esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Case Study
#### 3.2.1 Sichuan Earthquake  
There was a strong earthquake with a magnitude of 7.0 in Sichuan at 21:19:46 on 8 August 2017 (LT). The epicenter was at 103.82\({}^{\circ}\) E, 33.20\({}^{\circ}\) N. The depth was 20 km. At  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Province** & **Date** & **Magnitude** & **Latitude** & **Longitude** & **Depth (km)** \\ \hline Sichuan &
\begin{tabular}{c} 8 August \\ 2017 \\ 16 December \\ 2018 \\ 19 July 2019 \\ \end{tabular} & 7.0 & 33.20 & 103.82 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Seismic information.  
Figure 8: Probability gains in different channels at different distance thresholds.  
Figure 7: Infrared anomalies before earthquakes.  
07:27:52 on the next day (LT), an earthquake with a magnitude of 6.6 occurred in Xinjiang. The epicenter was at 82.89\({}^{\circ}\) E, 44.27\({}^{\circ}\) N. The depth was 11 km.  
The information on pre-seismic anomalies in different channels is shown in Table 3. In Table 3, the start time and the end time are the numbers of days that the start time and end time of the anomaly relative to the earthquake time. Negative numbers are before the earthquake and positive numbers are after the earthquake. The anomalies of the two long-wave channels appeared 25 days before the earthquake, as shown in Figure 9. On that day, there were discrete high-value points north of the epicenter in the images of the four channels, and the anomaly area of the long-wave infrared channels was larger, so it was identified earlier. As shown in Figure 10, there were high-amplitude and large-area anomalies in four channels on the 19 days before the earthquake. On that day, IR1, IR2 and IR4 obtained their maximum abnormal value. The anomalies were located between the epicenters of the two earthquakes and more close to that of the Sichuan earthquake. Figure 11 shows the largest coverage area of anomalies in the earthquake preparation region. During the abnormal period, the abnormal area of the four channels went through two times of increase and decrease. The abnormal area in the IR3 channel was less than 500 pixels 19 days before the earthquake, while that in the IR4 channel was also too small during the first time of increase and decrease. They are both ignored in Table 3. Anomalies in all channels lasted until 18 days before the earthquake.
# A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection
## 4 Discussion
For studies which had only few test subjects and a relatively small volume of data, the question of overfitting might not be a problem, but it is common practice to use a number of actions with respect to each algorithm. The complexity of thermal comfort perception by each human will always raise the question of validity for predictors in a data set. The findings of SLR show different sets of variables in combination with a number of algorithms and potential models. The Achilles heel of this topic is the tenuous link (if any) established between the non-invasive bio marker and that nebulous state of mind. The basic biological theory behind a collection of the skin images, heart rate, blood pressure and other parameters, is that the body is predefined by nature to restore conditions, which are comfortable for the functioning of the organism. Such a biological feature gives the assumption that there is a link between a certain bio-metrical trait and the actual feeling of thermal comfort. Unfortunately, there is a need to perform more targeted research within the field before it will be possible to validate certain bio-metrical parameters as the ones suitable for thermal comfort prediction. For better validation, it would be beneficial to utilize the link between the thermoregulation system and the subcortical level of the brain (or lower brain). Since the human body must to maintain core temperature within a normal range (e.g., 36.5-37.5 \({}^{\circ}\)C), the thermoregulation system needs continuous information flow from temperature-sensitive nerves. The signals travel from temperature-sensitive nerves through the spinal cord to subcortical level of the brain, where they are evaluated and the body's physiological features are adjusted respectively [66]. Based on such human body function, a new approach in non-invasive thermal comfort sensing is envisioned. For artificial intelligence algorithms, it is proposed to synthesize and use only those bio markers, which have direct interaction link to the lower brain that is directly responsible for the thermal comfort evaluation.
# A Wide-Range Four-Phase All-Digital DLL with De-Skew Circuit
###### Abstract  
A four-phase all-digital delay-locked loop (ADDLL) with a de-skew circuit for NAND Flash high-speed interfaces is proposed. The proposed de-skew circuit adopts a fall-edge-judgment phase adjuster and a three-stage digitally controlled delay line to align the system input clock and 0\({}^{\circ}\) output clock of the four-phase DLL over a wide frequency range, thus solving the four-phase offset caused by clock skew. A parallel-cascade configuration is proposed to solve the variable phase alignment problem caused by mode switching, thus effectively improving the phase-locked accuracy. The proposed circuit is fabricated in the 0.13 \(\upmu\)m CMOS process with a 0.072 mm\({}^{2}\) core area. The chip testing results show an operating frequency range from 26 MHz to 1.55 GHz and a typical alignment error of approximately 17 ps.  
delay-locked loop; NAND Flash high-speed interface; clock skew; wide range; high precision Article
## 5 Results
### PCA and HCA Results
#### 5.3.6 HCA-3 Results  
HCA-3 used only PC1 and PC2 from PCA-3 since the third PC accounts for less than 12% of the variance. This analysis resulted in the identification of four sample clusters (Figure 10). Cluster 1 (n = 59) represents samples with medium grain size classes, ranging from coarse-to-medium silts. Cluster 2 (n = 65) consists of samples with medium-to-fine silts. Cluster 3 (n = 4) comprises multi-modal samples, which are the coarsest in the sequence, that range from fine-to-medium sands. Cluster 4 (n = 30) includes samples with fine silts and clay.  
Based on these cluster assignments, the core CHA01 can be divided into four stratigraphic units (Figure 5). The lowest unit, U1 (900-869 cm), is primarily composed of samples from Clusters 1 and 2. U2 (868-488 cm) is further subdivided into three sub-units. U2a (868-744 cm) consists mainly of samples from Cluster 2. U2b (743-594 cm) shows variations between Cluster 2 and Cluster 4. U2c (593-488 cm) is the most homogeneous sub-unit, exclusively containing samples from Cluster 4. U3 (487-189 cm) exhibits a subdivision into three sub-units. U3a (487-394 cm) is highly homogeneous and comprises exclusively Cluster 1 samples. U3b (393-264 cm) alternates between Cluster 1 and Cluster 2 samples. U3c (263-189 cm) is characterized by great homogeneity and exclusively features samples from Cluster 2. Finally, U4 (189-0 cm) is primarily composed of Cluster 1 samples, with a small contribution from Cluster 2 samples.
# An Accurate Probabilistic Model for TVWS Identification
## 2 Background
### TVWS Basics  
As mentioned before, the unused and underutilized frequency spectrum spaces are referred to as WS. Efficient utilization of these WS has the potential to reduce the spectrum scarcity faced nowadays with the fast growth of wireless technologies. According to [18], the characteristics of the WS are: (_i_) A white space is an unused radio frequency; (_ii_) Its existence depends on time, frequency, and geographical location; (_iii_) Its utilization should not cause any harm to the PU. These concepts are known as TVWS when applied to the TV broadcasting frequencies.  
In this research, we are interested in the frequency bands assigned to the TV service. Table 1 illustrates the frequency ranges, channel numbers, bandwidth for each band, and the respective ITU regions [19] were the bands are allocated. Our work focuses on bands IV and V, i.e., 500 MHz to 698 MHz [20; 21]. It should be noted that channel 37 (608 MHz to 614 MHz) is used for radio astronomy and it is excluded from TV broadcast services. The three main reasons to choose said spectrum bands are explained next. First, the propagation characteristics of frequencies under 1 GHz are favorable for robust transmission, and there are standards as IEEE 802.22 considering the TV band to access as a SU. Being recommended by an organization like IEEE, regulatory bodies across the globe have started applying them in their territories [5]. Second, the selected range of frequencies offers 198 MHz (32 channels) of continuous spectrum, except for channel 37. This spectral continuity gives more possibilities to find and use idle channels without major modifications in the transmission parameters of the antennas. Third, multiple studies have demonstrated that there is a significant number of underused TV spectrum bands even in urban areas, and their availability increases rapidly in rural areas [18].  
Figure 2: In the Cognitive Radio (CR) cycle, the UHF-TV spectrum band is sensed, then subsequently analyzed, and a decision is taken based on the presence or absence of Primary Users (PUs).
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 4 Satellite Clock Bias Prediction
### Satellite Clock Bias Prediction
The RMS of the TKT algorithm will also increase, and the growth trend is conspicuous with a large range. This is consistent with the result shown in Figure 8. It is implicit that the prediction accuracy of the TKT algorithm has strong relevance with time, while that of the NKT algorithm and RKT algorithm remains rather stable. If the prediction period is one day or longer, the prediction residual of the TKT algorithm will gradually be larger than that of the other two algorithms, and the prediction accuracy will become worse. Thus, the timescale generated by the TKT algorithm is not recommended in the use of prediction longer than one day. TKT can effectively suppress timescale low-frequency noise, and this advantage can benefit users. According to our comparison of the prediction results, for the prediction of a single frequency reference, the TKT prediction results are higher than the NKT and RKT. It indicates that the TKT algorithm reduces the influence of low-frequency noise of other frequency standards on the modeling of this frequency standard. Therefore, when users use the satellite clock difference model based on this scale to correct their ranging, they only receive low-frequency errors of a single satellite clock and eliminate the pollution of low-frequency errors of other satellites.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 3 Health Monitoring in Corrosion
### Distributed OFS for Physical Sensing  
Brillouin scattering is a kind of an inelastic scattering that happens due to acoustic waves generated by the lattice vibrations. This kind of scattering is also extremely sensitive to the temperature and deformation value of the optical fiber. Another kind of inelastic scattering is the Raman scattering, which happens due to the exchange of energy with the molecular vibrations of the fiber. Technologies such as distributed temperature (DTS), distributed strain (DSS), etc. have grown massively in previous decades [100], and thus, their adaptation for monitoring the corrosion and structural health has been carried out in the oil and gas industry. Apart from monitoring the temperature, strain, etc. during operations such as well logging [101], the utilization of distributed OFS has been done in  
Figure 8: Optical spectral responses of (**a**) single and (**b**) multiple fiber gratings [71].  
order to monitor various physical parameters that are associated with corrosion, failure, and leak detection.  
The thermal properties of the fluids flowing inside a pipeline are the ones to detect any kind of leakage in the pipeline, and DTS is largely used for this purpose. One of the methods to lower the viscosity of the flowing fluids, such as crude oil, is heating transportation for a more efficient flow of the crude in the pipes. Any leakage of these hot oils would lead to a considerable amount of temperature change on the outer surface of the pipelines, which is easily detectable with the help of DTS [102]. It can be explained from the Joule-Thomson effect that when a gas exhibiting higher values of pressure leaks, there is a reduction in the resultant temperature. The leakage of liquids leads to an increment in the temperature due to which the DTS detects a leakage in the pipelines [101].  
The DAS that makes use of coherent Rayleigh backscattering was studied to see if they are capable of detecting vibrations in a pipeline that are induced due to leakage. The investigation was carried out with the help of optical fibers, which are wound over the pipes in a helical fashion (Figure 10) [103; 104].
## 2 Applications of Biosensors in Bacteria Detection
### Recognition of Bacteria Using Biosensors
#### 2.1.3 Electrochemical Biosensors
This can be alleviated by the immobilization of biorecognition ligands on the sensor membrane. For example, a biocompatible interface was developed by [PERSON] and co-workers for the detection of _Salmonella typhimurium_[134]. [PERSON] et al. developed a sensitive and low-cost disposable paper-based potentiometric immunosensor to detect food-borne pathogens (e.g., _S. typhimurium_) [134]. Amperometric biosensors are based on direct measurement of the current generated by the reduction or oxidation of species created by the analyte-bioreceptor interaction [23]. The bioreceptor is usually an enzyme, for instance glucose oxidase [23], but antibodies are also used [135]. The generated current is directly proportional to the analyte concentration [23]. The advantages of these biosensors are their relative simplicity, excellent sensitivity, and miniaturizability [23]. The disadvantage is the low specificity, depending on the used potential [23]. Although in the field of biosensing, amperometry is the most common detection method, in case of whole-cell bacterial detection, this technique is not widely applied [23]. [PERSON] and [PERSON], an active group in this field, reported an electrochemical biosensor which was composed of an array of gold nanoparticle-modified screen-printed carbon electrodes on which magnetic nanoparticles coupled to specific peptides were immobilized via streptavidin-biotin interaction. Taking advantage of the proteolytic activities of the protease enzymes produced from two bacteria on the specific peptides, the detection was achieved in 1 min [132]. The achieved limit of detection was 9 CFU/mL for _L. monocytogenes_ and 3 CFU/mL for _Staphylococcus aureus_. That paper presents a useful tabulation of electroanalytical methods for the determination of Listeria monocytogenes and _S. aureus_, showing their main characteristics, among others' detection limits, which fall in the range of 1-100 CFU/mL. As it is well known, the sensitivity of amperometry can be enhanced by the application of mediator molecules, which amplify the response current generated at the electrode. An amperometric biosensor has been developed for the _Streptococcus agalaciae_, where signal amplification was achieved by streptavidin conjugated horseradish peroxidase (HRP) [136]. This made possible the detection of _S. agalaciae_ in the range of 10 to \(10^{7}\) CFU/mL. Due to stability issues, the general use of HRP in amperomteric sensors is limited. The whole bacteria detecting biosensors, their obtainable LOD values and assay time are summarized in Table 1.
## 3 Theoretical Performance Analysis on Sidelink JCS System
Additionally, \(\eta_{t,ki}\) is a coefficient that takes values of \(0\) or \(1\), indicating the absence or presence of interference in that time slot, respectively. Therefore, considering Gaussian white noise and interference from other vehicles, the signal-to-interference-plus-noise ratio (SINR) of the reflected echo signal from a target located at a distance \(d\) from the transmitter is calculated as follows:  
\[\mathsf{SINR}_{t,i}=\frac{P_{R}}{P_{\text{n}}+I_{t,i}} \tag{7}\]  
According to [43; 44], the radar sensing performance of NR-V2X signals with OFDM waveforms is represented by the Cramer-Rao Lower Bound (CRLB) on distance estimation variances. The CRLB represents the best performance that can be achieved for unbiased estimation of these parameters. The CRLB for distance estimation for the transmitting/sensing vehicle \(i\) at time slot \(t\) is given by:  
\[\mathsf{CRLB}_{t,i}^{(\widehat{d})}=\frac{3c^{2}}{\mathsf{SINR}_{t,i}8\pi^{2} \Delta f^{2}MN(N^{2}-1)} \tag{8}\]  
The CRLB clearly describes the most optimistic performance achievable and serves as a benchmark for characterizing sensing accuracy. Practically, the signal processing methods cannot achieve performance below the theoretical CRLB. Further, based on the preceding analysis, the impact of sidelink bandwidth resources on sensing performance can be analyzed. According to (8), the CRLB for distance estimation decreases rapidly with the cube of the bandwidth. Therefore, increasing the subcarrier spacing (\(\Delta f\)) or the number of subcarriers (\(N\)) is advantageous for distance estimation. Simultaneously, according to [PERSON]'s theorem, increasing bandwidth benefits the data rate in vehicular networks. However, in a multi-user distributed resource allocation system with limited resources, indiscriminately increasing \(N\) to enhance sensing and communication performance may exacerbate consecutive packet collisions within the existing sidelink resource allocation strategy. This interference significantly impacts the performance of the JCS system and reduces resource utilization efficiency [28]. The subsequent sections will provide a detailed analysis of optimizing the allocation scheme to mitigate collisions.
## 4 Results and Discussion
### Groundwater Protection Strategy in Coastal Area
#### 4.3.2 Groundwater Protect Measures in Study Area  
Based on the results of vulnerability assessment of groundwater in Tangshan City and the above protection objectives, we have clarified that the protection performance of shallow groundwater is weak, and the current status of shallow groundwater pollution is serious, so we propose the following protection measures for the current situation characteristics.  
1. Reasonable land use and control of new highly polluting enterprises to reduce groundwater pollution: Urban planning, especially industrial zoning, should consider vulnerable areas and restrict the establishment of industrial zones in areas vulnerable to groundwater pollution, such as the center of Tangshan City, Kaiping District, Guye District, and Luaman County. In addition, wastewater discharge from industrial zones must be strictly managed to ensure that standards are met and groundwater pollution is controlled [51]. By controlling the sources of pollution, restricting the discharge of hazardous substances, and rationally managing groundwater extraction, it is possible to ensure that the quality of groundwater meets safety standards.
2. Balanced control of groundwater exploitation: Over-exploitation of groundwater will lead to increased water hardness, groundwater pollution, and even ground subsidence. At this stage, deep wells with serious over-exploitation should be closed, the amount of groundwater extraction should be limited, and the number of new wells should be controlled.
3. Regulate sewage irrigation and fertilizer use: It is important to control the amount of fertilizers used in agricultural areas and promote their effective use in order to minimize the amount that enters groundwater. Additionally, effluent used for irrigation should be analyzed and tested to ensure that it does not adversely affect soil and groundwater quality [52].
4. The establishment of a comprehensive groundwater monitoring network and early warning system throughout the region is essential. Regular water quality monitoring should be carried out to detect early warning signs in time for timely interventionsto mitigate serious consequences and to report data to ecological and environmental authorities.
# Spatial Variation of Water Chemistry in Aries River Catchment, Western Romania
## 2 Methodology
### Pollution Assessment  
The Heavy Metal Pollution Index (HPI) is a rating model that provides the aggregate influence of individual heavy metals on the overall quality of different water types [31; 32]. Seven heavy metals (Cd, Cu, Fe, Mn, Ni, Pb and Zn) were used for the calculation of HPI. The HPI was computed in two steps, according to Equations (1) and (2) [33]:  
\[HPI=\frac{\sum_{i=1}^{n}(Q_{i}W_{i})}{\sum_{i=1}^{n}W_{i}} \tag{1}\]  
\[Q_{i}=\frac{M_{i}}{S_{i}}\times 100 \tag{2}\]  
where, \(Q_{i}\) is the sub-index of the \(i\)th parameter, \(W_{i}\) is the unit weightage of the \(i\)th parameter and \(n\) is the number of the considered chemical parameters. \(M_{i}\) and \(S_{i}\) are the concentration of the monitored ith parameter and the standard maximum allowable values (mg/L), according to the Romanian Regulation and to the European Directive concerning water quality [34; 35].  
HPI scoRes. below the threshold (HPI! 100) indicate low pollution with heavy metals, while scoRes. higher than 100 (HPI! 100) suggest that the water is unsuitable for consumption and, when consumed, it causes harmful health consequences. The case of an HPI! 100 indicates pollution with heavy metals [36].  
The Heavy Metal Evaluation Index (HEI) is another tool for assessing water quality, offering an insight into the overall quality of water sources with respect to the heavy metals content, which is consistent with the HPI method. The HEI iscalculated based on Equation (3), according to [PERSON] et al. [37], [PERSON] and [PERSON] [38]. The surface water can be classified based on the HEI scores, as follows: surface waters with a low degree of pollution (HEI! 10), waters with a medium degree of pollution (10! HEI! 20) and waters with a high pollution level (HEI? 20) [39].  
\[HEI=\sum_{i=1}^{n}\frac{M_{i}}{S_{i}} \tag{3}\]  
where \(M_{i}\) is the determined concentration of the \(i\)th parameter and \(S_{i}\) represents the maximum allowable concentration (MAC) of the parameters. In the present study, \(S_{i}\) values were considered according to the Romanian and International Regulations, namely the Minister Order 161/2006/OD and Directive 2008/32/CE, regarding the quality of surface water resources [34; 35].