained (Figure 4). This is remarkable, considering the wide variety of paper samples used, especially in view of their composition - from cotton, containing almost pure cellulose to groundwood, containing up to \(\sim\)50% lignin. We attempted to normalise the peak area for furfural against the peak area of acetic acid, however, no improvement in the quality of correlation was obtained. This may be understandable, as while furfural is predominantly a degradation product of carbohydrates, acetic acid may form also during degradation of other paper components (e.g. lignin). The methodology allows for estimation of paper sample pH without an addition of water, which is needed for pH measurements using electrochemical techniques. This is important in cases, where sample wetting interferes with sample chemistry. However, as stressed above, the destructive sampling procedure prevents the methodology to be used for historical papers. Figure 3: Comparison of chromatographic peak areas for acetic acid and furfural emitted by cellulosic samples (Whatman filter paper no.1) impregnated with different phosphate buffers after an 18-h annealing period in a glass vial and equilibrium pH of the same samples. Conditions of extraction using SPME fibre: 40 \({}^{\circ}\)C, 1 h. Typical relative standard deviation in determination of log(_A_) was \(<\)5%.
## 3 Materials and Methods
### Results and Findings  
Results of part 1 are shown in Figure 5(a), which reflects the performance of the classifier and how much muscle fatigue can affect the classifiers. It can be seen that the classifiers with different features have different performances. MAV, RMS, WL, and SMA have better performances than ZC, WL, SSC, MDF, and MPF. The classification deterioration caused by muscle fatigue can be reflected by the accuracy comparison between the train and the test. The accuracies become significantly lower for the six features, including MAV, RMS, ZC, SSC, MDF, and MPF, while the changes are not significant for WL and SMA. Among all of the eight features, SMA has the best performance under the muscle fatigue condition.  
Figure 6: Results of the classification. (**a**) Results of part 1 that uses the non-fatigue data as the training set and the fatigue data as the test set; (**b**) results of part 2 that adds half of the fatigue data into the training set. The bars denote the mean value of accuracies, and the error bars denote standard deviation. Stars (*) indicate that the accuracies of the training and the test are significantly different by one-way ANOVA, and the significant level is set to \(\alpha=0.05\).  
Figure 6b shows the results of part 2, which is to validate our training strategy. In this part, the accuracies do not significantly decrease when testing on the test set, and the classifiers even perform better on the test set than on the training set (i.e., the cross-validation) in some cases. The accuracies of the cross-validation are not obviously lower than those in part 1. These results suggest that our strategy improves the performance of the classifier under fatigue. Figure 7 displays a scenario of potential application.
## 1 Introduction  
Optical microcavities are important for optical integration in practical applications [1; 2; 3; 4; 5; 6; 7; 8; 9]. Among them, Fabry-Perot microcavities (FPMC) are relatively simple to fabricate with high finesse [10; 11; 12], which is important for nonlinear optics applications, e.g., second-harmonic generation [12; 13; 14; 15], parametric amplification [17; 18; 19], frequency-comb generation [20], and entangled photon generation [21]. Efficient nonlinear processes can be achieved due the cavity enhancements, with high-reflective coatings over nonlinear optical material like LiNbO\({}_{3}\) (LN) and KTiOPO\({}_{4}\) (KTP) [22; 23; 24; 25].  
On the other hand, semiconductor lasers are key components in coherent telecommunication and dense wavelength-multiplexing transmission systems, due to their convenience and low cost. However, the above applications require narrow linewidth and high wavelength stability, which can be challenging for normal diode lasers. Even with narrow-linewidth designs, as in distributed feedback lasers or distributed Bragg reflector laser, their output wavelength can be easily affected by the current and temperature fluctuations, and relatively high sidebands, which exist in their spectra. Several approaches have been developed to stabilize the diode lasers [26; 27; 28; 29; 30], including the external feedback from the high-fnisse cavity.  
In this work, we fabricate a FPMC, with high-finesse of up to 55.5. This FPMC is used for the self-injection locking of a semiconductor distributed feedback (DFB) laser. The locked DFB laser shows low wavelength drift, which is free from current and temperature variation. A 402 kHz beat-note linewidth can be observed, showing a good and stable locking.
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 3 Results and Discussions
### Phenanthrene Sorption Performance
#### 3.2.4 Effect of Competitive Hydrophobic Organic Carbons (HOCs)  
The removal efficiency (R, %) and distribution coefficient (\(K_{d}\), mL/g) of PHE remained consistently high, exceeding 93% and 1.3 \(\times\) 10\({}^{4}\) mL/g, respectively, even in the presence of four other HOCs, as illustrated in Figure 5b. This indicated the remarkable selectivity of AAS towards PHE. Furthermore, the R (%) and \(K_{d}\) values followed the order: PHE \(\approx\) PYR \(\approx\) NAP > DBP > DiBP. The reduced sorption of DBP and DiBP, compared to PHE, can be attributed to their larger molecular size and longer chains, which hinder their diffusion into the pores of AAS. This hindrance limits their interaction with the sorbent surface, thereby reducing their sorption capacity [40].  
The adaptability of AAS for removing PHE from complex water matrices was evaluated, as depicted in Figure 5c. The experiments included groundwater and seawater, both of which posed challenges for PHE removal by AAS, resulting in reduced removal efficiencies of 62.5% and 66.8%, respectively (see Table 6). These lower efficiencies were attributed to the complex compositions and varying conditions of these environmental matrices. Despite these challenges, AAS consistently demonstrated effective PHE removal, with removal efficiencies (R%) consistently exceeding 70% across all tested water matrices. These results implied the robust performance and potential versatility of AAS as a reclaimable sorbent for remediating PHE-contaminated water. The ability of AAS to maintain relatively high PHE removal efficiencies in diverse water matrices highlights its suitability for practical applications in environmental remediation, offering a promising solution for mitigating PHE pollution in both natural and industrial settings.  
The sorption of PHE using AAS was attributed to three main mechanisms. Firstly, AAS contains Ca\({}^{2+}\) and Mg\({}^{2+}\) ions (Table 1), which interacted with the benzene ring of PHE through cation-\(\pi\) interactions [41]. Secondly, the surface of AAS exhibited a negative charge in water at pH 5.5 (pHp\({}_{\text{P2c}}\) = 6.8), allowing it to form a thin layer of water film and a surface microlayer that allowed the formation of a hydrogen bond with the benzene ring of PHE [23]. Thirdly, external surface sorption and flocculation may occur due to the presence of C-S-H minerals in AAS. C-S-H was insoluble in water and initially separated out as colloidal particles, gradually condensing into a gel structure. This gel played a role in the flocculation and subsequent removal of PHE from water [42]. These mechanisms collectively contribute to the effective sorption of PHE by AAS, making it a promising material for applications in the removal of PHE from contaminated water.
## 3 Results and Discussion
### Sensory Attributes  
The results of the organoleptic tests are shown in Tables 3 and 4. Generally, a significant (\(p<0.05\)) influence of sumac on the sensory properties was observed after treatment of fish samples with sumac at a ratio of 1.5% (T2). Organoleptic evaluation after 24 h of chilled storage revealed a significant reduction (\(p<0.05\)) in the natural fishy odour of T2 and T3 compared with T0 in a raw state and after heat treatment, whereas sour odour (in a raw state) and flavour (after heat treatment) were increased significantly (\(p<0.05\)) after sumac treatment at the T2 level (1.5%) (Table 3). The reason for such acidic odour and flavour may be attributed to the fact that sumac contains some volatile substances with a distinct flavour and desired sour taste [37]. Sumac was able to mask fish odour and flavour significantly (\(p<0.05\)) at ratios of 0.5-1.5% (T1-T2) in raw and heat-treated samples (Table 3). This property of sumac could be beneficial for masking or reducing the muddy and/or musty off-flavours that represent a post-harvest problem in some freshwater fish. It has been found that acidification treatment of the off-flavour compounds using acetic and citric acids significantly decreased the concentrations of geosmin and 2-methylisoborneol (MIB) in investigated samples [38]. [PERSON] et al. [39] indicated that the pH-shift method may be utilised to reduce off-flavours and thereby increase the acceptability of processed products of catfish. The results of organoleptic evaluation after 72 h of chilled storage are stated in Table 4. The panellists reported a significant reduction in overall acceptability and colour acceptability for T2 and T3 in a raw state. In contrast, a good colour for chicken wings treated with a water extract of sumac was reported by [PERSON] et al. [14], and a significant increase in the grades awarded by the panellists for the colour characteristics of beef samples treated with sumac was confirmed by [PERSON] et al. [37]. Such a positive effect of sumac on the colour of meat could be attributed to the use of a water extract of sumac by these researchers, whereas we seasoned the fish fillets directly with ground sumac. The significant (\(p<0.05\)) increase in juiciness for T3 samples in a raw state in comparison with T0 is in agreement with [PERSON] et al. [37], confirming that sumac contains some softening agent such as citric acid that increases the tenderness and juiciness of the treated meat. The natural fishy odour, sour odour and flavour, and masking of fish odour and flavour (in a raw state and after heat treatment) of the investigated samples are in line with organoleptic evaluation after one day of chilled storage (Tables 3 and 4). A positive effect of sumac on the sensory properties of meat during chilled storage has been reported by several authors [12; 14; 29; 37; 40]. The fact that such a clear role of sumac was not observed in our study could be due to the treatment of samples with ground sumac instead of a water extract of sumac, as well as the fact that sumac is not a substance that is usual for consumers (panellists) in our region.
## 3 TVWS-DTV Coexistence: Field Measurements
### Field Tests Considering Long Distances
#### 3.2.2 Transmission Power of the TVWS BS and Definition of Locations
Figure 12 presents the chosen locations, as well as prediction information for each location and the topographic relief profile of the link. The chosen locations were named \"MS_Loteamento\", \"MS_DeltaBlack\", \"MS_CafezalManoela\", and \"MS_Pedralva\", which were located at straight-line distances (\(d\)) of 5.68, 7.35, 14.7, and 38.4 km from the BS, respectively. The selected locations, described below, cover a variety of environments typical of the interior of the country, allowing the analysis of TVWS performance under different propagation and use conditions:  
* MS_Loteamento, located in a neighborhood far from the urban center, was chosen to represent expanding rural communities that, although far from central areas, have a higher concentration of residences and small properties. The objective of this location was to verify how TVWS can complement existing networks or act as a primary solution in regions with limited fiber optic or mobile network infrastructure;
* Delta Black Farm (MS_DeltaBlack) was selected because it represents large agricultural areas, where connectivity plays a crucial role in precision agriculture applications, remote monitoring, and automation of agricultural equipment. The lack of fixed telecommunications infrastructure in the region reflects the challenges faced by rural producers, who depend on wireless solutions to modernize their operations. In addition, the topography of the location influences signal propagation, making this environment ideal for evaluating the effectiveness of TVWS in open agricultural lands. * MS_CafezalManoela, located in Serra da Manoela, was selected to represent mountainous areas with agricultural production, a common scenario in several regions of Brazil. The choice of this location also reinforces the importance of TVWS in connecting isolated rural communities, which often depend on agriculture and livestock farming and have low population densities. This specific point was chosen because, despite the high relief, it still maintains LOS with the base station, allowing the evaluation of TVWS signal propagation in a high-altitude environment, but without direct obstructions. * Finally, MS_Pedralva, located in an olive grove on top of a mountain between the cities of Pedralva-MG and Maria da Fe-MG, was chosen to test the limits of TVWSsystem coverage, as it is located 38.4 km from the base station, in high and rugged terrain. This environment represents extreme scenarios of rural connectivity, where telecommunications infrastructure is practically non-existent and communication depends exclusively on long-range wireless solutions. The choice of this location also allowed us to assess the impact of signal attenuation due to the terrain, verifying the viability of TVWS in mountainous communities, a challenge present in several regions of Brazil.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 2 Related Works
### Point Cloud Attribute Compression  
[PERSON] et al. [16] first proposed a graph transform-based method to compress attributes, which were constructed of sub-graphs and associated Laplacian matrices to effectively code the colors of a given point cloud. However, it needed to repeatedly solve the eigen-decompositions of many large graph Laplacians, resulting in the approach infeasible for real-time processing. [PERSON] and [PERSON] [17] proposed a Region-Adaptive Hierarchical Transform (RAHT) with low complexity. They compressed the colors in a hierarchical sub-band transform that resembled an adaptive variation of a Haar wavelet, and the RAHT coefficients are encoded via arithmetic coding with assumed Laplace distribution. RAHT has been adopted into the MPEG G-PCC [18] as one of the core transformation parts for attribute compression. G-PCC further improved it with better entropy coding in version 6 (TMC13v6) and with the prediction of RAHT coefficients in the latest TMC13v14, which significantly improved performance and achieved the state-of-the-art PCAC efficiency.  
In addition to these traditional methods represented by G-PCC, a few pioneering works based on deep learning have emerged recently. [PERSON] et al. [7] proposed a PointNet [5] and PointNet++ [6] based autoencoder for attribute compression, and simultaneously proposed the improved module second-order point convolutional layer and dense point-inception block, which further extended the local receptive field and improved the ability of feature extraction. However, since PointNet series networks used feature extraction function max() at the cost of information loss, its final compression performance was unsatisfactory. [PERSON] et al. [10] proposed learning a neural network(NN)-based bidirectional mapping between a 3D point cloud and a 2D grid, and then used conventional image codec to encode the generated 2D grid. For every single point cloud, it overfitted the mapping function. However, since wrong mapping and repeated mapping can not be solved well, its final compression performance still suffers and also the model generalization is restricted. [PERSON] et al. [8] applied 3D dense convolutional autoencoder for the coding of both geometry and attribute components. In contrast, [PERSON] et al. [9] applied sparse convolution for feature extraction because of its superior efficiency for representing the geometry of unorganized points. They stacked sparse convolutional layers in the VAE structure and used conditional entropy model based on hyperprior and autoregressive prior, which has obviously reduced the gap between the learning-based method and the traditional G-PCC. Following this basic VAE framework, in this paper, we introduce the non-local attention mechanism to further narrow the gap.
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 3 Methods
### Fuzzy Temporal Windows for Data Pre-Processing  
As shown in Figure 5, fuzzy temporal windows (FTWs) are used to generate the input datasets from the raw sensor data for training temporal models. A fuzzy set that introduces each FTW \(T_{k}\) is specified by a membership function. The shape of the fuzzy set corresponds to a trapezoidal function \(T_{k}[l_{1},l_{2},l_{3},l_{4}]\) is shown in Equation (7). Four values that define the trapezoidal membership functions are a lower limit \(l_{1}\), an upper limit \(l_{4}\), a lower support limit \(l_{2}\), and an upper support limit \(l_{3}\). The four  
Figure 5: Architecture of the proposed joint learning of temporal model for human activity recognition.  
values of these \(l_{1},l_{2},l_{3},l_{4}\) are provided by the Fibonacci sequence, which has recently been successfully used for introducing FTWs. The Fibonacci sequence can easily be used for FTWs to build training datasets without involving a knowledge expert definition [17; 18; 46]. Figure 6 shows 12 multiple and incremental FTWs are designed based on the Fibonacci sequence. To generate a training input dataset, the FTWs are slided over raw sensors data \(x\) in every minute according to Equation (7): For example, in Ordonez smart homes A, the training input dataset generated by applying 15 FTWs on the raw sensor activations from all 12 binary sensors with the window size of \(1\) min. The datasets of Ordonez smart home A and B have 20,358 and 30,469 examples, respectively, where each example represents one minute of data with \(12\times 15=180\) features. Algorithm 1 shows the procedure of using FTWs in order to generate input training datasets.  
```
1:Input:\(\mathit{Sensor\_data}\) Sensor data from smart homes
2:\(\mathit{FTWs}\leftarrow\mathit{Fibonaccisequence}\) Fibonacci Sequence to define values of FTWs
3:\(\mathit{Intervals\_sensor}\leftarrow\mathit{Sensor\_data}\) Raw sensor data
4:for\(\mathit{ftw}\gets\mathit{FTWs}\)do
5:for\(interval\_sensor\gets Intervals\_sensor\)do
6: apply \(\mathit{ftw}\) on \(interval\_sensor\)
7:endfor
8:\(extracted\_features\gets maximum(\mathit{ftw})\)
9:endfor
10:\(\mathit{datasets}\leftarrow\mathit{extracted\_features}\)
11:Output:\(\mathit{datasets}\)
```  
**Algorithm 1**Fuzzy temporal windows to generate input Datasets
## 4 Results and Analysis
### Geogrid Strain  
The variation in the geogrid strain measured at the layers over time is illustrated in Figure 11. The maximum increase in the geogrid strain of 0.811% during and after construction was measured at the fifth layer. This value is equivalent to 16% of the geogrids' design tensile strain (5%). In addition, the maximum geogrid strain of 0.784% was measured at the fifth layer (2.2 m) during the construction of the reinforced backfill.  
Figure 10: Variation in horizontal earth pressure over height.  
Figure 11: _Cont._  
Except for the second layer, the maximum strain increases were measured for all layers during the construction of the reinforced backfill. The results demonstrate that the increase in the overburden load with the backfill height significantly influences the deformation of the geogrids. Considering the limited soil deformations in the second layer owing to the confining effects of the foundation, the geogrid deformation at the second layer was assumed to be minimal. Following the completion of the backfill construction process, the deformation of the geogrid resulting from the wall load increment, reinforced roadbed, and track construction is approximately negligible.
# HVdc Circuit Breakers: Prospects and Challenges
## 4 HVdc Circuit Breaker Topologies
### Voltage Rating  
Electro-mechanical dc breakers of 550 kV voltage ratings are available, while hybrid DCCBs are tested experimentally for 120 kV. However, SS dc breakers are not available for high voltage applications, only been designed and executed for medium voltage levels. But SSCB of 800 kV voltage rating can be realizable by looking at the development in semiconductor devices [106; 107; 108; 109; 110; 111; 113].  
Figure 7: Hybrid HVdc breaker [89].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Types of CB** & **Interruption Time** & **Power Losses** & **Voltage Rating** & **Current Rating** & **Cost [105]** & **Maintenance** & **Life Span** \\ \hline Electro–mechanical & –60 ms & 0.001\% & Max. 550 kV & Up to 4–8 kA & High & Required—High & Longest \\ Solid state & \(<\)1 ms & 30\% & 120–320 kV & 16 kA & Low & Required—Low & Long \\ Hybrid & 2–30 ms & 0.1\% & 800 kV exp. & 9–16 kA & Medium & Required—Medium & Longer \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of comparison of various HVdc breaker technologies.